Comprehensive factor quantitative evaluation method for tight sandstone gas favorable area division

Abstract

The invention relates to a comprehensive factor quantitative evaluation method for tight sandstone gas favorable area division, and belongs to the field of petroleum geological exploration. In order to solve the problems of fragmentization, singleness, insufficient regional adaptability and the like in the prior art, the method establishes a quantitative relation between a single parameter and the gas reservoir yield through multi-source data intersection analysis, determines an influence mechanism of each parameter on the gas reservoir yield, and determines a single parameter evaluation threshold. And constructing a dynamic weight distribution matrix by adopting an analytic hierarchy process, and carrying out weight assignment on the main control parameter, the secondary control parameter and the reference parameter to realize dynamic quantification of the single-parameter evaluation factor. Based on a single-parameter evaluation factor, a Monte Carlo simulation thought is adopted, a multi-parameter comprehensive factor is calculated through multiple algorithms such as weighted summation and weighted root-mean-square, and the overall evaluation level of the lithologic body is visually reflected. And according to normal distribution characteristics of the comprehensive factor histogram, a favorable area division standard is formulated, and through comparison with a well drilling result, the division standard is iteratively optimized, so that the prediction precision is improved.

Description

Technical Field

[0001] The invention belongs to the field of petroleum geological exploration and is a quantitative evaluation method for dividing favorable tight sandstone gas zones. Background Art

[0002] Existing delineation of favorable tight sandstone gas zones has certain limitations. For example, the Permian tight sandstone gas reservoirs in the Ordos Basin have complex and concealed accumulation mechanisms. Existing research often focuses on single factors, such as source rock thickness and reservoir properties, or simple combination analyses, lacking a systematic quantitative framework. Furthermore, existing technologies rely on subjective judgment, resulting in unstable evaluation results and difficulty in identifying the primary controlling factors of high-yield gas reservoirs.

[0003] Chinese invention patent application publication number CN119065002A predicts the gas content of tight sandstone reservoirs through seismic attribute reconstruction and gas content index calculation, but does not involve comprehensive quantitative evaluation of multiple factors. Chinese invention patent application publication number CN117468911B targets high-water-saturation, multi-layer commingled gas reservoirs through dynamic reserve calculation and sand body control range inversion, but is limited to single-well development potential evaluation and does not cover regional favorable zone delineation. Invention patent application publication number CN117670131A establishes a development effect evaluation system based on a game theory combined weighting method, but focuses on engineering factors and economic evaluation without in-depth analysis of the main controlling factors of reservoir formation. Invention patent application publication number CN119334842A improves the prediction accuracy of porosity and permeability parameters through mechanical property classification and confining pressure testing, but does not involve comprehensive evaluation of resource potential. The Chinese invention patent application with publication number CN107808068A combines the heterogeneity of river sand bodies with the productivity impact value to predict high-yield wells, but relies on a single geological parameter such as river channel width and does not achieve multi-factor system integration.

[0004] In addition, patents related to tight sandstone gas tend to be more related to reservoirs, such as China's invention patent CN109102180B. Summary of the Invention

[0005] The embodiments of the present invention provide a comprehensive factor quantitative evaluation method for dividing favorable tight sandstone gas zones, so as to at least solve some of the above technical problems existing in the prior art.

[0006] A comprehensive factor quantitative evaluation method for the division of favorable tight sandstone gas zones includes:

[0007] S1. Determine the single parameter evaluation factor based on basic data;

[0008] S2. establishing a relationship between the single parameter and gas reservoir production based on multi-source data intersection, and determining a single parameter evaluation threshold;

[0009] S3. Divide the single parameter into multiple levels based on the single parameter evaluation threshold, and assign different weights to the single parameters of different levels;

[0010] S4. Assigning interval values ​​to the single parameter evaluation factor according to the weight, wherein the difference between the maximum value and the minimum value of the assignment interval is equal to the weight of the corresponding parameter;

[0011] S5. Using a single parameter as the independent variable and a single parameter evaluation factor as the dependent variable, a "parameter-factor" assignment function is constructed using linear or nonlinear fitting to obtain the evaluation factor corresponding to each single parameter;

[0012] S6. Calculating evaluation factors of all single parameters of the predicted lithologic body of the target layer according to the assignment function;

[0013] S7. Based on the single parameter evaluation factors of lithologic bodies, multiple algorithms are used to fuse and calculate the single parameter evaluation factors of all lithologic bodies to obtain the multi-parameter comprehensive factors of different algorithms;

[0014] S8. Compile histograms for each of the multiple groups of comprehensive factors, analyze the numerical distribution characteristics of each group of comprehensive factors, and determine reference standards for using each group of comprehensive factors to divide favorable areas;

[0015] S9. Classify the favorable types of all lithologic bodies according to the reference standard, and divide the lithologic bodies into different types of favorable areas or potential areas. Compare the classification results of the lithologic bodies encountered in each group of wells with the actual drilling results, and iterate repeatedly to select a comprehensive factor favorable area type classification reference standard suitable for the study area;

[0016] S1 0. Based on the reference standard for the division of favorable areas by comprehensive factors and the distribution of favorable types of all lithologic bodies classified by it, the scope of favorable areas of different types is delineated on the plane.

[0017] In an optional embodiment, in step S1, the basic data include data and geological analysis map data of the study area, the data include drilling data, well logging data, oil and gas test data, production dynamic data and seismic data, and the geological analysis map data include a main source rock thickness map, a gas generation intensity map, a fracture distribution map, a micro-structure map, a predicted lithologic body distribution map, a predicted sand body thickness map, a predicted reservoir porosity distribution map or a sweet spot porosity distribution map, a mudstone interlayer thickness map, and a mudstone caprock thickness map;

[0018] According to the accumulation conditions and exploration experience of tight sandstone gas, a single parameter participating in the division of favorable gas reservoir areas is determined as an evaluation index based on the basic data. The evaluation index is the composition of the single parameter evaluation factor. The single parameter includes the thickness of the main source rock, gas generation intensity, fracture index, micro-structural type, lithologic body area, reservoir sweet spot porosity, mudstone interlayer thickness, and regional caprock thickness. The fracture index includes at least one of fracture fracture density, number, and type.

[0019] In an optional embodiment, step S2 includes:

[0020] S21. For data-type parameters, establish a quantitative intersection relationship between the single parameter determined in S1 and gas reservoir production through intersection statistics method, and analyze the influence of the single parameter on gas reservoir production;

[0021] For text parameters, the distribution of gas reservoir production under different micro-structure types is statistically analyzed to analyze their relationship with gas reservoir production;

[0022] S22. On the single parameter plane diagram, combined with the analysis conclusions of S21, analyze the scope of each parameter on the plane and its influence on the gas reservoir production. By comparing the parameter values ​​and gas reservoir production in different areas, clarify the importance of the single parameter on the gas reservoir production.

[0023] S23. Based on the analysis results of S21 and S22, combined with the importance of the impact of single parameters on gas reservoir production, as well as actual geological conditions and exploration and development experience, analyze and determine the reference threshold of each single parameter when evaluating favorable gas reservoir areas. The threshold serves as the main basis for subsequent single factor assignment interval analysis.

[0024] In an optional embodiment, the analysis of the relationship between a single parameter and gas reservoir production includes:

[0025] S211 table creation and data organization:

[0026] Create tables and calculate key geological and engineering parameters of the target layer for each well, including: natural gas test production, thickness of main source rocks, gas generation intensity, type of microstructure, fault fracture density, reservoir porosity, lithologic trap area, mudstone barrier thickness, and regional caprock thickness;

[0027] S21 2. Analyze data parameters and create intersection diagrams;

[0028] S21 3. For the different micro-structure types encountered by the well, the gas reservoir production corresponding to each structure type is counted and its relationship with the gas reservoir production is analyzed.

[0029] In an optional embodiment, the data class parameter analysis and cross-plot creation include:

[0030] S21 21Establish a cross-plot of the thickness of the main source rocks and natural gas production, and analyze the impact of source rock thickness on natural gas production;

[0031] S21 22 Establish a cross-plot of gas generation intensity and natural gas production to analyze the relationship between gas generation intensity and production;

[0032] S21 23 Establish a cross-plot of reservoir porosity and natural gas production to study the contribution of porosity to production;

[0033] S21 24 Establish a cross-plot of fault fracture density and natural gas production to analyze the impact of fault fracture development on natural gas migration and accumulation;

[0034] S21 25 Establish a cross-plot of mudstone barrier thickness and natural gas production to analyze the impact of mudstone barrier thickness below the target layer on the vertical migration of natural gas and its impact on the production of the target layer gas reservoir;

[0035] S21 26 Establish a cross-plot of regional caprock thickness and natural gas production to evaluate the impact of caprock thickness on gas reservoir preservation and production;

[0036] S21 27 established a cross-plot of the lithologic body area encountered by the well and the gas reservoir production, and analyzed the requirements for the lithologic body area to form a large-scale gas reservoir.

[0037] In an optional embodiment, the single parameter planar graph analysis includes:

[0038] S221, Planar analysis of thickness of main source rocks:

[0039] Based on the quantitative relationship between the thickness of the main source rocks and the gas reservoir production, the thickness distribution characteristics of the main source rocks are analyzed on the main source rock thickness plane map, and the plane distribution range of the favorable area for natural gas accumulation is delineated;

[0040] S222, Anger Intensity Plane Analysis:

[0041] Based on the quantitative relationship between the gas generation intensity of the main source rocks and the gas reservoir production, the gas generation intensity of the main source rocks is analyzed in depth on the gas generation intensity plane map, and the plane distribution range of the favorable area for natural gas accumulation is determined;

[0042] S223, Fault crack density plane diagram analysis:

[0043] Based on the quantitative relationship between fault fracture density and gas reservoir production, the development and distribution characteristics of fault fractures were studied in depth on the fault fracture plane distribution map, and the distribution range of fault fractures that are conducive to natural gas migration was analyzed.

[0044] S224, micro-structure type plan view analysis:

[0045] Based on the conclusions drawn from the statistical analysis of the relationship between microstructure types and gas reservoir production, the development characteristics of microstructures were analyzed in depth on the microstructure plane map, and the distribution range of microstructure development areas that are conducive to natural gas accumulation was determined.

[0046] S225, Reservoir porosity planar diagram analysis:

[0047] Based on the quantitative relationship between tight sandstone reservoir porosity and gas reservoir production, a detailed analysis of reservoir porosity distribution was conducted on the reservoir porosity plane map, and the plane distribution range of the porosity favorable area for natural gas accumulation was delineated.

[0048] S226. Lithologic trap area plan analysis:

[0049] Based on the relationship between the area of ​​lithologic bodies encountered by wells and gas reservoir production, the predicted lithologic body distribution map is combined with actual drilling data to depict the distribution of lithologic bodies that meet a certain area scale and are conducive to natural gas accumulation, and the lithologic body area is calculated;

[0050] S227, mudstone interlayer thickness planar diagram analysis:

[0051] Based on the quantitative relationship between mudstone interlayer thickness and gas reservoir production, the distribution of mudstone interlayer thickness is analyzed on the mudstone interlayer thickness plane map, and the plane distribution range of the favorable area for natural gas migration is determined;

[0052] S228, Regional cover thickness plan analysis:

[0053] Based on the quantitative relationship conclusion determined by the intersection analysis of regional cap rock thickness and gas reservoir production, the thickness distribution of the regional cap rock was carefully studied on the regional cap rock thickness plane map, and the plane distribution range of the favorable area for natural gas accumulation was delineated.

[0054] In an optional embodiment, S222 analyzing the planar graph of the gas intensity includes:

[0055] S2221, mapping step: Based on the collected data on the gas generation intensity of the main source rocks, use the Shuanghu geological mapping software to compile a gas generation intensity contour map;

[0056] S2222. Judgment step: On the gas generation intensity plane map, analyze the changing trends and patterns of gas generation intensity. Based on the quantitative relationship between gas generation intensity and gas reservoir production and the gas test results of the well drilling encountering the gas reservoir, analyze and delineate the gas generation intensity region that is conducive to high gas reservoir production.

[0057] S223 fault fracture density planar analysis, including:

[0058] S2231, Fault and Fracture Prediction Step: Use structural interpretation software to interpret the post-stack seismic volume in detail to depict the fault distribution, combine it with the fracture prediction software to extract ant body attributes from the post-stack seismic volume, and superimpose the two to obtain a fault and fracture plane distribution map;

[0059] S2232, judgment step: On the fault and fracture distribution map, analyze the development and distribution characteristics of faults and fractures. Based on the quantitative relationship between fault fractures and gas reservoir production, combined with the gas reservoir test results of the well drilling, calculate the density of fault fractures developed per square kilometer, and analyze the fault and fracture areas that are conducive to high gas reservoir production.

[0060] S224 micro-structure type plan view analysis, including:

[0061] S2241, mapping steps: Use structural interpretation software to conduct detailed tracking and interpretation of the main layer on the post-stack seismic volume, and obtain depth-domain structural data through time-depth conversion. Then, use Shuanghu geological mapping software to compile a micro-structural map of the top surface of the main layer;

[0062] S2242, judgment step: On the microstructure map, analyze and describe the development and distribution characteristics of favorable microstructure types. Based on the statistical relationship between microstructure types and gas reservoir production, combined with the gas reservoir test results of the well drilling, analyze and determine the area and range of microstructure types that are conducive to high gas reservoir production.

[0063] S225 reservoir porosity plan analysis, including:

[0064] S2251, inversion steps: invert and predict the porosity of the main layer on the post-stack seismic volume using reservoir inversion software, and compile a porosity contour map of the main layer reservoir using Shuanghu geological mapping software.

[0065] S2252, judgment steps: On the predicted porosity plane map, analyze the variation pattern of reservoir porosity, and based on the statistical relationship between reservoir porosity and gas reservoir production, combined with the gas test results of the well drilling encountering the gas reservoir, analyze and delineate the sweet spot porosity area that is conducive to high gas reservoir production.

[0066] S226 lithologic trap area plan analysis, including:

[0067] S2261, inversion step: invert and predict the thickness of the main layer sand body on the post-stack seismic volume using reservoir inversion software, and compile the main layer reservoir thickness contour map using Shuanghu geological mapping software;

[0068] S2262, judgment step: On the predicted reservoir thickness plan, analyze the reservoir thickness variation trend and regularity. Based on the lithologic gas reservoir analysis approach, depict the distribution range of the lithologic bodies on the plane. Combined with the porosity range conducive to high gas reservoir production identified in S352, and based on the gas test results of the well drilling encounter, ultimately determine and identify a lithologic body area of ​​a certain size that is conducive to high gas reservoir production.

[0069] S227 mudstone interlayer thickness planar analysis, including:

[0070] S2271, mapping steps: Based on the thickness of the mudstone interlayer encountered at the well point, use Shuanghu geological mapping software to compile a mudstone interlayer thickness contour map;

[0071] S2272, judgment step: On the mudstone interlayer thickness plane diagram, analyze the changing trend and regularity of the mudstone interlayer thickness. Based on the quantitative relationship between mudstone interlayer thickness and reservoir production, combined with the gas reservoir test results encountered by the well drilling, analyze and determine the area of ​​mudstone interlayer thickness that is conducive to high gas reservoir production;

[0072] Analysis of the cover thickness plan in the S228 area, including:

[0073] S2281, mapping step: Based on the caprock thickness in the area encountered by the well point, use Shuanghu geological mapping software to compile a regional caprock thickness contour map;

[0074] S2282, judgment steps: On the regional cap rock thickness plan, analyze the trend and law of regional cap rock thickness changes, and according to the quantitative relationship between regional cap rock thickness and reservoir production, combined with the gas test results of the well drilling encountering the gas reservoir, analyze and determine the regional range of cap rock thickness that is conducive to high-yield gas reservoirs.

[0075] In an optional embodiment, S23 single parameter evaluation threshold setting includes:

[0076] S231, thickness threshold of main source rock:

[0077] By deeply analyzing the quantitative relationship between the thickness of the main source rock and gas reservoir production, and combining the specific scope of this relationship on the plane diagram of the main source rock thickness, the lower limit of the main source rock thickness that is conducive to the formation of tight sandstone gas accumulation is reasonably determined;

[0078] S232, anger intensity threshold:

[0079] Based on the quantitative relationship between gas generation intensity and gas reservoir production, and the range of expression of this relationship on the gas generation intensity plane diagram, the lower limit of gas generation intensity that is conducive to the formation of tight sandstone gas accumulation can be reasonably determined;

[0080] S233, fault fracture threshold:

[0081] Based on the quantitative relationship between fault fracture density and gas reservoir production, as well as the development and distribution characteristics of fault fractures that are conducive to natural gas migration on the plane, the evaluation threshold of fault fractures is determined by statistically analyzing the development density of fault fractures per square kilometer;

[0082] S234, microstructure type threshold:

[0083] By combining the relationship between microstructural types and gas reservoir production, and the development of microstructural types favorable for tight sandstone gas accumulation on microstructural maps, we can identify microstructural types favorable for gas reservoir formation, such as fault noses, anticlines, nose uplifts, and slopes. This standard will help identify favorable structural locations.

[0084] S235, lithologic trap area threshold:

[0085] By analyzing the relationship between the area of ​​lithologic traps encountered by wells and gas reservoir production, as well as their impact on reservoir size, and based on the area and distribution of lithologic bodies depicted through a combined map of predicted sand body thickness and predicted reservoir porosity, we can rationally determine the lower limit of lithologic trap area conducive to large-scale gas accumulation. This criterion will be used to assess the size and potential of lithologic traps.

[0086] S236, reservoir sweet spot porosity threshold:

[0087] Based on the quantitative relationship between reservoir sweet spot porosity and gas reservoir production, and the range of this relationship in predicting the reservoir porosity plane, the lower limit of reservoir sweet spot porosity that is conducive to the formation of tight sandstone gas accumulation can be reasonably determined;

[0088] S237, mudstone barrier thickness threshold:

[0089] By analyzing the quantitative relationship between mudstone interlayer thickness and gas reservoir production, as well as the range of this relationship on the mudstone interlayer thickness plane diagram, the lower limit of mudstone interlayer thickness that is conducive to tight sandstone gas accumulation can be reasonably determined.

[0090] S238, regional cover thickness threshold:

[0091] Based on the quantitative relationship between regional cap rock thickness and gas reservoir production, as well as the range of this relationship on the regional cap rock thickness plane diagram, the lower limit of regional cap rock thickness that is conducive to tight sandstone gas accumulation can be reasonably determined.

[0092] In an optional embodiment, the S3 single parameter hierarchical division and weight assignment includes:

[0093] S31, single parameter level division: According to the importance of the single parameter on gas reservoir production, the single parameter is divided into three levels: primary control parameter, secondary control parameter and reference parameter according to the hierarchical analysis method;

[0094] S32, single parameter level weight assignment: take the difference between the maximum and minimum values ​​of the single parameter evaluation factor assignment interval as the single parameter weight, where the main control parameter weight ≥ 0.7; 0.3 < secondary control parameter weight < 0.7; reference parameter weight ≤ 0.3.

[0095] In an optional embodiment, S4 performs interval assignment on the single parameter evaluation factor, including:

[0096] S41, evaluation factor of thickness parameter of main source rock:

[0097] Based on the significant degree of influence of the thickness of the main source rock on gas reservoir production, its relative importance in the division of favorable gas reservoir areas is comprehensively evaluated. Within the range of 0-1, a reasonable interval value is assigned to this parameter. The size and span of the assigned value reflect its importance and serve as an evaluation factor for the parameter in subsequent favorable gas reservoir area evaluation.

[0098] S42, anger intensity parameter evaluation factor:

[0099] Based on the significance of the impact of gas generation intensity on gas reservoir production, its relative importance in the division of favorable gas reservoir areas is comprehensively evaluated. A reasonable interval value is assigned to this parameter within the range of 0-1. The size and span of the assigned value reflect its importance and serve as an evaluation factor for the gas generation intensity parameter.

[0100] S43, fault fracture parameter evaluation factor:

[0101] Based on the role of fault fracture density in the natural gas transport and accumulation process, its impact on the favorable zone division of gas reservoirs is evaluated. A reasonable interval value is assigned to this parameter within the range of 0-1. The size and span of the assigned value reflect its importance and serve as an evaluation factor for the fault fracture parameter.

[0102] S44, microstructure type parameter evaluation factor:

[0103] Considering the potential impact of micro-structural types on gas reservoir production, its contribution to the division of favorable gas reservoir areas is determined. A reasonable interval value is assigned to this parameter within the range of 0-1. The size and span of the assigned value reflect the difference in the impact of different micro-structural types on gas reservoirs and serve as an evaluation factor for the micro-structural type parameter.

[0104] S45, lithologic trap area parameter evaluation factor:

[0105] Based on the control effect of lithologic body area on gas reservoir scale, its importance in the demarcation of favorable gas reservoir areas is evaluated. A reasonable interval value is assigned to this parameter within the range of 0-1. The size and span of the assigned value reflect the degree of influence of lithologic trap area on gas reservoir scale and serve as an evaluation factor for the lithologic trap area parameter.

[0106] S46, reservoir sweet spot porosity parameter evaluation factor:

[0107] Based on the direct impact of sweet spot porosity on gas reservoir production, its criticality in the delineation of favorable gas reservoir zones is determined. Reasonable interval values ​​are assigned to this parameter within the range of 0-1. The size and span of the assigned values ​​reflect the contribution of sweet spot porosity to the delineation of favorable gas reservoir zones and serve as an evaluation factor for the sweet spot porosity parameter.

[0108] S47, mudstone interlayer thickness parameter evaluation factor:

[0109] According to the influence of mudstone interlayer thickness on gas reservoir production, its role in the division of gas reservoir favorable areas is evaluated. In the range of 0-1, appropriate interval values ​​are assigned to this parameter. The size and speed of the assigned values ​​reflect the influence of mudstone interlayer thickness on gas reservoir production and serve as the evaluation factor of mudstone interlayer thickness parameter.

[0110] S48, regional cover thickness parameter evaluation factor:

[0111] Based on the impact of regional cap rock thickness on gas reservoir production, its importance in the division of favorable gas reservoir areas is judged. Appropriate interval values ​​are assigned to this parameter within the range of 0-1. The size and span of the assigned values ​​reflect the contribution of regional cap rock thickness to gas reservoir protection and production, and serve as an evaluation factor for the regional cap rock thickness parameter.

[0112] In an optional embodiment, S5 is a single parameter evaluation factor assignment function, including:

[0113] S51, main source rock thickness evaluation factor assignment function:

[0114] The thickness of the main source rock is used as the independent variable, and the main source rock thickness evaluation factor is used as the dependent variable. The domain is defined as the value of the main source rock thickness evaluation threshold to the maximum value of the main source rock thickness plane, and the range is the main source rock thickness evaluation factor assignment interval. On this basis, the main source rock thickness evaluation factor assignment function is established to convert the main source rock thickness into the corresponding evaluation factor.

[0115] S52, anger intensity evaluation factor assignment function:

[0116] The anger intensity parameter is used as the independent variable, and the anger intensity evaluation factor is used as the dependent variable. The domain is the interval from the minimum to the maximum value of the anger intensity plane. The anger intensity threshold is included in this interval, and the range is the anger intensity evaluation factor assignment interval. Based on this relationship, a anger intensity evaluation factor assignment function is established to convert anger intensity into an evaluation factor.

[0117] S53, fault crack evaluation factor assignment function:

[0118] The fault fracture evaluation factor assignment function uses the fault fracture density per square kilometer divided by 10 for standardization. This is used as the independent variable, and the result is directly used as the fault fracture evaluation factor assignment function. This function aims to intuitively reflect the degree of fracture development of the lithologic body through the fault fracture density.

[0119] S54, microstructure type evaluation factor assignment function:

[0120] Within the micro-structure type evaluation factor assignment range, different interval values ​​are assigned as evaluation factors according to the differences in the degree of influence of different micro-structure types on gas reservoir production;

[0121] S55, lithologic trap area evaluation factor assignment function:

[0122] The lithologic trap area parameter is used as the independent variable, and the lithologic trap area evaluation factor is used as the dependent variable. The domain is from the value of the lithologic trap area evaluation threshold to the maximum value of the planar lithologic trap area. The evaluated lithologic body area is greater than the lithologic trap area threshold. The range is the lithologic trap area evaluation factor assignment interval. Based on this, the lithologic trap area evaluation factor assignment function is established to evaluate the size of the lithologic trap.

[0123] S56, reservoir sweet spot porosity evaluation factor assignment function:

[0124] The reservoir sweet spot porosity parameter is used as the independent variable, the reservoir sweet spot porosity evaluation factor is used as the dependent variable, and the domain is from the value of the reservoir sweet spot porosity evaluation threshold to the maximum value of the plane predicted reservoir porosity. The porosity of the evaluated lithologic bodies is greater than the sweet spot porosity threshold, and the range is the reservoir sweet spot porosity evaluation factor assignment interval. Based on this relationship, a reservoir sweet spot porosity evaluation factor assignment function is established to evaluate the reservoir performance.

[0125] S57, mudstone interlayer thickness evaluation factor assignment function:

[0126] The mudstone interlayer thickness parameter is used as the independent variable, the mudstone interlayer thickness evaluation factor is used as the dependent variable, the domain is the interval from the minimum to the maximum value of the mudstone interlayer thickness plane, the mudstone interlayer thickness threshold is included in this interval, and the range is the mudstone interlayer thickness evaluation factor assignment interval. Based on this, a mudstone interlayer thickness evaluation factor assignment function is established to evaluate the mudstone interlayer's ability to block the upward migration of natural gas.

[0127] S58, regional cover thickness evaluation factor assignment function:

[0128] The regional cover thickness parameter is taken as the independent variable, the regional cover thickness evaluation factor is taken as the dependent variable, the domain is the interval from the minimum to the maximum value of the regional cover thickness plane, the regional cover thickness threshold is included in this interval, and the range is the regional cover thickness evaluation factor assignment interval. Based on this relationship, a regional cover thickness evaluation factor assignment function is established to evaluate the sealing ability and protection performance of the regional cover.

[0129] In an optional embodiment, the assignment function formula fitting includes:

[0130] S591, when the single parameter threshold is the boundary of the domain interval:

[0131] S5911, definition module: the single parameter is the independent variable x, the single parameter evaluation factor is the dependent variable y, the definition domain is the interval [a1, a2] between the value a1 of the single parameter evaluation threshold and the single parameter plane maximum value a2, and the range is the evaluation factor assignment interval [b1, b2];

[0132] S591 2, discrimination module: x = a1, y = b1; x = a2, y = b2;

[0133] S591 3, assignment function: Based on the two sets of data (a1, b1) and (a2, b2), establish the "parameter-factor" assignment function fitting formula;

[0134] S592, when a single parameter threshold is contained within the domain interval, it is not the domain boundary:

[0135] S5921, definition module: the single parameter is the independent variable x, the single parameter evaluation factor is the dependent variable y, the definition domain is the interval [a1, a2] between the minimum value a1 and the maximum value a2 of the single parameter plane, and the range is the evaluation factor assignment interval [b1, b2];

[0136] S5922, discrimination module: x=a1, y=b1; x=a2, y=b2;

[0137] S5923, assignment function: Based on the two sets of data (a1, b1) and (a2, b2), a "parameter-factor" assignment function fitting formula is established.

[0138] In an optional embodiment, the calculation of the single parameter evaluation factor of the S6 lithologic body includes:

[0139] S61, calculation of the evaluation factor of the thickness of the main source rock of the lithologic body:

[0140] First, the planar boundaries of the lithologic bodies predicted to be favorable for large-scale hydrocarbon accumulation are accurately superimposed on the plane map of the main source rock thickness to ensure the spatial consistency of the two. Then, the thickness of the main source rock within each lithologic body is precisely measured, and the average value is calculated to reflect the overall thickness characteristics of the source rock within that lithologic body. Finally, based on the pre-set main source rock thickness evaluation factor assignment function, the calculated average thickness is converted into a corresponding evaluation factor for subsequent comprehensive evaluation.

[0141] S62, calculation of evaluation factor of gas generation intensity of lithologic body:

[0142] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the gas generation intensity plane map to ensure the accuracy of the evaluation range. Then, the gas generation intensity within each lithologic body is statistically analyzed and its average value is calculated to characterize the gas generation potential of the lithologic body. Finally, based on the gas generation intensity evaluation factor assignment function, the average gas generation intensity is converted into an evaluation factor, providing a basis for evaluating the gas generation performance of the lithologic body.

[0143] S63, calculation of evaluation factors for fault fractures in lithologic bodies:

[0144] First, the planar predicted boundaries of lithologic bodies favorable for large-scale reservoir formation are superimposed on the planar distribution map of fault fractures to ensure accurate correspondence between fault fracture information and lithologic body locations. Then, the fault fracture density per square kilometer within each lithologic body is statistically calculated to reflect the degree of fracture development within that lithologic body. Finally, based on the fault fracture evaluation factor assignment function, the fault fracture density is converted into an evaluation factor for use in evaluating the fracture and fracture characteristics of the lithologic body.

[0145] S64, calculation of the evaluation factor of the microstructural type of the lithologic body:

[0146] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on a microstructural plane map to ensure accurate correspondence between microstructural information and lithologic body locations. Next, a detailed statistical analysis of the microstructural types developed within each lithologic body is performed to reflect the microstructural characteristics of that lithologic body. Finally, based on a microstructural type evaluation factor assignment function, the statistically obtained microstructural types are converted into evaluation factors for use in evaluating the structural favorableness of the lithologic body.

[0147] S65, calculation of lithologic body trap area evaluation factor:

[0148] First, on the lithologic body distribution map, the area of ​​each lithologic body that is conducive to large-scale reservoir formation is accurately counted to ensure the accuracy of the area data. Then, based on the lithologic trap area evaluation factor assignment function, the statistically obtained trap area is converted into an evaluation factor to evaluate the scale and potential of the lithologic body reservoir formation.

[0149] S66, calculation of the porosity evaluation factor of the lithologic body sweet spot:

[0150] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the plane map of predicted reservoir porosity to ensure accurate correspondence between porosity information and lithologic body locations. Then, the porosity within each lithologic body is precisely measured, and its average value is calculated to reflect the reservoir properties of that lithologic body. Finally, based on the reservoir sweet spot porosity evaluation factor assignment function, the average porosity is converted into an evaluation factor for evaluating the reservoir performance of the lithologic body.

[0151] S67, calculation of evaluation factor for mudstone interlayer thickness in lithologic bodies:

[0152] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on a planar map of mudstone interlayer thickness to ensure that the interlayer thickness information accurately corresponds to the lithologic body location. Then, the mudstone interlayer thickness within each lithologic body is precisely measured, and its average value is calculated to reflect the interlayer development encountered during natural gas migration into that lithologic body. Finally, based on the mudstone interlayer thickness evaluation factor assignment function, the average interlayer thickness is converted into an evaluation factor, which is used to evaluate the interlayer sealing performance of the lithologic body.

[0153] S68, calculation of evaluation factor of caprock thickness in lithologic body area:

[0154] First, the planar predicted boundaries of lithologic bodies that are conducive to large-scale reservoir formation are superimposed on the regional cap rock thickness plan map to ensure that the cap rock thickness information accurately corresponds to the lithologic body location. Then, the regional cap rock thickness within each lithologic body is accurately measured, and its average value is calculated to reflect the cap rock development of that lithologic body. Finally, based on the regional cap rock thickness evaluation factor assignment function, the average cap rock thickness is converted into an evaluation factor for evaluating the cap rock sealing ability and protective performance of the lithologic body.

[0155] In an optional embodiment, S7 multi-parameter quantitative evaluation comprehensive factor determination includes:

[0156] S71, Summarize and create a table: First, systematically summarize and organize the single-parameter evaluation factors of all lithologic bodies to ensure data integrity and accuracy; then, create a table containing all lithologic bodies and their corresponding single-parameter evaluation factors for subsequent calculations and analysis;

[0157] S72, single parameter level weight setting: according to the three levels of primary control parameters, secondary control parameters, and reference parameters, set the single parameter weight. The weight is obtained by the difference between the maximum and minimum values ​​of the single parameter evaluation factor assignment interval. By quantifying the value range of the single parameter, its weight in the comprehensive evaluation is reasonably allocated;

[0158] S73, calculating comprehensive factor 1: performing a weighted summation of all single parameter evaluation factors of each lithologic body according to the single parameter evaluation factors of each lithologic body and their corresponding weights, and linearly scaling the sum to the range of 0-1 as the multi-parameter quantitative evaluation comprehensive factor 1;

[0159] S74, calculating comprehensive factor 2: based on the single parameter evaluation factors and their weights, calculating the weighted root mean square of all single parameter evaluation factors for each lithologic body, and linearly scaling them to the range of 0-1, as the multi-parameter quantitative evaluation comprehensive factor 2;

[0160] S75, calculating comprehensive factor 3: based on the single parameter evaluation factors and their weights, simply sum all single parameter evaluation factors of each lithologic body and linearly scale them to the range of 0-1 to serve as the multi-parameter quantitative evaluation comprehensive factor 3;

[0161] S76, calculate comprehensive factor 4: based on the single parameter evaluation factors and their weights, calculate the root mean square of all single parameter evaluation factors for each lithologic body, and linearly scale them to the range of 0-1 as the multi-parameter quantitative evaluation comprehensive factor 4.

[0162] In an optional embodiment, the linear scaling of the comprehensive factor to the interval of 0-1 includes:

[0163] S77, take the minimum value of the comprehensive factor a1, and when the ratio is in the range of 0-1, assign it a value of 0;

[0164] S78, taking the maximum value of the comprehensive factor a2, when the ratio is in the range of 0-1, assigning a value of 1;

[0165] S79. Establish a linear relationship based on the two sets of data (a1, 0) and (a2, 1): y = ax + c, where a and c are both constants, and when x = a1, y = 0; when x = a2, y = 1.

[0166] In an optional embodiment, S8 histogram obtains a reference standard for dividing favorable area types, including:

[0167] S81. Prepare a probability distribution histogram of comprehensive factor 1: First, perform statistical analysis on the calculated multi-parameter quantitative evaluation comprehensive factor 1 to determine its numerical range and distribution. Then, based on the normal distribution characteristics, or approximate normal distribution characteristics, of comprehensive factor 1, divide the numerical range into three main numerical intervals: large, medium, and small. These three intervals correspond to the first, second, and third favorable zone types, respectively, and serve as reference standards for classifying favorable zone types.

[0168] S82, prepare a probability distribution histogram of comprehensive factor 2: perform statistical analysis on comprehensive factor 2 and draw its probability distribution histogram. According to the normal distribution characteristics of comprehensive factor 2, divide it into three value intervals: large, medium, and small, corresponding to the first, second, and third favorable area types respectively;

[0169] S83, prepare a probability distribution histogram of comprehensive factor 3: perform statistical analysis on comprehensive factor 3 and draw its probability distribution histogram. Based on the normal distribution characteristics of comprehensive factor 3, divide it into three numerical intervals: large, medium, and small, which are used to classify favorable areas into Class I, Class II, and Class III.

[0170] S84. Prepare the probability distribution histogram of comprehensive factor 4: Conduct statistical analysis on comprehensive factor 4 and draw its probability distribution histogram. According to the normal distribution characteristics of comprehensive factor 4, it is divided into three numerical intervals: large, medium and small, corresponding to the first, second and third types of favorable areas respectively.

[0171] In an optional embodiment, the favorable type classification of the S9 lithologic body includes:

[0172] S91: Based on the three favorable zone types (Class I, Class II, and Class III) classified by comprehensive factor 1, all lithologic bodies on the plane are analyzed one by one, and the favorable types are classified according to the value of comprehensive factor 1. The classification results of the lithologic bodies encountered by wells are compared and analyzed with the actual well production data to verify the accuracy and reliability of the classification. The setting range of the comprehensive factor distribution interval used for the favorable zone type classification is repeatedly iterated and revised to obtain the optimal setting range of the comprehensive factor interval suitable for the favorable zone type classification;

[0173] S92: Based on the three favorable zone types defined by comprehensive factor 2, all lithologic bodies on the plane are classified into favorable types. The classification results of the lithologic bodies encountered by wells are compared and analyzed with the actual well production data. The most favorable lithologic body type is further verified and selected from the perspective of comprehensive factors of different algorithms. The setting range of the comprehensive factor distribution interval used for the classification of favorable zone types is repeatedly iterated and revised to obtain the optimal setting range of the comprehensive factor interval suitable for the classification of favorable zone types.

[0174] S93, repeating the above classification, comparison analysis and iterative correction process according to the three favorable area types divided by comprehensive factor 3;

[0175] S94: Based on the three favorable zone types defined by comprehensive factor 4, all lithologic bodies on the plane are reclassified into favorable types. The classification results of lithologic bodies encountered by wells are compared and analyzed in detail with the actual production data of the wells. The range of the comprehensive factor interval is repeatedly iterated and revised.

[0176] S95, using the Monte Carlo simulation approach, comprehensively evaluates the impact of different comprehensive factor calculation methods on the classification of favorable lithologic body types. The accuracy, stability, and predictive ability of the comprehensive factors for favorable lithologic body classification under different algorithms are analyzed. The most suitable favorable lithologic body classification scheme is selected, and the optimal comprehensive factors are then determined, providing a basis for the subsequent division of favorable tight sandstone gas zones and exploration and development.

[0177] In an optional embodiment, the invention further comprises:

[0178] S11. Comprehensive factors of lithologic bodies are used to rank and guide exploration:

[0179] Based on the analysis results of steps S9 and S10, the comprehensive factors of the lithologic bodies not encountered by wells in the favorable area of ​​the target layer are ranked as a whole. According to the ranking results, the favorable lithologic bodies are selected as the main targets of the next step of tight sandstone gas exploration.

[0180] The embodiments of the present invention provide a comprehensive factor quantitative evaluation method for the division of favorable areas for tight sandstone gas. In response to technical defects in the existing technology, such as the simplification of the evaluation process of the main controlling factors of reservoir formation, insufficient quantitative representation, and strong reliance on subjective experience, the method of the embodiments of the present invention proposes a dynamic quantitative evaluation system driven by multi-source data collaboration. By constructing a three-in-one technical framework of "organic fusion of multi-dimensional data - dynamic factor assignment and weighting - comprehensive factor quantitative representation", it effectively solves the problems of poor regional adaptability and insufficient scalability of traditional evaluation methods, filling the gap in systematic quantitative evaluation technology in this field.

[0181] The method of the present invention pioneers a technical framework of "organic fusion of multidimensional data - dynamic factor assignment and weighting - comprehensive factor quantitative characterization." This overcomes the limitations of traditional single-parameter analysis and constructs an evaluation index system that includes five key geological factors: gas source conditions, reservoir conditions, caprock conditions, structural conditions, and migration conditions, as well as eight core parameters, including the thickness of the main source rock, gas generation intensity, and fault fracture density. A dynamic factor assignment and weighting mechanism, based on multi-source data intersection analysis of drilling test data, geological maps, and geophysical results, dynamically quantifies parameter contributions, achieving a paradigm shift from qualitative empirical judgment to quantitative scientific evaluation of the controlling factors. A multi-algorithm comprehensive factor evaluation system is constructed. By combining multiple algorithms such as weighted summation and weighted root mean square (RMS), a quantitative evaluation histogram with spatial distribution characteristics is formed, along with a comprehensive factor reference standard for the classification of favorable zone types based on this system. This is used to classify favorable lithologic body types. The optimal comprehensive factor is finally selected through repeated iterations and comparison with well drilling results. Compared with traditional qualitative evaluation, this evaluation result can significantly improve the prediction accuracy of favorable zones by over 40%. BRIEF DESCRIPTION OF THE DRAWINGS

[0182] Figure 1 is a flow chart of the method of the present invention;

[0183] Figure 2 This is a flow chart for analyzing the relationship between a single parameter and gas reservoir production in the present invention;

[0184] Figure 3 This is a structural diagram of the relationship analysis between a single parameter and gas reservoir production in the present invention;

[0185] Figure 4 This is a structural diagram of a single-parameter plane diagram analysis in the present invention;

[0186] Figure 5 This is a flow chart for plane analysis of thickness of the main source rocks in the present invention;

[0187] Figure 6 This is a flow chart of the plane analysis of the gas intensity in the present invention;

[0188] Figure 7 This is a flow chart of the plane analysis of fault crack density in the present invention;

[0189] Figure 8 This is a flow chart of plane analysis of micro-structure types in the present invention;

[0190] Figure 9 This is a flow chart of reservoir porosity plane analysis in the present invention;

[0191] Figure 10 This is a flow chart for plane analysis of lithologic trap area in the present invention;

[0192] Figure 11 This is a flow chart for the plane analysis of mudstone interlayer thickness in the present invention;

[0193] Figure 12 This is a flow chart of the plane analysis of regional cover thickness in the present invention;

[0194] Figure 13 This is a structural diagram of the single parameter evaluation threshold analysis in the present invention;

[0195] Figure 14 This is a flow chart of single parameter hierarchical weight analysis in the present invention;

[0196] Figure 15 This is a structural diagram of the interval assignment analysis of a single parameter evaluation factor in the present invention;

[0197] Figure 16 This is a structural diagram of the single parameter evaluation factor assignment function analysis in the present invention;

[0198] Figure 17 This is a structural diagram of the fitting formula of the single-parameter evaluation factor assignment function in the present invention; Figure 18 This is a structural diagram of the fitting formula of the single-parameter evaluation factor assignment function in the present invention; Figure 19This is a structural diagram of the calculation of the single parameter evaluation factor of the lithologic body in the present invention;

[0199] Figure 20 This is a structural diagram of the multi-parameter quantitative evaluation comprehensive factor calculation in the present invention;

[0200] Figure 21 This is a block diagram of the linear ratio 0-1 interval structure of the comprehensive factor in the present invention;

[0201] Figure 22 This is a structural diagram of determining favorable area types based on the comprehensive factor histogram in the present invention;

[0202] Figure 23 This is a structural diagram for classifying favorable types of lithologic bodies in the present invention;

[0203] Figure 24 This is a cross-plot of a single parameter and gas reservoir production in the present invention, where:

[0204] Figure 24 (a) is the cross-plot of the thickness of the main source rock and the gas reservoir production;

[0205] Figure 24 (b) is the cross-plot of gas generation intensity and gas reservoir production;

[0206] Figure 24 (c) is the cross-plot of reservoir porosity and gas reservoir production;

[0207] Figure 24 (d) is the cross-plot of fault fracture density and gas reservoir production;

[0208] Figure 24 (e) is the cross-plot of mudstone barrier thickness and gas reservoir production;

[0209] Figure 24 (f) is the cross-plot of regional cap rock thickness and gas reservoir production;

[0210] Figure 25 This is the thickness map of the main source rocks in the present invention;

[0211] Figure 26 This is the hydrocarbon generation intensity diagram of the main source rocks in the present invention;

[0212] Figure 27 is a distribution diagram of fracture cracks in the present invention;

[0213] Figure 28 This is a micro-structure diagram of the present invention;

[0214] Figure 29 This is the predicted sand body thickness map in the present invention;

[0215] Figure 30 This is the predicted reservoir porosity map in the present invention;

[0216] Figure 31 The distribution map of the lithologic bodies in this invention is

[0217] Figure 32 This is a diagram of the thickness of the mudstone interlayer in the present invention;

[0218] Figure 33 This is a map of regional cap layer thickness in the present invention;

[0219] Figure 34 This is a cross-sectional view of the target lithologic body gas reservoir in the present invention;

[0220] Figure 35 is the comprehensive factor histogram of the present invention, wherein:

[0221] Figure 35 (a) is the histogram of comprehensive factor 1 in the present invention:

[0222] Figure 35 (b) is the histogram of comprehensive factor 2 in the present invention:

[0223] Figure 35 (c) is the histogram of comprehensive factor 3 in the present invention:

[0224] Figure 35 (d) is the histogram of comprehensive factor 4 in the present invention:

[0225] Figure 36 This is a multi-parameter superposition comprehensive evaluation diagram in the present invention:

[0226] Figure 37 This is a favorable zone division diagram for quantitative evaluation of the tight sandstone gas comprehensive factor 1 in the present invention;

[0227] Figure 38 This is a favorable zone division diagram for quantitative evaluation of the comprehensive factor 2 of tight sandstone gas in the present invention;

[0228] Figure 39 This is a favorable zone division diagram for quantitative evaluation of the comprehensive factor 2 of tight sandstone gas in the present invention;

[0229] Figure 40 This is a favorable zone division diagram for quantitative evaluation of the comprehensive factor 2 of tight sandstone gas in the present invention;

[0230] Figure 41 This is a statistical table of gas reservoir production in different micro-structures in the present invention, hereinafter referred to as Table 1;

[0231] Figure 42 This is a summary table of single parameter evaluation reference thresholds, evaluation factors, assignment functions, and parameter weights in the present invention, hereinafter referred to as Table 2;

[0232] Figure 43 This is a statistical table of single parameter evaluation factors of lithologic bodies in the present invention, hereinafter referred to as Table 3;

[0233] Figure 44 This is a summary table of the lithologic body multi-parameter comprehensive factor 1 and favorable zone classification in the present invention, hereinafter referred to as Table 4;

[0234] Figure 45 This is a summary table of the lithologic body multi-parameter comprehensive factor 2 and favorable zone classification in the present invention, hereinafter referred to as Table 5;

[0235] Figure 46 This is a summary table of the lithologic body multi-parameter comprehensive factor 3 and favorable zone classification in the present invention, hereinafter referred to as Table 6;

[0236] Figure 47 This is a summary table of the lithologic body multi-parameter comprehensive factor 4 and favorable zone classification in the present invention, hereinafter referred to as Table 7;

[0237] Figure 48 This is a statistical table of lithologic body well drilling conditions in the present invention, hereinafter referred to as Table 8;

[0238] Figure 49 This is a statistical table of the comprehensive factor 1 of the lithologic body not encountered by well drilling in the present invention, hereinafter referred to as Table 9;

[0239] Figure 50 It is a structural schematic diagram of the electronic device in the present invention. DETAILED DESCRIPTION

[0240] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0241] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other.

[0242] It should be noted that the concepts of "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0243] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0244] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0245] See also Figure 1 The embodiment of the present invention provides a comprehensive factor quantitative evaluation method for dividing favorable areas of tight sandstone gas, comprising the following steps:

[0246] S1. Determine the single parameter evaluation factor based on basic data.

[0247] Basic data includes data and geological analysis maps for the study area. Comprehensive data collection includes drilling data, well logging data, oil and gas test data, production dynamics data, and seismic data. At the same time, meticulously organized geological analysis maps include, but are not limited to, maps of the thickness of major source rocks, maps of gas generation intensity, fracture distribution maps, microstructural maps, predicted lithologic body distribution maps, predicted sand body thickness maps, predicted reservoir porosity distribution maps (or sweet spot porosity distribution maps), mudstone interlayer thickness maps, and mudstone caprock thickness maps.

[0248] On this basis, based on the accumulation conditions and exploration experience of tight sandstone gas, parameters involved in the delineation of favorable gas reservoir zones are selected as evaluation indicators. These indicators constitute the single parameter evaluation factors in the present invention. Single parameters involved in the subsequent delineation of favorable gas reservoir zones may include, for example, the thickness of the main source rock, gas generation intensity, fracture density (or specific indicators such as number and type), microstructural type, lithologic body area, reservoir sweet spot porosity (or average porosity, effective porosity, etc.), mudstone interlayer thickness, and regional caprock thickness.

[0249] S2. Establishing the relationship between the single parameter and gas reservoir production based on multi-source data intersection, and determining a single parameter evaluation threshold.

[0250] Step S2 may specifically include the following steps:

[0251] S21. Analysis of the relationship between single parameter and gas reservoir production:

[0252] For data-related parameters, a quantitative intersection relationship between the single parameter determined in S1 and gas reservoir production was established through intersection statistics, exploring the influence of single parameters on gas reservoir production. For text-related parameters, such as micro-structural type, the distribution of gas reservoir production under different micro-structural types was statistically analyzed to analyze its relationship with gas reservoir production.

[0253] S22. Single parameter planar graph analysis:

[0254] On the single-parameter plane diagram, combined with the analysis conclusions of S21, the scope of each parameter on the plane and its impact on gas reservoir production were analyzed. By comparing parameter values ​​and gas reservoir production in different areas, the importance of the impact of a single parameter on gas reservoir production was clarified.

[0255] S23. Determine the single parameter evaluation threshold:

[0256] Based on the analysis results of S21 and S22, combined with the importance of individual parameters in influencing gas reservoir production, actual geological conditions, and exploration and development experience, we determined the reference threshold for each parameter in evaluating favorable gas reservoir areas. This threshold will serve as the primary basis for subsequent single-factor assignment interval analysis.

[0257] S3. Based on the single parameter evaluation threshold, the single parameter is divided into multiple levels, and different weights are assigned to single parameters of different levels.

[0258] Taking the single parameter evaluation threshold determined by S2 as a reference, according to the importance of the single parameter and in accordance with the idea of ​​hierarchical analysis, three levels are defined, namely the main control parameters, secondary control parameters, and reference parameters, and different weights are given to parameters at different levels.

[0259] S4. Single parameter evaluation factor interval assignment:

[0260] According to the parameter weight, the single parameter evaluation factor is assigned an interval value, where the difference between the maximum and minimum values ​​of the assignment interval is equal to the parameter weight.

[0261] S5, single parameter evaluation factor assignment function and its formula fitting:

[0262] Using a single parameter as the independent variable and a single parameter evaluation factor as the dependent variable, a linear or nonlinear fitting approach is used to construct a parameter-factor assignment function to determine the evaluation factor corresponding to each single parameter. The evaluation factor is dimensionless. Its purpose is to unify parameters of different dimensions into a relatively consistent metric, providing a basis for subsequent quantitative evaluation.

[0263] S6. Calculation of single parameter evaluation factors of lithologic bodies:

[0264] Based on the established assignment function, the evaluation factors of all single parameters of the target layer prediction lithology are calculated to ensure that each lithology has a complete set of evaluation factor values.

[0265] S7. Determination of comprehensive factors for multi-parameter quantitative evaluation:

[0266] Based on the single-parameter evaluation factors of the lithologic bodies determined in the above steps, various algorithms, such as weighted summation, weighted root mean square (RMS), simple summation, and simple RMS, are used to fuse the single-parameter evaluation factors of all lithologic bodies and obtain multi-parameter comprehensive factors from different algorithms. These comprehensive factors reflect the overall evaluation level of the lithologic body from different perspectives.

[0267] S8. Comprehensive factor histogram to obtain reference standards for classification of favorable areas:

[0268] For each of the multiple groups of comprehensive factors obtained, their histograms were compiled and the numerical distribution characteristics of each group of comprehensive factors were analyzed. Based on the normal distribution characteristics of the histogram numerical intervals or the actual distribution, the reference standards for using each group of comprehensive factors to divide favorable areas were determined.

[0269] S9. Classification of favorable lithologic body types and iterative optimization of drilling results:

[0270] Based on the multiple sets of comprehensive factor reference standards, all lithologic bodies are classified into favorable types. These lithologic bodies are then divided into different types of favorable or potential areas. The classification results for lithologic bodies encountered in each group are then compared with the actual drilling results. Through repeated iterations, the most appropriate comprehensive factor favorable area classification reference standard for the study area is selected.

[0271] S10. Division of favorable tight sandstone gas zones and determination of favorable ranges:

[0272] Based on the preferred comprehensive factor favorable zone division reference standard and the distribution of all classified favorable lithologic body types, the scope of different types of favorable zones, such as "Class I, Class II, and Class III," is delineated on the plane. Among them, Class I and Class II favorable zones will be the focus of subsequent exploration work.

[0273] S11. Comprehensive factors of lithologic bodies are used to rank and guide exploration:

[0274] Based on the analysis results of S9 and S10, the comprehensive factors of the lithologic bodies not encountered by wells in the "Class I and Class II" favorable areas of the target layer were ranked overall. Based on the ranking results, the most favorable lithologic bodies were selected as the main targets for the next round of tight sandstone gas exploration.

[0275] In some embodiments, the analysis of the relationship between a single parameter and gas reservoir production specifically includes the following steps:

[0276] S211, table creation and data organization:

[0277] Create a table and compile statistics for key geological and engineering parameters of the target formations, well by well. These include: natural gas test production, thickness of the primary source rock, gas generation intensity (i.e., the amount of natural gas generated per unit volume of source rock), microstructural types (such as fault noses, anticlines, nose rises, slopes, and synclines), fault fracture density, reservoir porosity, lithologic trap area, mudstone barrier thickness, and regional caprock thickness. These data will serve as the basis for subsequent analysis.

[0278] S21 2, Data parameter analysis and cross-plot creation:

[0279] Using the collected data, a series of data parameter analyses are performed. The specific steps include:

[0280] S21 21Establish a cross-plot of the thickness of the main source rocks and natural gas production to explore the effect of source rock thickness on natural gas production;

[0281] S21 22 Establish a cross-plot of gas generation intensity and natural gas production to analyze the relationship between gas generation intensity and production;

[0282] S21 23 Establish a cross-plot of reservoir porosity and natural gas production to study the contribution of porosity to production;

[0283] S21 24 Establish a cross-plot of fault fracture density and natural gas production to analyze the impact of fault fracture development on natural gas migration and accumulation;

[0284] S21 25 Establish a cross-plot of mudstone barrier thickness and natural gas production to analyze the impact of mudstone barrier thickness below the target layer on the vertical migration of natural gas and its impact on the production of the target layer gas reservoir;

[0285] S21 26 Establish a cross-plot of regional caprock thickness and natural gas production to evaluate the impact of caprock thickness on gas reservoir preservation and production;

[0286] S21 27 established a cross-plot of the lithologic body area encountered by the well and the gas reservoir production, and analyzed the requirements for the lithologic body area to form a large-scale gas reservoir.

[0287] S21 3. Text parameter analysis and microstructure type statistics:

[0288] For each microstructural type encountered during drilling, the corresponding gas reservoir production is calculated. This analysis reveals the impact of different microstructural types on gas reservoir production. This step also helps identify microstructural types that have a significant impact on natural gas production, providing guidance for subsequent favorable zone evaluation and exploration target selection.

[0289] In some embodiments, single parameter planar graph analysis specifically includes the following steps:

[0290] S221, analysis of the thickness of the main source rocks:

[0291] Based on the quantitative relationship established by the intersection analysis of the thickness of the main source rocks and gas reservoir production, the thickness distribution characteristics of the main source rocks are analyzed on the main source rock thickness plane map, and the plane distribution range of the favorable area for natural gas accumulation is delineated. This range will provide an important basis for subsequent favorable area evaluation. The specific steps include the following:

[0292] S2211, mapping steps: Based on the thickness data of the main source rocks encountered in the wells, use the Shuanghu geological mapping software to compile the thickness contour map of the main source rocks.

[0293] S221 2. Judgment steps: On the plane diagram of the thickness of the main source rock, analyze the trend and regularity of the thickness changes of the main source rock. Based on the quantitative relationship between the thickness of the main source rock and the gas reservoir production, combined with the gas testing results of the well drilling encountering the gas reservoir, analyze and delineate the regional range of the source rock thickness that is conducive to high gas reservoir production.

[0294] S222, Anger Intensity Plane Analysis:

[0295] Based on the quantitative relationship established by cross-analysis of the gas generation intensity of the primary source rocks and gas reservoir production, a detailed analysis of the gas generation intensity of the primary source rocks is conducted on a gas generation intensity plane map, and the distribution range of favorable areas for natural gas accumulation is determined. This analysis helps identify areas with high gas generation potential. Specific steps include:

[0296] S2221, mapping steps: Based on the collected data on the gas generation intensity of the main source rocks, use the Shuanghu geological mapping software to compile a gas generation intensity contour map.

[0297] S2222, judgment steps: On the gas generation intensity plane diagram, analyze the changing trends and patterns of gas generation intensity; based on the quantitative relationship between gas generation intensity and gas reservoir production, combined with the gas testing results of the well drilling encountering the gas reservoir, analyze and delineate the gas generation intensity area that is conducive to high gas reservoir production.

[0298] S223, fault fracture density plane diagram analysis:

[0299] Based on the quantitative relationship established through cross-analysis of fault fracture density and gas reservoir production, we conducted an in-depth study of the development and distribution characteristics of fault fractures on a planar distribution map of fault fractures, and analyzed the distribution areas where fault fractures are conducive to natural gas migration. This analysis will help us understand the vertical migration paths and accumulation areas of natural gas. The specific steps include the following:

[0300] S2231, Fault and fracture prediction step: Use structural interpretation software to interpret the post-stack seismic volume in detail to depict the fault distribution, combine with fracture prediction software to extract ant body attributes from the post-stack seismic volume, and superimpose the two to obtain the fault and fracture plane distribution map.

[0301] S2232, judgment steps: On the fault and fracture distribution map, analyze the development and distribution characteristics of faults and fractures. Based on the quantitative relationship between fault fractures and gas reservoir production, combined with the gas test results of the well drilling encountering the gas reservoir, count and calculate the density of fault fractures developed within a unit square kilometer, and analyze the scope of the fault and fracture areas that are conducive to high gas reservoir production.

[0302] S224, micro-structure type plan view analysis:

[0303] Based on the relationship between microstructural types and gas reservoir production, a detailed analysis of microstructural development characteristics was conducted on a microstructural plan map, and the distribution of microstructural development zones favorable for natural gas accumulation was determined. This analysis will help identify favorable structural locations and provide a basis for subsequent delineation of favorable gas reservoir zones. The specific steps involved are as follows:

[0304] S2241, mapping steps: Use structural interpretation software to conduct detailed tracking and interpretation of the main force layer on the post-stack seismic volume, and obtain depth domain structural data through time-depth conversion, and then use Shuanghu geological mapping software to compile a micro-structural map of the top surface of the main force layer.

[0305] S2242, judgment steps: On the micro-structure map, analyze and describe the development and distribution characteristics of favorable micro-structure types. Based on the statistical relationship between micro-structure types and gas reservoir production, combined with the gas testing results of well drilling encounters in gas reservoirs, analyze and clarify the areas and scopes of the development of micro-structure types that are conducive to high gas reservoir production.

[0306] S225, reservoir porosity planar analysis:

[0307] Based on the quantitative relationship established through cross-analysis of tight sandstone reservoir porosity and gas reservoir production, a detailed analysis of the reservoir porosity distribution is conducted on a reservoir porosity map, and the plane distribution range of the porosity favorable for natural gas accumulation is delineated. This range provides guidance for finding high-porosity, high-productivity reservoirs. The specific steps include the following:

[0308] S2251, inversion steps: invert and predict the porosity of the main layer on the post-stack seismic volume using reservoir inversion software, and compile a porosity contour map of the main layer reservoir using Shuanghu geological mapping software.

[0309] S2252, judgment steps: On the predicted porosity plane map, analyze the variation pattern of reservoir porosity, and based on the statistical relationship between reservoir porosity and gas reservoir production, combined with the gas test results of the well drilling encountering the gas reservoir, analyze and delineate the sweet spot porosity area that is conducive to high gas reservoir production.

[0310] S226, lithologic trap area plan analysis:

[0311] Based on the relationship between the area of ​​lithologic bodies encountered during drilling and gas reservoir production, the predicted lithologic body distribution map, combined with actual drilling data, is used to delineate the distribution of lithologic bodies that meet a certain scale and are favorable for natural gas accumulation, and the lithologic body area is calculated. This delineation provides support for subsequent favorable zone delineation. Specific steps include:

[0312] S2261, inversion steps: invert and predict the thickness of the main layer sand body on the post-stack seismic volume using reservoir inversion software, and compile a main layer reservoir thickness contour map using Shuanghu geological mapping software.

[0313] S2262, Judgment Step: Analyze the reservoir thickness variation trends and patterns on the predicted reservoir thickness plan. Based on the lithologic gas reservoir analysis approach, delineate the distribution range of the lithologic bodies on the plane. Combined with the porosity range favoring high gas reservoir production identified in S352, and based on the gas testing results of the wells encountered during drilling, ultimately identify and determine a specific lithologic body area conducive to high gas reservoir production.

[0314] S227, mudstone interlayer thickness planar analysis:

[0315] Based on the quantitative relationship established by cross-analysis of mudstone barrier thickness and gas reservoir production, the distribution of mudstone barrier thickness is analyzed on a planar map, and the distribution range of favorable areas for natural gas migration is determined. This analysis will help assess the ability of mudstone barriers beneath the target layer to block upward natural gas migration. The specific steps include the following:

[0316] S2271, mapping steps: Based on the thickness of the mudstone interlayer encountered at the well point, use the Shuanghu geological mapping software to compile a mudstone interlayer thickness contour map.

[0317] S2272, judgment steps: On the mudstone interlayer thickness plane diagram, analyze the changing trend and regularity of the mudstone interlayer thickness; according to the quantitative relationship between the mudstone interlayer thickness and the production volume, combined with the gas test results of the well drilling encountering the gas reservoir, analyze and determine the area range of the mudstone interlayer thickness that is conducive to the high production volume of the gas reservoir.

[0318] S228, regional cover thickness plan analysis:

[0319] Based on the quantitative relationship established through cross-analysis of regional caprock thickness and gas reservoir production, a detailed study of the regional caprock thickness distribution was conducted on a regional caprock thickness plan map, and the distribution range of favorable areas for natural gas accumulation was delineated. This range provided important clues for finding regional caprocks with good sealing properties. The specific steps involved were as follows:

[0320] S2281, mapping steps: Based on the caprock thickness in the area encountered by the well point, use the Shuanghu geological mapping software to compile a regional caprock thickness contour map.

[0321] S2282, judgment steps: On the regional cap rock thickness plan, analyze the trend and law of regional cap rock thickness changes, and according to the quantitative relationship between regional cap rock thickness and reservoir production, combined with the gas test results of the well drilling encountering the gas reservoir, analyze and determine the regional range of cap rock thickness that is conducive to high-yield gas reservoirs.

[0322] In some embodiments, the single parameter evaluation threshold setting specifically includes the following steps:

[0323] S231, thickness threshold of main source rock:

[0324] By thoroughly analyzing the quantitative relationship between primary source rock thickness and gas reservoir production, and combining this relationship's specific range of action on a primary source rock thickness map, we can rationally determine the lower limit of primary source rock thickness conducive to tight sandstone gas accumulation. This lower limit will serve as a key indicator for assessing source rock potential.

[0325] S232, anger intensity threshold:

[0326] Based on the quantitative relationship between gas generation intensity and reservoir production, and the range of this relationship on a gas generation intensity plane diagram, the lower limit of gas generation intensity conducive to tight sandstone gas accumulation can be reasonably determined. This criterion will help identify areas with high gas generation potential.

[0327] S233, fault fracture threshold:

[0328] Based on the quantitative relationship between fault fracture density and gas reservoir production, as well as the planar development and distribution characteristics of fault fractures that facilitate natural gas migration, a threshold for fault fracture evaluation was determined through statistical analysis of fault fracture density per square kilometer. This criterion will be used to assess the extent of regional fault development and natural gas migration potential.

[0329] S234, microstructure type threshold:

[0330] By combining the relationship between microstructural types and gas reservoir production, and the development of microstructural types favorable for tight sandstone gas accumulation on microstructural maps, we can identify microstructural types favorable for gas reservoir formation, such as fault noses, anticlines, nose uplifts, and slopes. This standard will help identify favorable structural locations.

[0331] S235, lithologic trap area threshold:

[0332] By analyzing the relationship between the area of ​​lithologic traps encountered by wells and gas reservoir production, as well as their impact on reservoir size, and based on the area and distribution of lithologic bodies depicted through a combined map of predicted sand body thickness and predicted reservoir porosity, we can rationally determine the lower limit of lithologic trap area conducive to large-scale gas accumulation. This criterion will be used to assess the size and potential of lithologic traps.

[0333] S236, reservoir sweet spot porosity threshold:

[0334] Based on the quantitative relationship between reservoir sweet spot porosity and gas reservoir production, and the range of this relationship in predicting reservoir porosity maps, the lower limit of reservoir sweet spot porosity conducive to tight sandstone gas accumulation can be reasonably determined. This criterion will help identify high-porosity, high-productivity reservoir areas.

[0335] S237, mudstone barrier thickness threshold:

[0336] By analyzing the quantitative relationship between mudstone barrier thickness and gas reservoir production, and the range of this relationship on a planar graph of mudstone barrier thickness, we can rationally determine the lower limit of mudstone barrier thickness that is conducive to the formation of tight sandstone gas reservoirs. This criterion will be used to analyze the extent to which mudstone barriers beneath the target layer hinder the upward migration of natural gas.

[0337] S238, regional cover thickness threshold:

[0338] Based on the quantitative relationship between regional caprock thickness and gas reservoir production, and the range of this relationship on a regional caprock thickness map, we can rationally determine the lower limit of regional caprock thickness conducive to tight sandstone gas accumulation. This criterion will help identify regional caprocks with good sealing properties, ensuring gas reservoir preservation and stable production.

[0339] In some embodiments, the single-parameter level weight setting specifically includes the following steps:

[0340] S31, single parameter level division: According to the importance of the single parameter on gas reservoir production, the single parameter is divided into three levels: "main control parameter, secondary control parameter, and reference parameter" according to the hierarchical analysis method.

[0341] S32, single parameter level weight assignment: Take the difference between the maximum and minimum values ​​of the single parameter evaluation factor assignment interval as the single parameter weight. Among them, the main control parameter weight ≥ 0.7; 0.3 < secondary control parameter weight < 0.7; reference parameter weight ≤ 0.3.

[0342] In some embodiments, the interval assignment of a single parameter evaluation factor specifically includes the following steps:

[0343] S41, evaluation factor of thickness parameter of main source rock:

[0344] Based on the significant impact of the thickness of the primary source rock on gas reservoir production, its relative importance in the delineation of favorable gas reservoir zones is comprehensively assessed. Within the range of 0-1, this parameter is assigned a reasonable interval. The size and span of the assigned value reflect its importance and serve as an evaluation factor for the parameter in subsequent favorable gas reservoir zone evaluation.

[0345] S42, anger intensity parameter evaluation factor:

[0346] Based on the significance of the impact of gas generation intensity on gas reservoir production, its relative importance in the delineation of favorable gas reservoir zones is comprehensively assessed. Similarly, within the range of 0-1, this parameter is assigned a reasonable interval. The size and span of the assigned value reflect its importance and serve as an evaluation factor for the gas generation intensity parameter.

[0347] S43, fault fracture parameter evaluation factor:

[0348] The impact of fault fracture density on the transport and accumulation of natural gas was evaluated to assess its impact on the delineation of favorable gas reservoir zones. Within the range of 0-1, this parameter was assigned reasonable intervals. The size and span of the assigned values ​​reflected its importance and served as an evaluation factor for the fault fracture parameter.

[0349] S44, microstructure type parameter evaluation factor:

[0350] Considering the potential impact of microstructural types on gas reservoir production, their contribution to the delineation of favorable gas reservoir zones is determined. Reasonable interval values ​​are assigned to this parameter within the range of 0-1. The size and span of the assigned values ​​reflect the differences in the impact of different microstructural types on gas reservoirs and serve as evaluation factors for the microstructural type parameter.

[0351] S45, lithologic trap area parameter evaluation factor:

[0352] Based on the control of lithologic body area on gas reservoir size, its importance in the delineation of favorable gas reservoir zones is assessed. Reasonable intervals are assigned to this parameter within the range of 0-1. The size and span of the assigned values ​​reflect the degree of influence of lithologic trap area on gas reservoir size and serve as an evaluation factor for the lithologic trap area parameter.

[0353] S46, reservoir sweet spot porosity parameter evaluation factor:

[0354] Based on the direct impact of sweet spot porosity on gas reservoir production, its criticality in the delineation of favorable gas reservoir zones is determined. Within the range of 0-1, this parameter is assigned a reasonable interval. The size and span of the assigned value reflect the contribution of sweet spot porosity to the delineation of favorable gas reservoir zones and serve as an evaluation factor for the sweet spot porosity parameter.

[0355] S47, mudstone interlayer thickness parameter evaluation factor:

[0356] The role of mudstone interlayer thickness in delineating favorable gas reservoir zones is evaluated based on its impact on reservoir production. Within the range of 0-1, this parameter is assigned appropriate intervals. The magnitude and speed of the assigned values ​​reflect the impact of mudstone interlayer thickness on reservoir production and serve as an evaluation factor for the mudstone interlayer thickness parameter.

[0357] S48, regional cover thickness parameter evaluation factor:

[0358] Based on the impact of regional caprock thickness on gas reservoir production, its importance in the delineation of favorable gas reservoir zones is determined. Within the range of 0-1, this parameter is assigned appropriate intervals. The size and span of the assigned values ​​reflect the contribution of regional caprock thickness to gas reservoir protection and production, serving as an evaluation factor for the regional caprock thickness parameter.

[0359] In some embodiments, the single-parameter evaluation factor assignment function specifically includes the following steps:

[0360] S51, main source rock thickness evaluation factor assignment function:

[0361] The thickness of the primary source rock is used as the independent variable, and the primary source rock thickness evaluation factor is used as the dependent variable. The domain is defined as the range from the primary source rock thickness evaluation threshold to the maximum value of the primary source rock thickness plane, and the range is the primary source rock thickness evaluation factor assignment interval. Based on this, a primary source rock thickness evaluation factor assignment function is established to convert the primary source rock thickness into the corresponding evaluation factor.

[0362] S52, anger intensity evaluation factor assignment function:

[0363] The anger intensity parameter is used as the independent variable, and the anger intensity evaluation factor is used as the dependent variable. The domain is defined as the interval between the minimum and maximum values ​​of the anger intensity plane (the anger intensity threshold is included in this interval), and the range is the anger intensity evaluation factor assignment interval. Based on this relationship, a anger intensity evaluation factor assignment function is established to convert anger intensity into an evaluation factor.

[0364] S53, fault crack evaluation factor assignment function:

[0365] The fault fracture evaluation factor assignment function uses the fault fracture density per square kilometer (divided by 10 for standardization) as the independent variable, and directly uses the result as the fault fracture evaluation factor assignment function. This function aims to intuitively reflect the degree of fracture development in the lithologic body through the fault fracture density.

[0366] S54, microstructure type evaluation factor assignment function:

[0367] Within the microstructure type evaluation factor assignment range, different numerical ranges are assigned as evaluation factors based on the varying degrees of impact of different microstructure types on gas reservoir production. For example, the evaluation factor for a broken nose type is 0.9, the evaluation factor for anticline types is 0.7, the evaluation factor for nose-uplift types is 0.5, the evaluation factor for slope types is 0.3, and the evaluation factor for negative structures is 0.1. This function reflects the differences between different microstructure types through clear numerical correspondences.

[0368] S55, lithologic trap area evaluation factor assignment function:

[0369] The lithologic trap area parameter is used as the independent variable, and the lithologic trap area evaluation factor is used as the dependent variable. The domain is defined from the lithologic trap area evaluation threshold to the maximum planar lithologic trap area (the area of ​​each individual lithologic body evaluated is greater than the lithologic trap area threshold), and the range is the lithologic trap area evaluation factor assignment interval. Based on this, a lithologic trap area evaluation factor assignment function is established to assess the size of lithologic traps.

[0370] S56, reservoir sweet spot porosity evaluation factor assignment function:

[0371] The reservoir sweet spot porosity parameter is used as the independent variable, and the reservoir sweet spot porosity evaluation factor is used as the dependent variable. The domain is defined as the range from the reservoir sweet spot porosity evaluation threshold to the maximum predicted reservoir porosity (the porosity of the evaluated lithologic bodies is greater than the sweet spot porosity threshold), and the range is the reservoir sweet spot porosity evaluation factor assignment interval. Based on this relationship, a reservoir sweet spot porosity evaluation factor assignment function is established to evaluate reservoir performance.

[0372] S57, mudstone interlayer thickness evaluation factor assignment function:

[0373] The mudstone interlayer thickness parameter is used as the independent variable, and the mudstone interlayer thickness evaluation factor is used as the dependent variable. The domain is defined as the interval between the minimum and maximum values ​​of the mudstone interlayer thickness plane, where the mudstone interlayer thickness threshold is included in this interval, and the range is the mudstone interlayer thickness evaluation factor assignment interval. Based on this, a mudstone interlayer thickness evaluation factor assignment function is established to evaluate the mudstone interlayer's ability to block upward natural gas migration.

[0374] S58, regional cover thickness evaluation factor assignment function:

[0375] The regional cover thickness parameter is used as the independent variable, and the regional cover thickness evaluation factor is used as the dependent variable. The domain is defined as the interval between the minimum and maximum values ​​of the regional cover thickness plane (the regional cover thickness threshold is included in this interval), and the range is the assignment interval of the regional cover thickness evaluation factor. Based on this relationship, a regional cover thickness evaluation factor assignment function is established to evaluate the sealing capacity and protective performance of the regional cover.

[0376] In some embodiments, the assignment function fitting formula specifically includes the following steps:

[0377] S591, when the single parameter threshold is the boundary of the domain interval, includes the following steps:

[0378] S5911, definition module: the single parameter is the independent variable x, the single parameter evaluation factor is the dependent variable y, the definition domain is the interval [a1, a2] between the value a1 of the single parameter evaluation threshold and the single parameter plane maximum value a2, and the value range is the evaluation factor assignment interval [b1, b2].

[0379] S5912, discrimination module: x=a1, y=b1; x=a2, y=b2.

[0380] S5913, assignment function: Based on the two sets of data (a1, b1) and (a2, b2), a "parameter-factor" assignment function fitting formula is established.

[0381] S592, when the single parameter threshold is contained within the domain interval (not the domain boundary):

[0382] S5921, definition module: the single parameter is the independent variable x, the single parameter evaluation factor is the dependent variable y, the definition domain is the interval [a1, a2] between the minimum value a1 and the maximum value a2 of the single parameter plane, and the value range is the evaluation factor assignment interval [b1, b2].

[0383] S5922, discrimination module: x=a1, y=b1; x=a2, y=b2.

[0384] S5923, assignment function: Based on the two sets of data (a1, b1) and (a2, b2), a "parameter-factor" assignment function fitting formula is established.

[0385] In some embodiments, calculation of a single parameter evaluation factor of a lithologic body specifically includes the following steps:

[0386] S61, calculation of the evaluation factor of the thickness of the main source rock of the lithologic body:

[0387] First, the planar predicted boundaries of lithologic bodies that are conducive to large-scale hydrocarbon accumulation are accurately superimposed on the plane map of the thickness of the main source rock to ensure the consistency of the spatial positions of the two. Then, the thickness of the main source rock within each lithologic body is accurately measured, and its average value is calculated to reflect the overall thickness characteristics of the source rock within the lithologic body. Finally, according to the pre-set main source rock thickness evaluation factor assignment function, the calculated average thickness is converted into the corresponding evaluation factor for subsequent comprehensive evaluation.

[0388] S62, calculation of evaluation factor of gas generation intensity of lithologic body:

[0389] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the gas-gas intensity plane map to ensure the accuracy of the evaluation range. Then, the gas-gas intensity within each lithologic body is statistically analyzed, and its average value is calculated to characterize the gas-gas potential of the lithologic body. Finally, based on the gas-gas intensity evaluation factor assignment function, the average gas-gas intensity is converted into an evaluation factor, providing a basis for evaluating the gas-gas performance of the lithologic body.

[0390] S63, calculation of evaluation factors for fault fractures in lithologic bodies:

[0391] First, the planar predicted boundaries of lithologic bodies that are conducive to large-scale oil reservoir formation are superimposed on the planar distribution map of fault fractures to ensure that the fault fracture information accurately corresponds to the location of the lithologic bodies. Then, the fault fracture density per square kilometer within each lithologic body is statistically calculated to reflect the degree of fracture development in that lithologic body. Finally, based on the fault fracture evaluation factor assignment function, the fault fracture density is converted into an evaluation factor for use in evaluating the fracture and fracture characteristics of the lithologic body.

[0392] S64, calculation of the evaluation factor of the microstructural type of the lithologic body:

[0393] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the microstructural plane map to ensure accurate correspondence between the microstructural information and the lithologic body locations. Then, a detailed statistical analysis of the microstructural types developed within each lithologic body is performed, such as fault noses, anticlines, nose uplifts, slopes, and synclines, to reflect the microstructural characteristics of the lithologic body. Finally, based on the microstructural type evaluation factor assignment function, the statistically obtained microstructural types are converted into evaluation factors for use in evaluating the structural favorableness of the lithologic body.

[0394] S65, calculation of lithologic body trap area evaluation factor:

[0395] First, on the planar distribution map of lithologic bodies, the area of ​​each lithologic body that is conducive to large-scale reservoir formation is accurately counted to ensure the accuracy of the area data. Then, based on the lithologic trap area evaluation factor assignment function, the statistically obtained trap area is converted into an evaluation factor for evaluating the scale and potential of lithologic body reservoir formation.

[0396] S66, calculation of porosity evaluation factor of lithologic body sweet spot:

[0397] First, the planar boundaries of lithologic bodies predicted to be conducive to large-scale reservoir formation are superimposed on the predicted reservoir porosity plan to ensure that the porosity information accurately corresponds to the location of the lithologic bodies. Then, the porosity within the range of each lithologic body is accurately measured, and its average value is calculated to reflect the reservoir properties of the lithologic body. Finally, based on the reservoir sweet spot porosity evaluation factor assignment function, the average porosity is converted into an evaluation factor for evaluating the reservoir performance of the lithologic body.

[0398] S67, calculation of evaluation factor for mudstone interlayer thickness in lithologic bodies:

[0399] First, the planar predicted boundaries of lithologic bodies favorable for large-scale reservoir formation are superimposed on a planar map of mudstone interlayer thickness to ensure accurate correspondence between interlayer thickness information and lithologic body locations. Then, the mudstone interlayer thickness within each lithologic body is precisely measured, and its average value is calculated to reflect the interlayer development encountered during the migration of natural gas into that lithologic body. Finally, based on the mudstone interlayer thickness evaluation factor assignment function, the average interlayer thickness is converted into an evaluation factor for evaluating the interlayer sealing performance of the lithologic body.

[0400] S68, calculation of evaluation factor of caprock thickness in lithologic body area:

[0401] First, the planar predicted boundaries of lithologic bodies that are conducive to large-scale reservoir formation are superimposed on the regional cap rock thickness plan map to ensure that the cap rock thickness information accurately corresponds to the lithologic body location. Then, the regional cap rock thickness within each lithologic body is accurately measured, and its average value is calculated to reflect the cap rock development of that lithologic body. Finally, based on the regional cap rock thickness evaluation factor assignment function, the average cap rock thickness is converted into an evaluation factor for evaluating the cap rock sealing ability and protective performance of the lithologic body.

[0402] In some embodiments, the multi-parameter quantitative evaluation comprehensive factor is determined, specifically comprising the following steps:

[0403] S71, Summarize and create a table: First, systematically summarize and organize the single-parameter evaluation factors for all lithologic bodies to ensure data integrity and accuracy. Then, create a table containing all lithologic bodies and their corresponding single-parameter evaluation factors for subsequent calculations and analysis.

[0404] S72, Single Parameter Level Weighting: To reflect the varying importance of different single parameter evaluation factors in the comprehensive evaluation, single parameter weights are set based on three levels: primary control parameters, secondary control parameters, and reference parameters. This weight is calculated by taking the difference between the maximum and minimum values ​​within the single parameter evaluation factor's range. This step aims to rationally assign weight to each parameter in the comprehensive evaluation by quantifying its range of values.

[0405] S73, calculate comprehensive factor 1: Based on the single-parameter evaluation factors and their corresponding weights for each lithologic body, perform a weighted sum of all single-parameter evaluation factors for each lithologic body and linearly scale them to the range of 0-1 to obtain the multi-parameter quantitative evaluation comprehensive factor 1. This factor takes into account the evaluation contribution and weight of each single parameter and can more comprehensively reflect the overall characteristics of the lithologic body.

[0406] S74, Calculate Comprehensive Factor 2: Similarly, based on the single-parameter evaluation factors and their weights, calculate the weighted root mean square (RMS) of all single-parameter evaluation factors for each lithologic body and linearly scale them to the range of 0-1 to serve as the multi-parameter quantitative evaluation comprehensive factor 2. This factor not only considers the evaluation contribution and weight of each single parameter but also, through the RMS calculation method, further emphasizes the influence of evaluation factors with larger values ​​on the comprehensive evaluation.

[0407] S75, Calculate Comprehensive Factor 3: To simplify the calculation process, all single-parameter evaluation factors for each lithologic body can be simply summed based on their weights and linearly scaled to a value between 0 and 1 to form the multi-parameter quantitative evaluation comprehensive factor 3. Although this factor is relatively simple to calculate, it can still reflect the basic characteristics of the lithologic body.

[0408] S76, Calculate Comprehensive Factor 4: Alternatively, the root mean square (RMS) of all single-parameter evaluation factors for each lithologic body and their weights can be calculated and linearly scaled to the range of 0–1 to form the multi-parameter quantitative evaluation comprehensive factor 4. This factor, through the RMS calculation, highlights the impact of numerical differences among the single-parameter evaluation factors on the comprehensive evaluation, providing an alternative perspective for lithologic body evaluation.

[0409] Through the six steps above, four sets of multi-parameter quantitative evaluation comprehensive factors (Comprehensive Factor 1, Comprehensive Factor 2, Comprehensive Factor 3, and Comprehensive Factor 4) are obtained, each reflecting the characteristics of the lithologic body from different perspectives. These comprehensive factors can provide an analytical basis for subsequent favorable zone delineation and lithologic body evaluation.

[0410] In some embodiments, the linear scaling of the comprehensive factor to a range of 0-1 specifically includes the following steps:

[0411] S77, take the minimum value of the comprehensive factor a1, and when the ratio reaches the range of 0-1, assign it a value of 0;

[0412] S78, taking the maximum value of the comprehensive factor a2, when the ratio is in the range of 0-1, assigning a value of 1;

[0413] S79. Establish a linear relationship based on the two sets of data (a1, 0) and (a2, 1): y = ax + c, where a and c are both constants, and when x = a1, y = 0; when x = a2, y = 1.

[0414] In some embodiments, obtaining a reference standard for dividing favorable area types from a histogram specifically includes the following steps:

[0415] S81. Prepare a probability distribution histogram for comprehensive factor 1. First, perform a statistical analysis on the calculated multi-parameter quantitative evaluation comprehensive factor 1 to determine its numerical range and distribution. Then, based on the normal distribution characteristics (or approximate normal distribution characteristics) of comprehensive factor 1, divide the numerical range into three main numerical intervals: "large, medium, and small." These three intervals correspond to "Class I, Class II, and Class III" favorable zone types, respectively, and serve as reference standards for dividing favorable zone types. Through the intuitive probability distribution histogram, the distribution of lithologic bodies within the value range of different comprehensive factors can be clearly seen, providing a basis for the division of favorable zones.

[0416] S82, compile a probability distribution histogram for comprehensive factor 2. Similar to step 1, perform statistical analysis on comprehensive factor 2 and plot its probability distribution histogram. Similarly, based on the normal distribution characteristics of comprehensive factor 2, divide it into three numerical ranges: "large," "medium," and "small," corresponding to "Class I," "Class II," and "Class III" favorable zone types, respectively. This step aims to further verify and refine the criteria for classifying favorable zone types based on the comprehensive factors of different algorithms.

[0417] S83. Compile a probability distribution histogram for comprehensive factor 3. Conduct another statistical analysis of comprehensive factor 3 and plot its probability distribution histogram. Based on the normal distribution characteristics of comprehensive factor 3, it is again divided into three numerical ranges: "large, medium, and small." These are used to classify favorable areas into "Class I, Class II, and Class III." This step allows for further comprehensive consideration of the impact of comprehensive factors on favorable area classification using different calculation methods.

[0418] S84. Compile a probability distribution histogram for comprehensive factor 4. Finally, perform a statistical analysis of comprehensive factor 4 and plot its probability distribution histogram. Similarly, based on the normal distribution characteristics of comprehensive factor 4, divide it into three numerical ranges: "large, medium, and small," corresponding to "Class I, Class II, and Class III" favorable zone types, respectively. This step allows for a comprehensive assessment of the impact of different comprehensive factor calculation methods on the favorable zone classification criteria, thereby selecting the optimal classification scheme.

[0419] In the four steps above, the probability distribution histogram of each set of comprehensive factors is drawn based on its respective numerical characteristics and distribution patterns. By comparing and analyzing the histograms of different comprehensive factors, the comprehensive factor that most accurately reflects the favorable nature of the lithologic body and its corresponding classification criteria can be selected, providing a scientific basis for subsequent favorable zone delineation.

[0420] In some embodiments, the classification of favorable lithologic body types specifically includes the following steps:

[0421] S91: Based on the three favorable zone types (Class 1, Class 2, and Class 3) defined by comprehensive factor 1, all lithologic bodies on the plane are analyzed one by one and classified into favorable types according to the value of comprehensive factor 1. The classification results of lithologic bodies encountered by wells are further compared and analyzed with actual well production data to verify the accuracy and reliability of the classification. The setting range of the comprehensive factor distribution interval used for favorable zone classification is repeatedly iterated and revised to obtain the optimal setting range of the comprehensive factor interval for favorable zone classification.

[0422] Similarly, in step S92, all lithologic bodies on the plane are classified into favorable types based on the three favorable zone types identified by comprehensive factor 2. The classification results for lithologic bodies encountered by wells are then compared and analyzed with actual well production data. This step aims to further verify and select the most favorable lithologic body types from the perspective of the comprehensive factors of different algorithms. Similarly, the setting range of the comprehensive factor distribution interval used for favorable zone classification is repeatedly iterated and revised to obtain the optimal comprehensive factor interval setting range for favorable zone classification.

[0423] S93: Repeat the above classification, comparative analysis, and iterative correction process based on the three favorable zone types identified by comprehensive factor 3. This step helps comprehensively consider the favorable properties of lithologic bodies under different calculation methods and increases the robustness of the classification results.

[0424] S94, based on the three favorable area types divided by comprehensive factor 4, all lithologic bodies on the plane are again classified into favorable types, and the classification results of lithologic bodies encountered by wells are compared and analyzed in detail with the actual well production data, and the comprehensive factor interval setting range is repeatedly iterated and revised.

[0425] S95, using the Monte Carlo simulation approach, comprehensively evaluates the impact of different comprehensive factor calculation methods on the classification of favorable lithologic body types. The accuracy, stability, and predictive ability of the comprehensive factors for favorable lithologic body classification under different algorithms are analyzed. The most suitable favorable lithologic body classification scheme is selected, and the optimal comprehensive factors are then determined, providing a scientific basis for the subsequent division of favorable tight sandstone gas zones and exploration and development.

[0426] In the method of the embodiment of the present invention, first, based on the preparation of basic data and information, a quantitative relationship model between a single parameter and gas reservoir production is established based on the intersection analysis of multi-source data. By analyzing the scope of action of this model on the single parameter plane, the inherent mechanism of the influence of each parameter on gas reservoir production is determined, and then the reference threshold for single parameter evaluation is determined. For example, the reference threshold for evaluating the thickness of the main source rock is 2.5m, and the threshold for gas generation intensity is 14×10 8 m 3 / km 2 The reservoir sweet spot porosity is set at 7% as the threshold, and the thresholds for mudstone interlayer thickness and regional cap rock thickness are 40m and 25m, respectively.

[0427] Next, based on the reference thresholds for single-parameter evaluation, a dynamic weighting matrix for single-parameter evaluation factors was constructed using the analytic hierarchy process (AHP). The eight core parameters were divided into three levels: primary controlling parameters (weight ≥ 0.7), secondary controlling parameters (weight 0.3 < 0.7), and reference parameters (weight ≤ 0.3). Based on the parameter hierarchy weights, the single-parameter evaluation factors were dynamically assigned values ​​within the range of 0–1, with the assigned value range corresponding to the hierarchy weights of the single parameters. For example, parameters such as gas generation intensity, fault fracture density, and reservoir sweet spot porosity, which serve as primary controlling parameters for favorable zone delineation, all had hierarchy weights set to 1, and their evaluation factor assignment ranges from 0–1. For example, parameters such as the thickness of the main source rock and the thickness of the regional caprock, which serve as secondary controlling parameters for favorable zone delineation, had hierarchy weights set to 0.4, and their evaluation factor assignment ranges from 0.3 to 0.7. For example, mudstone interlayer thickness, which serves as a reference parameter for favorable zone delineation, had a hierarchy weight set to 0.2, and its evaluation factor assignment ranges from 0.4 to 0.6.

[0428] Then, through linear or nonlinear fitting, a parameter-factor conversion function is established to obtain the evaluation factor of each single parameter. For example, the evaluation function of the main source rock thickness is y = 0.1333x-0.0333, where y represents the main source rock thickness evaluation factor and x represents the main source rock thickness. If x is 2.5m (threshold), y is 0.3; if x is 5.5m (the maximum value on the main source rock thickness plane), y is 0.7, and there is a linear relationship between the two. For example, the evaluation function of the gas intensity evaluation factor is y = 0.2x-2.4, if x is 1 4×10 8 m 3 / km 2 , y is 0.4; if x is 17×10 8 m 3 / km 2, y is 1. For example, the reservoir sweet spot porosity assignment function is y = 0.1667x - 0.6667, where y represents the evaluation factor and x represents the reservoir sweet spot porosity. If x is 7% (the threshold), y is 0.5; if x is at its maximum value of 10%, y is 1, and the values ​​between the two are . Another example is the fault fracture density evaluation function: "fault fracture density per square kilometer / 1 0"; the microstructural type evaluation factor is assigned values ​​based on: 0.9 for fault nose, 0.7 for anticline, 0.5 for nose rise, 0.3 for slope, 0.1 for negative direction, etc.

[0429] Furthermore, the Monte Carlo simulation approach is used to optimize the multi-parameter comprehensive factor calculation model to generate a multi-dimensional comprehensive factor evaluation index set including weighted sum factor, weighted root mean square factor, direct sum factor, direct root mean square factor, etc.

[0430] Finally, a favorable zone classification standard was established based on the normal distribution characteristics of the comprehensive factor value intervals shown in the probability distribution histogram. For example, when the comprehensive factor is greater than 0.8, it is designated as a Class I favorable zone, which has the highest favorableness; when the comprehensive factor is 0.6 ≤ or greater than 0.8, it is designated as a Class II favorable zone, which has the second lowest favorableness; and when the comprehensive factor is less than 0.6, it is designated as a Class III favorable zone, which has relatively low favorableness. To improve the accuracy and reliability of the model, actual drilling data is used for feedback and the model is iteratively optimized to ensure that it is more consistent with actual conditions. The above analysis and evaluation process is repeated for each comprehensive factor derived by the algorithm, and their results are rigorously compared and evaluated. Finally, the comprehensive factor that best matches the actual drilling situation and its corresponding favorable zone classification standard are determined.

[0431] A comprehensive factor quantitative evaluation method for dividing favorable areas of tight sandstone gas provided by an embodiment of the present invention was specifically applied in a certain area of ​​the Ordos Basin. It comprehensively evaluated multiple potential favorable areas of the main gas-producing layers in the area and divided the most favorable areas.

[0432] The evaluation area, located in the southwestern Tianhuan Sag of the Ordos Basin, covers approximately 1,800 square kilometers. A systematic collection of drilling, logging, and other wellbore data, fracturing and gas testing data, and 3D seismic data from 27 wells in the area was compiled. Using this multifaceted data set, single-parameter maps for various geological analyses were compiled, including maps of the thickness of the primary source rock, gas generation intensity, fracture and crack distribution, top structural maps of the target strata, predicted sandstone thickness maps, predicted lithologic distribution maps, reservoir porosity contour maps of the target strata, mudstone interlayer thickness maps, regional caprock thickness maps, and gas reservoir profiles of the target strata.

[0433] The software used for geological analysis is Shuanghu software and Geomap software.

[0434] The following uses a block in the southern Tianhuan Sag of the Ordos Basin as an example to demonstrate the steps and implementation of a comprehensive factor quantitative evaluation method for the division of favorable tight sandstone gas zones.

[0435] The present application is described in detail with reference to the accompanying drawings, using exemplary embodiments of the thirteen implementation steps and their related further configurations. However, it should be understood that elements, structures, and features of one embodiment may also be beneficially incorporated into other embodiments without further description.

[0436] S1, determine the single parameter involved in the evaluation, namely the evaluation factor:

[0437] Eight single parameters, including thickness of main source rock, gas generation intensity, fault fracture density, reservoir sweet spot porosity, microstructural type, lithologic trap area, mudstone barrier thickness, and regional caprock thickness, are selected as evaluation factors in the embodiment of the present invention.

[0438] S2, the single parameter is intersected with the gas reservoir production to determine the quantitative relationship, and the single parameter evaluation threshold is determined. Taking the evaluation block in this embodiment as an example, the specific implementation is as follows:

[0439] S21, intersecting the single parameter with the gas reservoir production to determine a quantitative relationship, specifically includes the following steps:

[0440] S211, create tables and organize data for the 8 single parameters in S1.

[0441] S21 2, based on the single parameters of the data in S211, the intersection analysis is performed to determine the quantitative relationship between them and gas reservoir production, as follows:

[0442] S21 21, the intersection of the thickness of the main source rock and the natural gas production, Figure 24 (a) shows that when the gas reservoir production is less than 40,000 cubic meters per day, the thickness of the main source rock (1.5-5.5m) and the production of the low-yield gas reservoir (less than 40,000 cubic meters per day) have a certain weak linear relationship, namely y = 0.3668x, where y is the gas reservoir production and x is the thickness of the main source rock. However, there is no clear linear correlation between the thickness of the main source rock and the high-yield gas reservoir (greater than 40,000 cubic meters per day). This shows that the thickness of the main source rock in this area is not the only factor that determines the high gas reservoir production. In the embodiment, the well drilling encountered box 8 下 The thickness of the main source rocks corresponding to the gas reservoir production is greater than 2.5m.

[0443] S21 22, gas generation intensity and natural gas production intersect, Figure 24 (b) shows that when the intensity of anger is between 1 3 and 17×10 8 m 3 / km 2When the yield of low-yield gas reservoirs is increased within the range of 1.1194x-1.4179, there is a weak linear relationship between the two, namely y = 1.1194x-1.4179, where y is the yield of the gas reservoir and x is the gas generation intensity. However, when considering industrial-grade high-yield gas reservoirs, this positive correlation becomes weaker, indicating that the formation of high-yield gas reservoirs is also subject to the joint constraints of various other geological factors. 下 The gas generation intensity corresponding to the gas reservoir production is basically 1 4×1 0 8 m 3 / km 2 above.

[0444] S21 23, Intersection of reservoir porosity and natural gas production, Figure 24 Figure (c) shows that the gas production of the gas reservoir below Box 8 has a weak linear positive correlation with reservoir porosity, i.e., y = 0.5556x - 1.7346, where y is the gas reservoir production and x is the reservoir porosity. This pattern is particularly pronounced when the gas reservoir production is less than 40,000 cubic meters per day. However, when the production exceeds 40,000 cubic meters per day, this regularity becomes blurred, indicating that while reservoir properties are an important factor affecting gas reservoir production, they are not the decisive factor. In this example, when the gas reservoir production reaches above 10,000 cubic meters per day, the reservoir porosity below Box 8 is primarily concentrated between 5.8% and 10%, with the high-yield reservoir porosity exceeding 7%.

[0445] S2124, intersection of fault fracture density and natural gas production, Figure 24 (d) shows that there is a strong linear positive correlation between the gas production of the gas reservoir under box 8 and the fault fracture density, that is, y = 1.0828x-2.751 5, where y is the gas reservoir production and x is the fault fracture density. This shows that the degree of fault fracture development has a relatively important impact on the production of the main layer gas reservoir. Box 8 in the embodiment 下 The predicted fault fracture density at the well locations where gas reservoirs achieve production is more than 2 per square kilometer.

[0446] S2125, intersection of mudstone barrier thickness and natural gas production, Figure 24 (e) shows that box 8 下 The gas reservoir production and the thickness of the underlying mudstone interlayer show an exponential negative correlation relationship, that is, y = 17.506e-0.089x, where y is the gas reservoir production and x is the thickness of the mudstone interlayer. 下 Gas reservoir production has an increasing trend. 下 The mudstone barrier thickness required for gas reservoir production is less than 40m.

[0447] S2126, intersection of regional caprock thickness and natural gas production, Figure 24 (f) shows that box 8 下There is an exponential positive correlation between gas reservoir production and regional caprock thickness, y = 0.0929e-0.0792x, where y is gas reservoir production and x is regional caprock thickness. 下 The production of gas reservoir has an increasing trend. 下 The regional cap rock thickness required for high gas reservoir production is greater than 25m.

[0448] S2127, statistics on the lithologic body trap area and the gas test production of the encountered wells show that the lithologic body trap area with effective production generally reaches or exceeds 5km2, especially those wells that encountered the main part of the lithologic body and successfully obtained industrial high-yield gas flow.

[0449] S213, text parameter analysis and microstructure type statistics, as shown in Table 1, Example Block Box 8 下 Statistics on the gas layer production and micro-structure types of wells drilled in the Shan 1 section show that the gas reservoirs with local micro-structures such as broken noses, micro-anticlines, and nose uplifts have higher production.

[0450] S22, single parameter plane diagram analysis, taking the evaluation block of this embodiment as an example, is specifically implemented as follows:

[0451] S221, analysis of the thickness of the main source rocks:

[0452] S2211, mapping steps: Based on the thickness data of the main source rocks encountered in the wells, use the Shuanghu geological mapping software to compile the thickness contour map of the main source rocks.

[0453] S2212, judgment steps: Figure 25 As shown, the thickness of the primary source rock ranges from 1.5 to 5.5 meters, gradually thinning from north to south, particularly along the eastern border and in the southwest corner, where thickness rapidly decreases. Since the S2121 quantitative relationship suggests that primary source rock thickness is not a decisive factor in gas reservoir production, the evaluation criteria for this parameter should be appropriately relaxed to maximize the inclusion of the evaluation area. In this example, areas with a thickness greater than 2.5 meters were selected as the reference for primary source rock thickness.

[0454] S222, Anger Intensity Plane Analysis:

[0455] S2221, mapping steps: Based on the collected data on the gas generation intensity of the main source rocks, use the Shuanghu geological mapping software to compile a gas generation intensity contour map.

[0456] S2222, judgment steps: According to the quantitative relationship of S2122, there is a certain weak linear positive correlation between gas generation intensity and the output of low-yield gas reservoirs. When the gas generation intensity is between 13 and 17×10 8 m 3 / km2 When the range is improved, it can directly promote the increase of gas reservoir production. Figure 26 As shown, the intensity of gas distribution is 12-17×108m 3 / km 2 The interval generally shows a characteristic of gradually decreasing from east to west. In the embodiment, the gas intensity is selected to be greater than 14×108m 3 / km 2 area as a reference.

[0457] S223, fault fracture density plane diagram analysis:

[0458] S2231, Fault and fracture prediction step: Use structural interpretation software to interpret the post-stack seismic volume in detail to depict the fault distribution, combine with fracture prediction software to extract ant body attributes from the post-stack seismic volume, and superimpose the two to obtain the fault and fracture plane distribution map.

[0459] S2232, judgment steps: Figure 27 The fault and fracture distribution map shows that faults and fractures are well developed in the example area. While the degree of fault development in the central and eastern regions is somewhat lower, fractures are still relatively well developed. Fault and fracture development is high near wells with high production rates.

[0460] S224, micro-structure type plan view analysis:

[0461] S2241, mapping steps: Use structural interpretation software to conduct detailed tracking and interpretation of the main force layer on the post-stack seismic volume, and obtain depth domain structural data through time-depth conversion, and then use Shuanghu geological mapping software to compile a micro-structural map of the top surface of the main force layer.

[0462] S2242, judgment steps: Figure 28 On the microstructure map shown, the microstructure types in the example block are relatively developed, among which the favorable local microstructures of broken nose and anticline types are widely distributed throughout the area. Figure 34 As shown in Table 1, these local structures are favorable locations for natural gas accumulation.

[0463] S225, reservoir porosity planar analysis:

[0464] S2251, inversion steps: invert and predict the porosity of the main layer on the post-stack seismic volume using reservoir inversion software, and compile a porosity contour map of the main layer reservoir using Shuanghu geological mapping software.

[0465] S2252, judgment steps: Figure 29 and Figure 30The predicted sand body thickness and predicted reservoir porosity maps show that lithologic traps are well developed in the example block, with less developed sand bodies in the eastern part and more developed sand bodies in the western part. Areas with sand body thickness greater than 4 meters and porosity greater than 7% are considered lithologic body development zones.

[0466] S226, lithologic trap area plan analysis:

[0467] S2261, inversion steps: invert and predict the thickness of the main layer sand body on the post-stack seismic volume using reservoir inversion software, and compile the main layer reservoir thickness contour map using Shuanghu geological mapping software.

[0468] S2262, judgment steps: Figure 31 The figure shows the distribution range of the lithologic bodies delineated by S225, from which the lithologic bodies with an area greater than 5 km2 are selected. 2 monomers, which are the main objects of the evaluation of the next favorable area of ​​the embodiment block.

[0469] S227, mudstone interlayer thickness planar analysis:

[0470] S2271, mapping steps: Based on the thickness of the mudstone interlayer encountered at the well point, use the Shuanghu geological mapping software to compile a mudstone interlayer thickness contour map.

[0471] S2272, judgment steps: Figure 32 The mudstone interlayer thickness plan view shows that the mudstone interlayer thickness in the central and eastern parts of the example block is thicker overall, while the mudstone thickness gradually decreases to the west and south. This thickness distribution characteristic indicates that the central and eastern regions are more conducive to the formation and preservation of the Shan 1 gas reservoir, while the central and western regions are more conducive to the formation and preservation of the Box 8 gas reservoir. 下 Gas reservoir formation preference. According to the quantitative analysis of S2125, the area with mudstone interlayer thickness less than 40m is favorable for the formation of gas reservoirs. 下 Formation of gas reservoirs.

[0472] S228, regional cover thickness plan analysis:

[0473] S2281, mapping steps: Based on the caprock thickness in the area encountered by the well point, use the Shuanghu geological mapping software to compile a regional caprock thickness contour map.

[0474] S2282, judgment steps: Figure 33As shown in the regional cover thickness plan, the cover thickness in the central and northern areas of the example block can reach 30 to 40 meters, showing a strong sealing ability. The cover thickness in the central and southern areas also reaches 25 to 35 meters, and also has good preservation conditions. However, there is a northeast-trending thinning area in the central region, with a cover thickness of about 20 to 25 meters, and its sealing ability is weaker than other areas. The quantitative intersection of S2126 shows that the area with a cover thickness greater than 25 in the example block is conducive to the preservation of box 8. 下 Formation of gas reservoirs.

[0475] S23, establishing a single parameter evaluation threshold, taking the evaluation block of this embodiment as an example, is specifically implemented as follows:

[0476] S231, thickness threshold of main source rock: through S2121 quantitative relationship analysis and S221 plane diagram analysis, the lower limit of the thickness of the main source rock in the example block is determined to be 2.5m as the threshold of this parameter.

[0477] S232, gas intensity threshold: Through the quantitative relationship analysis of S2122 and the plane diagram analysis of S222, the gas intensity threshold of the block in the embodiment is determined to be 14×108m 3 / km 2 .

[0478] S233, fault fracture threshold: Determine the parameter threshold of the fault fracture development density per square kilometer in the embodiment block through the quantitative relationship analysis in S2124 and the plane map analysis in S223.

[0479] S234, micro-structure type threshold: combined with S2242, Figure 34 ,Table 1, clearly defines the micro-structural types such as broken nose, anticline, nose ridge, slope, and negative direction as the threshold types for the evaluation of this parameter.

[0480] S235, lithologic trap area threshold: Based on the quantitative relationship analysis of S2127 and the planar analysis of S226, the lithologic trap area of ​​the example block is determined to be 5km 2 is the parameter threshold.

[0481] S236, reservoir sweet spot porosity threshold: Through the quantitative relationship analysis in S2123 and the planar graph analysis in S225, the lower limit of the reservoir sweet spot porosity in the example block is determined to be 7% as the threshold of this parameter.

[0482] S237, mudstone interlayer thickness threshold: Through the quantitative relationship analysis of S2125 and the planar diagram analysis of S227, the upper limit of the mudstone interlayer thickness in the example block is determined to be 40m as the threshold of this parameter.

[0483] S238, regional cap layer thickness threshold: Through the quantitative relationship analysis in S2126 and the plan view analysis in S228, the lower limit of the cap layer thickness in the block area of ​​the embodiment is determined to be 25m as the threshold of this parameter.

[0484] S3, determination of the single parameter level weight, taking the evaluation block of this embodiment as an example, is specifically implemented as follows:

[0485] S31, single parameter level division: According to the importance of the single parameter on gas reservoir production, the single parameter is divided into three levels: "main control parameter, secondary control parameter, and reference parameter" according to the hierarchical analysis method.

[0486] S32, single parameter level weight assignment: Take the difference between the maximum and minimum values ​​of the single parameter evaluation factor assignment interval as the single parameter weight. Among them, the main control parameter weight ≥ 0.7; 0.3 < secondary control parameter weight < 0.7; reference parameter weight ≤ 0.3.

[0487] In the block shown in Table 2, five primary controlling parameters are identified for this example: gas generation intensity, fault fracture density, microstructural type, lithologic trap area, and reservoir sweet spot porosity; two secondary controlling parameters are identified: primary source rock thickness and regional cap rock thickness; and one reference parameter is identified: mudstone interlayer thickness. Among the primary controlling parameters, the hierarchical weights for gas generation intensity, fault fracture density, and reservoir sweet spot porosity are all 1, the weight for microstructural type is 0.8, and the weight for lithologic trap area is 0.7. Among the secondary controlling parameters, the weights for primary source rock thickness and regional cap rock thickness are both 0.4, and the weight for the secondary controlling parameter, mudstone interlayer thickness, is 0.2.

[0488] S4, single parameter evaluation factor interval assignment, taking the evaluation block of this embodiment as an example, the specific implementation is as follows:

[0489] The main control parameter evaluation factor has the largest range of assignment intervals, indicating that this parameter has the greatest impact on the evaluation results; the secondary parameter evaluation factor has a medium range of assignment intervals, indicating that this parameter has a medium impact on the evaluation results; the reference parameter evaluation factor has the smallest range of assignment intervals, indicating that this parameter has the smallest impact on the evaluation results. The assignment interval of the evaluation factor corresponds to the hierarchical weight of the single parameter, that is, the hierarchical weight is equal to the difference between the maximum and minimum values ​​of the assignment interval. In order to reflect the importance of different parameters in the favorable area evaluation and their impact on the evaluation results, parameters of different levels are assigned different intervals, and even parameters with the same hierarchical weight have different assignment intervals. This setting is for the convenience of application of this setting in other embodiments.

[0490] S41, the evaluation factor interval of the thickness parameter of the main source rock is assigned a value of 0.3-0.7.

[0491] S42, the anger intensity parameter evaluation factor interval is assigned a value of 0-1.

[0492] S43, the fault fracture parameter evaluation factor interval is assigned a value of 0-1.

[0493] S44, the micro-structure type parameter evaluation factor interval is assigned a value of 0.1-0.9.

[0494] S45, the evaluation factor interval of the lithologic trap area parameter is assigned a value of 0-0.7.

[0495] S46, the reservoir sweet spot porosity parameter evaluation factor interval is assigned a value of 0-1.

[0496] S47, the evaluation factor interval of the mudstone interlayer thickness parameter is assigned a value of 0.4-0.6.

[0497] S48, the regional cover thickness parameter evaluation factor interval is assigned a value of 0.3-0.7.

[0498] S5, single parameter evaluation factor assignment function and assignment function formula fitting, taking the evaluation block of this embodiment as an example, for data type parameters, there are two specific implementations as follows:

[0499] S591, when the single parameter threshold is the boundary of the domain interval, specifically including:

[0500] S5911, definition module: the single parameter is the independent variable x, the single parameter evaluation factor is the dependent variable y, the definition domain is the interval [a1, a2] between the value a1 of the single parameter evaluation threshold and the single parameter plane maximum value a2, and the value range is the evaluation factor assignment interval [b1, b2].

[0501] S591 2, discrimination module: x=a1, y=b1; x=a2, y=b2.

[0502] S591 3. Assignment function: Based on the two sets of data (a1, b1) and (a2, b2), establish the "parameter-factor" assignment function fitting formula.

[0503] In this embodiment, based on the above settings, a "parameter-factor" assignment function is used to convert three parameters, including the thickness of the main source rock, the area of ​​the lithologic trap, and the porosity of the reservoir sweet spot, into evaluation factors through formula fitting. The specific settings are as follows:

[0504] 851. Evaluation factor function for the thickness of the primary source rock: Linear: y = 0.1333x - 0.0333. For a single parameter with a threshold of 2.5 m, the evaluation factor is 0.3; for a single parameter with a maximum value of 5.5 m, the evaluation factor is 0.7. When the primary source rock thickness is less than 0.25 m, gas generation conditions are considered extremely poor.

[0505] S55, lithologic trap area evaluation factor assignment function: Logarithm: y = 0.0138x + 0.231, single parameter threshold 5km 2 , the evaluation factor is 0.3; the single parameter is 10km 2 , the evaluation factor is 0.4; the single parameter takes the maximum value of 34km 2 , the evaluation factor is 0.7. When the lithologic trap area is less than 1 km 2 It is not selected as a favorable area.

[0506] S56, reservoir sweet spot porosity evaluation factor assignment function: Linear: y = 0.1667x - 0.6667, single parameter threshold 7%, evaluation factor 0.5; single parameter maximum value 10%, evaluation factor 1. When the reservoir porosity is less than 4%, the reservoir condition is considered extremely poor.

[0507] S592, when the single parameter threshold is contained within the domain interval (not the domain boundary):

[0508] S5921, definition module: the single parameter is the independent variable x, the single parameter evaluation factor is the dependent variable y, the definition domain is the interval [a1, a2] between the minimum value a1 and the maximum value a2 of the single parameter plane, and the value range is the evaluation factor assignment interval [b1, b2].

[0509] S5922, discrimination module: x=a1, y=b1; x=a2, y=b2.

[0510] S5923, assignment function: Based on the two sets of data (a1, b1) and (a2, b2), a "parameter-factor" assignment function fitting formula is established.

[0511] In this embodiment, based on the above settings, a "parameter-factor" assignment function is used to convert three parameters, namely, gas generation intensity, lithologic trap area, mudstone barrier thickness, and regional caprock thickness, into evaluation factors through formula fitting. The specific settings are as follows:

[0512] S52, anger intensity evaluation factor assignment function: linear: y = 0.2x-2.4, single parameter is threshold 1 4×10 8 m 3 / km 2 , the evaluation factor is 0.4; the maximum value of a single parameter is 17×10 8 m 3 / km 2 , the evaluation factor takes the value of 1. When the anger intensity is less than 12×10 8 m 3 / km 2 When angry, the conditions are considered extremely bad.

[0513] S57, mudstone interlayer thickness evaluation factor assignment function: Linear: y = -0.0133x + 0.9333, single parameter 40m, evaluation factor 0.4; single parameter threshold 25m, evaluation factor 0.6. When the mudstone interlayer thickness exceeds 70m, diffusion and migration conditions are considered extremely poor.

[0514] S58, Regional Cover Thickness Evaluation Factor Assignment Function: Linear: y = 0.02x - 0.2, single parameter threshold of 25m, evaluation factor of 0.3; single parameter maximum of 45m, evaluation factor of 0.7. When the regional cover thickness is less than 10m, the preservation condition is considered extremely poor.

[0515] S5, single parameter evaluation factor assignment function and assignment function formula fitting, taking the evaluation block of this embodiment as an example, for text parameters, the specific implementation is as follows:

[0516] S53, fault fracture evaluation factor assignment function: The fault fracture density per square kilometer (divided by 10 for standardization) is directly used as the fault fracture evaluation factor assignment function.

[0517] S54, Microstructure Type Evaluation Factor Assignment Function: Based on the varying degrees of impact of different microstructure types on gas reservoir production, different intervals of values ​​are assigned to serve as the evaluation factor assignment function. In this example, the evaluation factor for the broken nose type is assigned a value of 0.9, the evaluation factor for the anticline type is assigned a value of 0.7, the evaluation factor for the nose rise type is assigned a value of 0.5, the evaluation factor for the slope type is assigned a value of 0.3, and the evaluation factor for the negative structure type is assigned a value of 0.1.

[0518] S6, calculation of single parameter evaluation factor of lithologic body, taking the evaluation block of this embodiment as an example, combined with Table 3, the specific implementation is as follows:

[0519] S61, calculation of the evaluation factor of the thickness of the main source rock of the lithologic body:

[0520] First, the boundaries of the lithologic bodies predicted to be favorable for large-scale hydrocarbon accumulation are accurately superimposed on the plane map of the primary source rock thickness to ensure spatial consistency between the two. Then, the primary source rock thickness within each lithologic body is precisely measured, and the average value is calculated to reflect the overall thickness characteristics of the source rock within that lithologic body. Finally, based on a pre-defined primary source rock thickness evaluation factor assignment function, the calculated average thickness is converted into a corresponding evaluation factor for subsequent comprehensive evaluation. For example, in this example, the primary source rock thickness evaluation factors for lithologic bodies numbered 1, 7, and 12 are calculated to be 0.6, 0.5, and 0.2, respectively.

[0521] S62, calculation of evaluation factor of gas generation intensity of lithologic body:

[0522] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the gas-gas intensity plan to ensure the accuracy of the evaluation range. Then, the gas-gas intensity within each lithologic body is statistically analyzed and its average value is calculated to characterize the gas-gas potential of that lithologic body. Finally, using the gas-gas intensity evaluation factor assignment function, the average gas-gas intensity is converted into an evaluation factor, providing a basis for evaluating the gas-gas performance of the lithologic body. For example, in this example, the gas-gas intensity evaluation factors for lithologic bodies numbered 1, 7, and 12 are calculated to be 0.4, 0.3, and 0.9, respectively.

[0523] S63, calculation of the evaluation factor of the fault fracture density of the lithologic body:

[0524] First, the planar boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the fault-fracture planar distribution map to ensure accurate correspondence between fault-fracture information and lithologic body locations. Next, the fault-fracture density per square kilometer within each lithologic body is statistically calculated to reflect the degree of fracture and crack development within that lithologic body. Finally, based on the fault-fracture evaluation factor assignment function, the fault-fracture density is converted into an evaluation factor for evaluating the fracture and crack characteristics of the lithologic body. For example, in this example, the fault-fracture density evaluation factors for lithologic bodies numbered 1, 7, and 12 are calculated to be 0.4, 0.2, and 0.3, respectively.

[0525] S64, calculation of the evaluation factor of the microstructural type of the lithologic body:

[0526] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the microstructural plan to ensure accurate correspondence between microstructural information and lithologic body locations. Next, a detailed count of microstructural types developed within each lithologic body, such as snouts, anticlines, nose bulges, slopes, and synclines, is performed to reflect the microstructural characteristics of that lithologic body. Finally, using a microstructural type evaluation factor assignment function, the statistically obtained microstructural types are converted into evaluation factors for evaluating the structural favorableness of the lithologic body. For example, in this example, the microstructural type evaluation factors for lithologic bodies numbered 1, 7, and 12 are calculated to be 0.7, 0.5, and 0.4, respectively.

[0527] S65, calculation of lithologic trap area evaluation factor:

[0528] First, on a lithologic body distribution map, the area of ​​each lithologic body that is conducive to large-scale hydrocarbon accumulation is accurately counted to ensure the accuracy of the area data. Then, using a lithologic trap area evaluation factor assignment function, the statistically calculated trap area is converted into an evaluation factor to assess the scale and potential of the lithologic body's hydrocarbon accumulation. For example, in this example, the lithologic trap area evaluation factors for lithologic bodies numbered 1, 7, and 12 are calculated to be 0.4, 0.3, and 0.4, respectively.

[0529] S66, calculation of sweet spot porosity evaluation factor of lithologic reservoir:

[0530] First, the boundaries of the lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the plane map of predicted reservoir porosity to ensure accurate correspondence between porosity information and lithologic body locations. Next, the porosity within each lithologic body is precisely measured, and the average value is calculated to reflect the reservoir properties of that lithologic body. Finally, using the reservoir sweet spot porosity evaluation factor assignment function, the average porosity is converted into an evaluation factor for evaluating the reservoir performance of the lithologic body. For example, in this example, the reservoir sweet spot porosity evaluation factors for lithologic bodies numbered 1, 7, and 12 are calculated to be 0.7, 0.6, and 0.6, respectively.

[0531] S67, calculation of evaluation factor for mudstone interlayer thickness in lithologic bodies:

[0532] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on a planar map of mudstone interlayer thickness to ensure accurate correspondence between interlayer thickness information and lithologic body locations. Next, the mudstone interlayer thickness within each lithologic body is precisely measured, and its average value is calculated to reflect the interlayer development encountered by natural gas during migration into that lithologic body. Finally, using the mudstone interlayer thickness evaluation factor assignment function, the average interlayer thickness is converted into an evaluation factor for evaluating the interlayer sealing performance of the lithologic body. For example, in this example, the mudstone interlayer thickness evaluation factors for lithologic bodies numbered 1, 7, and 12 are calculated to be 0.5, 0.5, and 0.5, respectively.

[0533] S68, calculation of evaluation factor of caprock thickness in lithologic body area:

[0534] First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on a regional caprock thickness map to ensure accurate correspondence between caprock thickness information and lithologic body locations. Next, the regional caprock thickness within each lithologic body is precisely measured, and its average value is calculated to reflect the caprock development of that lithologic body. Finally, using a regional caprock thickness evaluation factor assignment function, the average caprock thickness is converted into an evaluation factor to assess the caprock sealing capacity and protective performance of the lithologic body. For example, in this example, the regional caprock thickness evaluation factors for lithologic bodies numbered 1, 7, and 12 are calculated to be 0.6, 0.2, and 0.3, respectively.

[0535] S7, calculation of comprehensive factors for multi-parameter quantitative evaluation, taking the evaluation block of this embodiment as an example, is specifically implemented as follows:

[0536] S71, create a summary table: As shown in Table 3, create a table containing all lithologic bodies and their corresponding single parameter evaluation factors.

[0537] S72, single parameter level weight setting, as shown in Table 2, combined with S3, the level weight setting is performed for all single parameters.

[0538] S73, calculate comprehensive factor 1: As shown in Table 4, based on the single-parameter evaluation factors and their corresponding weights for each lithologic body, a weighted sum of all single-parameter evaluation factors for each lithologic body is performed, and linearly scaled to the range of 0-1 to form the multi-parameter quantitative evaluation comprehensive factor 1. This factor takes into account the evaluation contribution and weight of each single parameter and can more comprehensively reflect the overall characteristics of the lithologic body. For example, in this example, the calculated comprehensive factors 1 for lithologic bodies numbered 1, 7, and 12 are 0.89, 0.25, and 0.71, respectively.

[0539] S74, calculate comprehensive factor 2: As shown in Table 5, based on the single-parameter evaluation factors and their weights, calculate the weighted root mean square (RMS) of all single-parameter evaluation factors for each lithologic body and linearly scale them to the range of 0-1 to form the multi-parameter quantitative evaluation comprehensive factor 2. This factor not only considers the evaluation contribution and weight of each single parameter but also, through the RMS calculation, further emphasizes the influence of larger evaluation factors on the comprehensive evaluation. For example, in this example, the calculated comprehensive factors 2 for lithologic bodies numbered 1, 7, and 12 are 0.70, 0.12, and 0.68, respectively.

[0540] S75, calculate comprehensive factor 3: As shown in Table 6, based on the single-parameter evaluation factors and their weights, a simple sum is performed for each lithologic body, and the sum is linearly scaled to the range of 0-1 to form the multi-parameter quantitative evaluation comprehensive factor 3. Although this factor is relatively simple to calculate, it still reflects the basic characteristics of the lithologic body. For example, in this example, the calculated comprehensive factors 3 for lithologic bodies numbered 1, 7, and 12 are 0.99, 0.21, and 0.52, respectively.

[0541] S76, Calculate Comprehensive Factor 4: As shown in Table 7, based on the single-parameter evaluation factors and their weights, calculate the root mean square (RMS) of all single-parameter evaluation factors for each lithologic body and linearly scale them to the range of 0–1 to form the multi-parameter quantitative evaluation comprehensive factor 4. This factor, calculated as the RMS, highlights the impact of numerical differences in each single-parameter evaluation factor on the comprehensive evaluation, providing an alternative perspective for lithologic body evaluation. For example, in this example, the calculated comprehensive factors 4 for lithologic bodies numbered 1, 7, and 12 are 0.59, 0.13, and 0.69, respectively.

[0542] Through the six steps above, four sets of multi-parameter quantitative evaluation comprehensive factors (Comprehensive Factor 1, Comprehensive Factor 2, Comprehensive Factor 3, and Comprehensive Factor 4) are obtained, each reflecting the characteristics of the lithologic body from different perspectives. These comprehensive factors can provide an analytical basis for subsequent favorable zone delineation and lithologic body evaluation.

[0543] S8, using the comprehensive factor histogram method to obtain a reference standard for the classification of favorable area types, taking the evaluation block of this embodiment as an example, the specific implementation is as follows:

[0544] S81, Figure 35 As shown in (a), the probability distribution histogram of comprehensive factor 1 shows a normal distribution, with its value range divided into three main intervals: ≥0.8, 0.6-0.8, and ≤0.6. These three intervals correspond to the favorable area types of "Class I, Class II, and Class III," respectively.

[0545] S82, Figure 35 As shown in (b), the probability distribution histogram of comprehensive factor 2 shows that comprehensive factor 2 has an approximately normal distribution characteristic, and its value range is divided into three main value intervals: "≥0.8, 0.5-0.8, and ≤0.5." These three intervals correspond to the "first, second, and third" favorable area types, respectively.

[0546] S83, Figure 35 As shown in (c), the probability distribution histogram of comprehensive factor 3 shows a normal distribution, with its value range divided into three main intervals: ≥0.8, 0.5-0.8, and ≤0.5. These three intervals correspond to the favorable area types of "Class I, Class II, and Class III," respectively.

[0547] S84, Figure 35 As shown in (d), the probability distribution histogram of comprehensive factor 4 shows that comprehensive factor 4 has an approximately normal distribution characteristic, and its value range is divided into three main value intervals: "≥0.8, 0.5-0.8, and ≤0.5." These three intervals correspond to the "first, second, and third" favorable area types, respectively.

[0548] In the above four steps, the probability distribution histogram of each group of comprehensive factors is drawn based on their respective numerical characteristics and distribution laws. By comparing and analyzing the histograms of different comprehensive factors, the comprehensive factors that can most accurately reflect the favorableness of the lithologic body and their corresponding division criteria can be selected, providing a scientific basis for the subsequent division of favorable areas. In this embodiment, in order to obtain the normal distribution results of the histogram, the normal distribution intervals corresponding to the three types of favorable area types of comprehensive factors 2, 3, and 4 are set to ≤0.5, which is different from the setting of the normal distribution intervals corresponding to the three types of favorable area types of ≤0.6 of comprehensive factor 1.

[0549] S9, classification of favorable lithologic body types. Taking the evaluation block in this embodiment as an example, the specific implementation is as follows:

[0550] Based on the three favorable zone types (Class I, Class II, and Class III) defined by each set of comprehensive factors, all lithologic bodies on the plane were analyzed individually and categorized into favorable types based on the numerical values ​​of the comprehensive factors. The classification results for lithologic bodies encountered by wells were then compared with actual well production data to verify the accuracy and reliability of the classification. Through repeated iterations and corrections to the distribution range of the comprehensive factors used for favorable zone classification, the optimal range of comprehensive factor intervals for favorable zone classification was determined.

[0551] S91, comprehensive factor 1, classification of favorable lithologic types: Figure 37 As shown, this example classifies a total of 51 lithologic bodies into favorable types. The classification results show that there are 9 Class I lithologic bodies, 28 Class II lithologic bodies, and 14 Class III lithologic bodies. Among them, Class I lithologic bodies are mainly distributed in the northeastern part of the block in this example, followed by the northwest, and both are distributed in a northeastern strip. Class II lithologic bodies are developed over a large area, separated from Class I lithologic bodies, and have a surrounding feature. On the plane, they are also mainly distributed in the northwest and east of the block. Class III lithologic bodies are mainly distributed in the southwest and southeast of the block. The above lithologic body classification and distribution results are the result of the combined effect of the eight single-parameter evaluation factors in the example of this invention. No single factor can determine the favorable type of lithologic body.

[0552] S92, Figure 38 As shown in Figure 2, this example classified 51 lithologic bodies into favorable types. The classification results show that there are 5 Type I lithologic bodies, 25 Type II lithologic bodies, and 21 Type III lithologic bodies. Type I lithologic bodies are mainly distributed in the northeastern part of the block in this example, and are distributed in a northeast-trending strip. Type II lithologic bodies are widely developed, surrounding Type I lithologic bodies on the one hand, and developing in intervals with Type III lithologic bodies on the other hand. They are mainly distributed in the eastern and northwest parts of the block on the plane. Type III lithologic bodies are mainly distributed in the southwest and northwest parts of the block.

[0553] S93, Figure 39 As shown, this example also classified 51 lithologic bodies into favorable types. The classification results show that there are 8 Class I lithologic bodies, 28 Class II lithologic bodies, and 15 Class III lithologic bodies. Among them, Class I lithologic bodies have three development zones within the block of the example, all distributed in a northeast-trending strip; Class II lithologic bodies are distributed over a large area surrounding Class I lithologic bodies, mainly distributed in the central and northern parts of the block in plane; Class III lithologic bodies are mainly distributed in the southwest and southeastern parts of the block, and also develop in parts of the central and eastern parts of the block.

[0554] S94, Figure 40As shown in Figure 2, this example further classified 51 lithologic bodies into favorable types. The classification results show that there are 5 Type I lithologic bodies, 26 Type II lithologic bodies, and 20 Type III lithologic bodies. Type I lithologic bodies are mainly distributed in the northeastern part of the block in this example, and are distributed in a northeast-trending strip. Type II lithologic bodies are widely developed, surrounding Type I lithologic bodies on the one hand, and developing in intervals with Type III lithologic bodies on the other hand. They are mainly distributed in the eastern and northwest parts of the block on the plane. Type III lithologic bodies are mainly distributed in the southwest and northwest parts of the block.

[0555] S95, using a Monte Carlo simulation approach, comprehensively evaluated the impact of different comprehensive factor calculation methods on the classification of favorable lithologic bodies. The accuracy, stability, and predictive power of the comprehensive factors for favorable lithologic body classification using different algorithms were analyzed. The most suitable favorable lithologic body classification scheme was selected, and the optimal comprehensive factor was determined, providing a scientific basis for subsequent favorable tight sandstone gas zone delineation and exploration and development. In this embodiment of the present invention, a comparison of the lithologic body classification results of S92 and S91 reveals a primary difference in the classification of Class I and Class II lithologic bodies. S91 classified lithologic bodies numbered 1, 18, 37, and 41 as Class I, while S92 classified them as Class II. Combined with gas reservoir testing results from well drilling, it was determined that lithologic bodies numbered 1, 18, and 37 all produced high-yield industrial gas flows, making their classification as Class I more appropriate. Therefore, a comparison of S92 and S91 reveals that S91 provides a better fit with the well data. Comparing the lithologic body classification results of S93 and S91 reveals a major difference in the classification and distribution of Class I and Class II lithologic bodies. S93 classifies lithologic bodies 5 and 40 as Class I, while S91 classifies them as Class II. Furthermore, S91 classifies lithologic bodies 29, 34, and 37 as Class I, while S93 classifies them as Class II. Combined with the gas reservoir test results from the wells, it is clear that lithologic body 37 produced high-yield industrial gas flow and is more appropriately classified as Class I; however, lithologic body 5 did not produce high-yield gas flow and should not be classified as Class I. Therefore, a comparison of S93 and S91 reveals that S91 better matches the wells. A comparison of the lithologic body classification results of S94 and S91 reveals a major difference in the classification of Class I and Class II lithologic bodies. S91 classifies the lithologic bodies numbered 1, 18, 31, and 47 into one category, and S94 classifies them into two categories. Combined with the gas test results of the well drilling encountering the gas reservoir, it can be seen that the lithologic bodies numbered 1 and 18 both obtained high-yield industrial gas flow, and it is more appropriate to classify them as lithologic bodies of one category. Therefore, a comparison between S94 and S91 shows that S91 is more consistent with the well. According to the comparison between the above four algorithms, the "comprehensive factor quantitative evaluation method for the division of favorable areas of tight sandstone gas" provided by the present invention is the most suitable in this embodiment based on the comprehensive factor obtained by single factor weighting and its favorable area division scheme. In the absence of further settings, it is not ruled out that the evaluation results obtained by the other three algorithms are applicable to other embodiments.

[0556] S10, classification of favorable area types and determination of favorable ranges. Taking the evaluation block of this embodiment as an example, the specific implementation is as follows:

[0557] According to the reference standard for the division of favorable areas of the preferred comprehensive factor 1 in S9 and the distribution of favorable types of all lithologic bodies classified by it, the scope of different types of favorable areas such as "Class 1, Class 2, Class 3" is delineated on the plane. Figure 37 Among them, the first and second favorable areas will be the focus of subsequent exploration work. Figure 36 It can be seen that the quantitative evaluation results of the "comprehensive factor quantitative evaluation method for the division of favorable areas of tight sandstone gas" provided by the present invention based on multi-dimensional data fusion are more scientific and reliable than the previous multi-parameter superposition qualitative evaluation results.

[0558] S11, lithologic body comprehensive factor alignment and exploration guidance, taking the evaluation block in this embodiment as an example, the specific implementation is as follows:

[0559] Based on the analysis results of S9 and S10, the comprehensive factors of the lithologic bodies not encountered by wells within the "Class I and Class II" favorable areas of the block in this example were ranked overall. As shown in Table 9, based on the ranking results, the most favorable lithologic body was selected as the primary target for the next stage of tight sandstone gas exploration.

[0560] According to an embodiment of the present invention, the present invention further provides an electronic device and a readable storage medium.

[0561] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above methods when executing the computer program.

[0562] See Figure 50 , which is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 50 As shown, the electronic device 600 may include: at least one processor 601 , at least one network interface 604 , a user interface 603 , a memory 605 , and at least one communication bus 602 .

[0563] The communication bus 602 is used to implement the connection and communication between these components.

[0564] The user interface 603 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 603 may also include a standard wired interface and a wireless interface.

[0565] The network interface 604 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0566] The processor 601 may include one or more processing cores. The processor 601 utilizes various interfaces and circuits to connect various components within the electronic device 600. It executes instructions, programs, code sets, or instruction sets stored in the memory 605, and accesses data stored in the memory 605 to perform various functions and process data within the electronic device 600. Optionally, the processor 601 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 601 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content displayed on the display; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 601 and may be implemented as a separate chip.

[0567] Among them, the memory 605 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 605 includes a non-transitory computer-readable storage medium. The memory 605 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 605 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 605 may also be optionally at least one storage device located away from the aforementioned processor 601. As Figure 50 As shown, the memory 605 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program.

[0568] exist Figure 50In the electronic device 600 shown, the user interface 603 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 601 can be used to call the application stored in the memory 605 and specifically perform the operations of any of the above method embodiments.

[0569] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic or optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0570] An embodiment of the present invention further provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any method described in the above method embodiments.

[0571] Those skilled in the art will clearly understand that the technical solution of the present invention can be implemented with the help of software and / or hardware. "Unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

[0572] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0573] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0574] In the several embodiments provided herein, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented. In addition, the coupling or direct coupling or communication connection shown or discussed may be through some interface. The indirect coupling or communication connection of devices or units may be electrical or other forms.

[0575] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0576] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0577] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program codes.

[0578] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0579] The foregoing is merely an exemplary embodiment of the present invention and is not intended to limit the scope of the present invention. That is, any equivalent changes and modifications made in accordance with the teachings of the present invention are still within the scope of the present invention. Those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the disclosure herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary technical means in the art not described in the present invention. The description and examples are to be considered merely as exemplary, and the scope and spirit of the present invention are defined by the claims.

Claims

1. A comprehensive factor quantitative evaluation method for the division of favorable tight sandstone gas zones, characterized by: include: S1. Determine the single parameter evaluation factor based on basic data; S2. Based on the intersection of multi-source data, establishing the relationship between the single parameter and gas reservoir production, and determining the single parameter evaluation threshold; S3. Divide the single parameter into multiple levels based on the single parameter evaluation threshold, and assign different weights to the single parameters of different levels; S4. Assigning interval values ​​to the single parameter evaluation factor according to the weight, wherein the difference between the maximum value and the minimum value of the assignment interval is equal to the weight of the corresponding parameter; S5. Using a single parameter as the independent variable and a single parameter evaluation factor as the dependent variable, a "parameter-factor" assignment function is constructed using linear or nonlinear fitting to obtain the evaluation factor corresponding to each single parameter; S6. Calculating evaluation factors of all single parameters of the predicted lithologic body of the target layer according to the assignment function; S7. Based on the single parameter evaluation factors of lithologic bodies, multiple algorithms are used to fuse and calculate the single parameter evaluation factors of all lithologic bodies to obtain the multi-parameter comprehensive factors of different algorithms; S8. Compile histograms for each of the multiple groups of comprehensive factors, analyze the numerical distribution characteristics of each group of comprehensive factors, and determine reference standards for using each group of comprehensive factors to divide favorable areas; S9. Classify the favorable types of all lithologic bodies according to the reference standard, and divide the lithologic bodies into different types of favorable areas or potential areas. Compare the classification results of the lithologic bodies encountered in each group of wells with the actual drilling results, and iterate repeatedly to select a comprehensive factor favorable area type classification reference standard suitable for the study area; S10. Based on the reference standard for the division of favorable areas by comprehensive factors and the distribution of favorable types of all lithologic bodies classified by it, the scope of different types of favorable areas is delineated on the plane.

2. The method according to claim 1, characterized in that In step S1, the basic data include data and geological analysis map data of the study area, the data include drilling data, well logging data, oil and gas test data, production dynamic data and seismic data, and the geological analysis map data include main source rock thickness map, gas generation intensity map, fracture distribution map, micro-structure map, predicted lithologic body distribution map, predicted sand body thickness map, predicted reservoir porosity distribution map or sweet spot porosity distribution map, mudstone interlayer thickness map, and mudstone caprock thickness map; According to the accumulation conditions and exploration experience of tight sandstone gas, a single parameter participating in the division of favorable gas reservoir areas is determined as an evaluation index based on the basic data. The evaluation index is the composition of the single parameter evaluation factor. The single parameter includes the thickness of the main source rock, gas generation intensity, fracture index, micro-structural type, lithologic body area, reservoir sweet spot porosity, mudstone interlayer thickness, and regional caprock thickness. The fracture index includes at least one of fracture fracture density, number, and type.

3. The method according to claim 2, characterized in that The step S2 comprises: S21. For data-type parameters, establish a quantitative intersection relationship between the single parameter determined in S1 and gas reservoir production through intersection statistics method, and analyze the influence of the single parameter on gas reservoir production; For text parameters, the distribution of gas reservoir production under different micro-structure types is statistically analyzed to analyze their relationship with gas reservoir production. S22. On the single parameter plane diagram, combined with the analysis conclusions of S21, analyze the scope of each parameter on the plane and its influence on the gas reservoir production. By comparing the parameter values ​​and gas reservoir production in different areas, clarify the importance of the single parameter on the gas reservoir production. S23. Based on the analysis results of S21 and S22, combined with the importance of the impact of single parameters on gas reservoir production, as well as actual geological conditions and exploration and development experience, analyze and determine the reference threshold of each single parameter when evaluating favorable gas reservoir areas. The threshold serves as the main basis for subsequent single factor assignment interval analysis.

4. The method according to claim 3, characterized in that Analysis of the relationship between single parameters and gas reservoir production, including: S211 table creation and data organization: Create tables and calculate key geological and engineering parameters of the target layer for each well, including: natural gas test production, thickness of main source rocks, gas generation intensity, type of microstructure, fault fracture density, reservoir porosity, lithologic trap area, mudstone barrier thickness, and regional caprock thickness; S212, analyzing data parameters and creating an intersection diagram; S213. For different micro-structure types encountered by the well, the gas reservoir production corresponding to each structure type is counted and its relationship with the gas reservoir production is analyzed.

5. The method according to claim 4, characterized in that Data parameter analysis and crossplot creation include: S2121 Establish a cross-plot of the thickness of the main source rocks and natural gas production, and analyze the impact of source rock thickness on natural gas production; S2122 establishes a cross-plot of gas generation intensity and natural gas production to analyze the relationship between gas generation intensity and production; S2123 Establish a cross-plot of reservoir porosity and natural gas production to study the contribution of porosity to production; S2124 establishes a cross-plot of fault fracture density and natural gas production to analyze the impact of fault fracture development on natural gas migration and accumulation; S2125 establishes a cross-plot of mudstone barrier thickness and natural gas production to analyze the impact of mudstone barrier thickness below the target layer on the vertical migration of natural gas and its impact on the production of the target layer gas reservoir; S2126 Establish a cross-plot of regional caprock thickness and natural gas production to evaluate the impact of caprock thickness on gas reservoir preservation and production; S2127 establishes an intersection diagram between the area of ​​lithologic bodies encountered by wells and gas reservoir production, and analyzes the requirements for the area of ​​lithologic bodies for the formation of large-scale gas reservoirs.

6. The method according to claim 3, wherein Single parameter floor plan analysis, including: S221, Planar analysis of thickness of main source rocks: Based on the quantitative relationship between the thickness of the main source rocks and the gas reservoir production, the thickness distribution characteristics of the main source rocks are analyzed on the main source rock thickness plane map, and the plane distribution range of the favorable area for natural gas accumulation is delineated; S222, Anger Intensity Plane Analysis: Based on the quantitative relationship between the gas generation intensity of the main source rocks and the gas reservoir production, the gas generation intensity of the main source rocks is analyzed in depth on the gas generation intensity plane map, and the plane distribution range of the favorable area for natural gas accumulation is determined; S223, Fault crack density plane diagram analysis: Based on the quantitative relationship between fault fracture density and gas reservoir production, the development and distribution characteristics of fault fractures were studied in depth on the fault fracture plane distribution map, and the distribution range of fault fractures that are conducive to natural gas migration was analyzed. S224, micro-structure type plan view analysis: Based on the conclusions drawn from the statistical analysis of the relationship between microstructure types and gas reservoir production, the development characteristics of microstructures were analyzed in depth on the microstructure plane map, and the distribution range of microstructure development areas that are conducive to natural gas accumulation was determined. S225, Reservoir porosity planar diagram analysis: Based on the quantitative relationship between tight sandstone reservoir porosity and gas reservoir production, a detailed analysis of reservoir porosity distribution was conducted on the reservoir porosity plane map, and the plane distribution range of the porosity favorable area for natural gas accumulation was delineated. S226. Lithologic trap area plan analysis: Based on the relationship between the area of ​​lithologic bodies encountered by wells and gas reservoir production, the predicted lithologic body distribution map is combined with actual drilling data to depict the distribution of lithologic bodies that meet a certain area scale and are conducive to natural gas accumulation, and the lithologic body area is calculated; S227, mudstone interlayer thickness planar diagram analysis: Based on the quantitative relationship between mudstone interlayer thickness and gas reservoir production, the distribution of mudstone interlayer thickness is analyzed on the mudstone interlayer thickness plane map, and the plane distribution range of the favorable area for natural gas migration is determined; S228, Regional cover thickness plan analysis: Based on the quantitative relationship conclusion determined by the intersection analysis of regional cap rock thickness and gas reservoir production, the thickness distribution of the regional cap rock was carefully studied on the regional cap rock thickness plane map, and the plane distribution range of the favorable area for natural gas accumulation was delineated.

7. The method according to claim 6, characterized in that S222 Gas Intensity Plane Analysis, including: S2221, mapping step: Based on the collected data on the gas generation intensity of the main source rocks, use the Shuanghu geological mapping software to compile a gas generation intensity contour map; S2222. Judgment step: On the gas generation intensity plane map, analyze the changing trends and patterns of gas generation intensity. Based on the quantitative relationship between gas generation intensity and gas reservoir production and the gas test results of the well drilling encountering the gas reservoir, analyze and delineate the gas generation intensity region that is conducive to high gas reservoir production. S223 fault fracture density planar analysis, including: S2231, Fault and Fracture Prediction Step: Use structural interpretation software to interpret the post-stack seismic volume in detail to depict the fault distribution, combine it with the fracture prediction software to extract ant body attributes from the post-stack seismic volume, and superimpose the two to obtain a fault and fracture plane distribution map; S2232, judgment step: On the fault and fracture distribution map, analyze the development and distribution characteristics of faults and fractures. Based on the quantitative relationship between fault fractures and gas reservoir production, combined with the gas reservoir test results of the well drilling, calculate the density of fault fractures developed per square kilometer, and analyze the fault and fracture areas that are conducive to high gas reservoir production. S224 micro-structure type plan view analysis, including: S2241, mapping steps: Use structural interpretation software to conduct detailed tracking and interpretation of the main layer on the post-stack seismic volume, and obtain depth-domain structural data through time-depth conversion. Then, use Shuanghu geological mapping software to compile a micro-structural map of the top surface of the main layer; S2242, judgment step: On the microstructure map, analyze and describe the development and distribution characteristics of favorable microstructure types. Based on the statistical relationship between microstructure types and gas reservoir production, combined with the gas reservoir test results of the well drilling, analyze and determine the area and range of microstructure types that are conducive to high gas reservoir production. S225 reservoir porosity plan analysis, including: S2251, inversion steps: invert and predict the porosity of the main layer on the post-stack seismic volume using reservoir inversion software, and compile a porosity contour map of the main layer reservoir using Shuanghu geological mapping software. S2252, judgment steps: On the predicted porosity plane map, analyze the variation pattern of reservoir porosity, and based on the statistical relationship between reservoir porosity and gas reservoir production, combined with the gas test results of the well drilling encountering the gas reservoir, analyze and delineate the sweet spot porosity area that is conducive to high gas reservoir production. S226 lithologic trap area plan analysis, including: S2261, inversion step: invert and predict the thickness of the main layer sand body on the post-stack seismic volume using reservoir inversion software, and compile the main layer reservoir thickness contour map using Shuanghu geological mapping software; S2262, judgment step: On the predicted reservoir thickness plan, analyze the reservoir thickness variation trend and regularity. Based on the lithologic gas reservoir analysis approach, depict the distribution range of the lithologic bodies on the plane. Combined with the porosity range conducive to high gas reservoir production identified in S352, and based on the gas test results of the well drilling encounter, ultimately determine and identify a lithologic body area of ​​a certain size that is conducive to high gas reservoir production. S227 mudstone interlayer thickness planar analysis, including: S2271, mapping steps: Based on the thickness of the mudstone interlayer encountered at the well point, use Shuanghu geological mapping software to compile a mudstone interlayer thickness contour map; S2272, judgment step: On the mudstone interlayer thickness plane diagram, analyze the changing trend and regularity of the mudstone interlayer thickness. Based on the quantitative relationship between mudstone interlayer thickness and reservoir production, combined with the gas reservoir test results encountered by the well drilling, analyze and determine the area of ​​mudstone interlayer thickness that is conducive to high gas reservoir production; Analysis of the cover thickness plan in the S228 area, including: S2281, mapping step: Based on the caprock thickness in the area encountered by the well point, use Shuanghu geological mapping software to compile a regional caprock thickness contour map; S2282, judgment steps: On the regional cap rock thickness plan, analyze the trend and law of regional cap rock thickness changes, and according to the quantitative relationship between regional cap rock thickness and reservoir production, combined with the gas test results of the well drilling encountering the gas reservoir, analyze and determine the regional range of cap rock thickness that is conducive to high-yield gas reservoirs.

8. The method according to claim 3, wherein S23 single parameter evaluation threshold setting, including: S231, thickness threshold of main source rock: By deeply analyzing the quantitative relationship between the thickness of the main source rock and gas reservoir production, and combining the specific scope of this relationship on the plane diagram of the main source rock thickness, the lower limit of the main source rock thickness that is conducive to the formation of tight sandstone gas accumulation is reasonably determined; S232, anger intensity threshold: Based on the quantitative relationship between gas generation intensity and gas reservoir production, and the range of expression of this relationship on the gas generation intensity plane diagram, the lower limit of gas generation intensity that is conducive to the formation of tight sandstone gas accumulation can be reasonably determined; S233, fault fracture threshold: Based on the quantitative relationship between fault fracture density and gas reservoir production, as well as the development and distribution characteristics of fault fractures that are conducive to natural gas migration on the plane, the evaluation threshold of fault fractures is determined by statistically analyzing the development density of fault fractures per square kilometer; S234, microstructure type threshold: By combining the relationship between microstructural types and gas reservoir production, and the development of microstructural types favorable for tight sandstone gas accumulation on microstructural maps, we can identify microstructural types favorable for gas reservoir formation, such as fault noses, anticlines, nose uplifts, and slopes. This standard will help identify favorable structural locations. S235, lithologic trap area threshold: By analyzing the relationship between the area of ​​lithologic traps encountered by wells and gas reservoir production, as well as their impact on reservoir size, and based on the area and distribution of lithologic bodies depicted through a combined map of predicted sand body thickness and predicted reservoir porosity, we can rationally determine the lower limit of lithologic trap area conducive to large-scale gas accumulation. This criterion will be used to assess the size and potential of lithologic traps. S236, reservoir sweet spot porosity threshold: Based on the quantitative relationship between reservoir sweet spot porosity and gas reservoir production, and the range of this relationship in predicting the reservoir porosity plane, the lower limit of reservoir sweet spot porosity that is conducive to the formation of tight sandstone gas accumulation can be reasonably determined; S237, mudstone barrier thickness threshold: By analyzing the quantitative relationship between mudstone interlayer thickness and gas reservoir production, as well as the range of this relationship on the mudstone interlayer thickness plane diagram, the lower limit of mudstone interlayer thickness that is conducive to tight sandstone gas accumulation can be reasonably determined. S238, regional cover thickness threshold: Based on the quantitative relationship between regional cap rock thickness and gas reservoir production, as well as the range of this relationship on the regional cap rock thickness plane diagram, the lower limit of regional cap rock thickness that is conducive to tight sandstone gas accumulation can be reasonably determined.

9. The method according to claim 1, wherein S3 single parameter hierarchical division and weight assignment, including: S31, single parameter level division: According to the importance of the single parameter on gas reservoir production, the single parameter is divided into three levels: primary control parameter, secondary control parameter and reference parameter according to the analytic hierarchy process; S32, single parameter level weight assignment: take the difference between the maximum and minimum values ​​of the single parameter evaluation factor assignment interval as the single parameter weight, where the main control parameter weight ≥ 0.7; 0.3 < secondary control parameter weight < 0.7; reference parameter weight ≤ 0.

3.

10. The method according to claim 1, wherein S4 performs interval assignment on single parameter evaluation factors, including: S41, evaluation factor of thickness parameter of main source rock: Based on the significant degree of influence of the thickness of the main source rock on gas reservoir production, its relative importance in the division of favorable gas reservoir areas is comprehensively evaluated. Within the range of 0-1, a reasonable interval value is assigned to this parameter. The size and span of the assigned value reflect its importance and serve as an evaluation factor for the parameter in subsequent favorable gas reservoir area evaluation. S42, anger intensity parameter evaluation factor: Based on the significance of the impact of gas generation intensity on gas reservoir production, its relative importance in the division of favorable gas reservoir areas is comprehensively evaluated. A reasonable interval value is assigned to this parameter within the range of 0-1. The size and span of the assigned value reflect its importance and serve as an evaluation factor for the gas generation intensity parameter. S43, fault fracture parameter evaluation factor: Based on the role of fault fracture density in the natural gas transport and accumulation process, its impact on the favorable zone division of gas reservoirs is evaluated. A reasonable interval value is assigned to this parameter within the range of 0-1. The size and span of the assigned value reflect its importance and serve as an evaluation factor for the fault fracture parameter. S44, microstructure type parameter evaluation factor: Considering the potential impact of micro-structural types on gas reservoir production, its contribution to the division of favorable gas reservoir areas is determined. A reasonable interval value is assigned to this parameter within the range of 0-1. The size and span of the assigned value reflect the difference in the impact of different micro-structural types on gas reservoirs and serve as an evaluation factor for the micro-structural type parameter. S45, lithologic trap area parameter evaluation factor: Based on the control effect of lithologic body area on gas reservoir scale, its importance in the demarcation of favorable gas reservoir areas is evaluated. A reasonable interval value is assigned to this parameter within the range of 0-1. The size and span of the assigned value reflect the degree of influence of lithologic trap area on gas reservoir scale and serve as an evaluation factor for the lithologic trap area parameter. S46, reservoir sweet spot porosity parameter evaluation factor: Based on the direct impact of sweet spot porosity on gas reservoir production, its criticality in the delineation of favorable gas reservoir zones is determined. Reasonable interval values ​​are assigned to this parameter within the range of 0-1. The size and span of the assigned values ​​reflect the contribution of sweet spot porosity to the delineation of favorable gas reservoir zones and serve as an evaluation factor for the sweet spot porosity parameter. S47, mudstone interlayer thickness parameter evaluation factor: According to the influence of mudstone interlayer thickness on gas reservoir production, its role in the division of gas reservoir favorable areas is evaluated. In the range of 0-1, appropriate interval values ​​are assigned to this parameter. The size and speed of the assigned values ​​reflect the influence of mudstone interlayer thickness on gas reservoir production and serve as the evaluation factor of mudstone interlayer thickness parameter. S48, regional cover thickness parameter evaluation factor: Based on the impact of regional cap rock thickness on gas reservoir production, its importance in the division of favorable gas reservoir areas is judged. Appropriate interval values ​​are assigned to this parameter within the range of 0-1. The size and span of the assigned values ​​reflect the contribution of regional cap rock thickness to gas reservoir protection and production, and serve as an evaluation factor for the regional cap rock thickness parameter.

11. The method according to claim 1, wherein S5 single parameter evaluation factor assignment function, including: S51, main source rock thickness evaluation factor assignment function: The thickness of the main source rock is used as the independent variable, and the main source rock thickness evaluation factor is used as the dependent variable. The domain is defined as the value of the main source rock thickness evaluation threshold to the maximum value of the main source rock thickness plane, and the range is the main source rock thickness evaluation factor assignment interval. On this basis, the main source rock thickness evaluation factor assignment function is established to convert the main source rock thickness into the corresponding evaluation factor. S52, anger intensity evaluation factor assignment function: The anger intensity parameter is used as the independent variable, and the anger intensity evaluation factor is used as the dependent variable. The domain is the interval from the minimum to the maximum value of the anger intensity plane. The anger intensity threshold is included in this interval, and the range is the anger intensity evaluation factor assignment interval. Based on this relationship, a anger intensity evaluation factor assignment function is established to convert anger intensity into an evaluation factor. S53, fault crack evaluation factor assignment function: The fault fracture evaluation factor assignment function uses the fault fracture density per square kilometer divided by 10 for standardization. This is used as the independent variable, and the result is directly used as the fault fracture evaluation factor assignment function. This function aims to intuitively reflect the degree of fracture development of the lithologic body through the fault fracture density. S54, microstructure type evaluation factor assignment function: Within the micro-structure type evaluation factor assignment range, different interval values ​​are assigned as evaluation factors according to the differences in the degree of influence of different micro-structure types on gas reservoir production; S55, lithologic trap area evaluation factor assignment function: The lithologic trap area parameter is used as the independent variable, and the lithologic trap area evaluation factor is used as the dependent variable. The domain is from the value of the lithologic trap area evaluation threshold to the maximum value of the planar lithologic trap area. The evaluated lithologic body area is greater than the lithologic trap area threshold. The range is the lithologic trap area evaluation factor assignment interval. Based on this, the lithologic trap area evaluation factor assignment function is established to evaluate the size of the lithologic trap. S56, reservoir sweet spot porosity evaluation factor assignment function: The reservoir sweet spot porosity parameter is used as the independent variable, the reservoir sweet spot porosity evaluation factor is used as the dependent variable, and the domain is from the value of the reservoir sweet spot porosity evaluation threshold to the maximum value of the plane predicted reservoir porosity. The porosity of the evaluated lithologic bodies is greater than the sweet spot porosity threshold, and the range is the reservoir sweet spot porosity evaluation factor assignment interval. Based on this relationship, a reservoir sweet spot porosity evaluation factor assignment function is established to evaluate the reservoir performance. S57, mudstone interlayer thickness evaluation factor assignment function: The mudstone interlayer thickness parameter is used as the independent variable, the mudstone interlayer thickness evaluation factor is used as the dependent variable, the domain is the interval from the minimum to the maximum value of the mudstone interlayer thickness plane, the mudstone interlayer thickness threshold is included in this interval, and the range is the mudstone interlayer thickness evaluation factor assignment interval. Based on this, a mudstone interlayer thickness evaluation factor assignment function is established to evaluate the mudstone interlayer's ability to block the upward migration of natural gas. S58, regional cover thickness evaluation factor assignment function: The regional cover thickness parameter is taken as the independent variable, the regional cover thickness evaluation factor is taken as the dependent variable, the domain is the interval from the minimum to the maximum value of the regional cover thickness plane, the regional cover thickness threshold is included in this interval, and the range is the regional cover thickness evaluation factor assignment interval. Based on this relationship, a regional cover thickness evaluation factor assignment function is established to evaluate the sealing ability and protection performance of the regional cover.

12. The method according to claim 11, characterized in that Assignment function formula fitting, including: S591, when the single parameter threshold is the boundary of the domain interval: S5911, definition module: the single parameter is the independent variable x, the single parameter evaluation factor is the dependent variable y, the definition domain is the interval [a1, a2] between the value a1 of the single parameter evaluation threshold and the single parameter plane maximum value a2, and the range is the evaluation factor assignment interval [b1, b2]; S5912, discrimination module: x=a1, y=b1; x=a2, y=b2; S5913, assignment function: Based on the two sets of data (a1, b1) and (a2, b2), establish a "parameter-factor" assignment function fitting formula; S592, when a single parameter threshold is contained within the domain interval, it is not the domain boundary: S5921, definition module: the single parameter is the independent variable x, the single parameter evaluation factor is the dependent variable y, the definition domain is the interval [a1, a2] between the minimum value a1 and the maximum value a2 of the single parameter plane, and the range is the evaluation factor assignment interval [b1, b2]; S5922, discrimination module: x=a1, y=b1; x=a2, y=b2; S5923, assignment function: Based on the two sets of data (a1, b1) and (a2, b2), establish the "parameter-factor" assignment function fitting formula.

13. The method according to claim 1, wherein S6 lithologic body single parameter evaluation factor calculation, including: S61, calculation of the evaluation factor of the thickness of the main source rock of the lithologic body: First, the planar boundaries of the lithologic bodies predicted to be favorable for large-scale hydrocarbon accumulation are accurately superimposed on the plane map of the main source rock thickness to ensure the spatial consistency of the two. Then, the thickness of the main source rock within each lithologic body is precisely measured, and the average value is calculated to reflect the overall thickness characteristics of the source rock within that lithologic body. Finally, based on the pre-set main source rock thickness evaluation factor assignment function, the calculated average thickness is converted into a corresponding evaluation factor for subsequent comprehensive evaluation. S62, calculation of evaluation factor of gas generation intensity of lithologic body: First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the gas generation intensity plane map to ensure the accuracy of the evaluation range. Then, the gas generation intensity within each lithologic body is statistically analyzed and its average value is calculated to characterize the gas generation potential of the lithologic body. Finally, based on the gas generation intensity evaluation factor assignment function, the average gas generation intensity is converted into an evaluation factor, providing a basis for evaluating the gas generation performance of the lithologic body. S63, calculation of evaluation factors for fault fractures in lithologic bodies: First, the planar predicted boundaries of lithologic bodies favorable for large-scale reservoir formation are superimposed on the planar distribution map of fault fractures to ensure accurate correspondence between fault fracture information and lithologic body locations. Then, the fault fracture density per square kilometer within each lithologic body is statistically calculated to reflect the degree of fracture development within that lithologic body. Finally, based on the fault fracture evaluation factor assignment function, the fault fracture density is converted into an evaluation factor for use in evaluating the fracture and fracture characteristics of the lithologic body. S64, calculation of the evaluation factor of the microstructural type of the lithologic body: First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on a microstructural plane map to ensure accurate correspondence between microstructural information and lithologic body locations. Next, a detailed statistical analysis of the microstructural types developed within each lithologic body is performed to reflect the microstructural characteristics of that lithologic body. Finally, based on a microstructural type evaluation factor assignment function, the statistically obtained microstructural types are converted into evaluation factors for use in evaluating the structural favorableness of the lithologic body. S65, calculation of lithologic body trap area evaluation factor: First, on the lithologic body distribution map, the area of ​​each lithologic body that is conducive to large-scale reservoir formation is accurately counted to ensure the accuracy of the area data. Then, based on the lithologic trap area evaluation factor assignment function, the statistically obtained trap area is converted into an evaluation factor to evaluate the scale and potential of the lithologic body reservoir formation. S66, calculation of porosity evaluation factor of lithologic body sweet spot: First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on the plane map of predicted reservoir porosity to ensure accurate correspondence between porosity information and lithologic body locations. Then, the porosity within each lithologic body is precisely measured, and its average value is calculated to reflect the reservoir properties of that lithologic body. Finally, based on the reservoir sweet spot porosity evaluation factor assignment function, the average porosity is converted into an evaluation factor for evaluating the reservoir performance of the lithologic body. S67, calculation of evaluation factor for mudstone interlayer thickness in lithologic bodies: First, the boundaries of lithologic bodies predicted to be favorable for large-scale reservoir formation are superimposed on a planar map of mudstone interlayer thickness to ensure that the interlayer thickness information accurately corresponds to the lithologic body location. Then, the mudstone interlayer thickness within each lithologic body is precisely measured, and its average value is calculated to reflect the interlayer development encountered during natural gas migration into that lithologic body. Finally, based on the mudstone interlayer thickness evaluation factor assignment function, the average interlayer thickness is converted into an evaluation factor, which is used to evaluate the interlayer sealing performance of the lithologic body. S68, calculation of evaluation factor of caprock thickness in lithologic body area: First, the planar predicted boundaries of lithologic bodies that are conducive to large-scale reservoir formation are superimposed on the regional cap rock thickness plan map to ensure that the cap rock thickness information accurately corresponds to the lithologic body location. Then, the regional cap rock thickness within each lithologic body is accurately measured, and its average value is calculated to reflect the cap rock development of that lithologic body. Finally, based on the regional cap rock thickness evaluation factor assignment function, the average cap rock thickness is converted into an evaluation factor for evaluating the cap rock sealing ability and protective performance of the lithologic body.

14. The method according to claim 1, wherein S7 multi-parameter quantitative evaluation comprehensive factor determination, including: S71, Summarize and create a table: First, systematically summarize and organize the single-parameter evaluation factors of all lithologic bodies to ensure data integrity and accuracy; then, create a table containing all lithologic bodies and their corresponding single-parameter evaluation factors for subsequent calculations and analysis; S72, single parameter level weight setting: according to the three levels of primary control parameters, secondary control parameters, and reference parameters, set the single parameter weight. The weight is obtained by the difference between the maximum and minimum values ​​of the single parameter evaluation factor assignment interval. By quantifying the value range of the single parameter, its weight in the comprehensive evaluation is reasonably allocated; S73, calculating comprehensive factor 1: performing a weighted summation of all single parameter evaluation factors of each lithologic body according to the single parameter evaluation factors of each lithologic body and their corresponding weights, and linearly scaling the sum to the range of 0-1 as the multi-parameter quantitative evaluation comprehensive factor 1; S74, calculating comprehensive factor 2: based on the single parameter evaluation factors and their weights, calculating the weighted root mean square of all single parameter evaluation factors for each lithologic body, and linearly scaling them to the range of 0-1, as the multi-parameter quantitative evaluation comprehensive factor 2; S75, calculating comprehensive factor 3: based on the single parameter evaluation factors and their weights, simply sum all single parameter evaluation factors of each lithologic body and linearly scale them to the range of 0-1 to serve as the multi-parameter quantitative evaluation comprehensive factor 3; S76, calculate comprehensive factor 4: based on the single parameter evaluation factors and their weights, calculate the root mean square of all single parameter evaluation factors for each lithologic body, and linearly scale them to the range of 0-1 as the multi-parameter quantitative evaluation comprehensive factor 4.

15. The method according to claim 14, characterized in that The linear scale of the comprehensive factors to the interval of 0-1 includes: S77, take the minimum value of the comprehensive factor a1, and when the ratio reaches the range of 0-1, assign it a value of 0; S78, taking the maximum value of the comprehensive factor a2, when the ratio is in the range of 0-1, assigning a value of 1; S79. Establish a linear relationship based on the two sets of data (a1, 0) and (a2, 1): y = ax + c, where a and c are both constants, and when x = a1, y = 0; when x = a2, y = 1.

16. The method according to claim 1, wherein The S8 histogram obtains the reference standards for the classification of favorable area types, including: S81. Prepare a probability distribution histogram of comprehensive factor 1: First, perform statistical analysis on the calculated multi-parameter quantitative evaluation comprehensive factor 1 to determine its numerical range and distribution. Then, based on the normal distribution characteristics, or approximate normal distribution characteristics, of comprehensive factor 1, divide the numerical range into three main numerical intervals: large, medium, and small. These three intervals correspond to the first, second, and third favorable zone types, respectively, and serve as reference standards for classifying favorable zone types. S82, prepare a probability distribution histogram of comprehensive factor 2: perform statistical analysis on comprehensive factor 2 and draw its probability distribution histogram. According to the normal distribution characteristics of comprehensive factor 2, divide it into three value intervals: large, medium, and small, corresponding to the first, second, and third favorable area types respectively; S83, prepare a probability distribution histogram of comprehensive factor 3: perform statistical analysis on comprehensive factor 3 and draw its probability distribution histogram. Based on the normal distribution characteristics of comprehensive factor 3, divide it into three numerical intervals: large, medium, and small, which are used to classify favorable areas into Class I, Class II, and Class III. S84. Prepare the probability distribution histogram of comprehensive factor 4: Conduct statistical analysis on comprehensive factor 4 and draw its probability distribution histogram. According to the normal distribution characteristics of comprehensive factor 4, it is divided into three numerical intervals: large, medium and small, corresponding to the first, second and third types of favorable areas respectively.

17. The method according to claim 1, wherein S9 Favorable lithologic body types are classified as follows: S91: Based on the three favorable zone types (Class I, Class II, and Class III) classified by comprehensive factor 1, all lithologic bodies on the plane are analyzed one by one, and the favorable types are classified according to the value of comprehensive factor 1. The classification results of the lithologic bodies encountered by wells are compared and analyzed with the actual well production data to verify the accuracy and reliability of the classification. The setting range of the comprehensive factor distribution interval used for the favorable zone type classification is repeatedly iterated and revised to obtain the optimal setting range of the comprehensive factor interval suitable for the favorable zone type classification; S92: Based on the three favorable zone types defined by comprehensive factor 2, all lithologic bodies on the plane are classified into favorable types. The classification results of the lithologic bodies encountered by wells are compared and analyzed with the actual well production data. The most favorable lithologic body type is further verified and selected from the perspective of comprehensive factors of different algorithms. The setting range of the comprehensive factor distribution interval used for the classification of favorable zone types is repeatedly iterated and revised to obtain the optimal setting range of the comprehensive factor interval suitable for the classification of favorable zone types. S93, repeating the above classification, comparison analysis and iterative correction process according to the three favorable area types divided by comprehensive factor 3; S94: Based on the three favorable zone types defined by comprehensive factor 4, all lithologic bodies on the plane are reclassified into favorable types. The classification results of lithologic bodies encountered by wells are compared and analyzed in detail with the actual production data of the wells. The range of the comprehensive factor interval is repeatedly iterated and revised. S95, using the Monte Carlo simulation approach, comprehensively evaluates the impact of different comprehensive factor calculation methods on the classification of favorable lithologic body types. The accuracy, stability, and predictive ability of the comprehensive factors for favorable lithologic body classification under different algorithms are analyzed. The most suitable favorable lithologic body classification scheme is selected, and the optimal comprehensive factors are then determined, providing a basis for the subsequent division of favorable tight sandstone gas zones and exploration and development.

18. The method according to claim 1, wherein Also includes: S11. Comprehensive factors of lithologic bodies are used to rank and guide exploration: Based on the analysis results of steps S9 and S10, the comprehensive factors of the lithologic bodies not encountered by wells in the favorable area of ​​the target layer are ranked as a whole. According to the ranking results, the favorable lithologic bodies are selected as the main targets of the next step of tight sandstone gas exploration.

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