Lake facies shale gas sweet spot quantitative evaluation method

Abstract

The invention belongs to the technical field of exploration and development of petroleum and natural gas geological resources, and particularly discloses a lacustrine facies shale gas sweet spot quantitative evaluation method. According to the quantitative evaluation method for the lacustrine facies shale gas sweet spots, the AHP analytic hierarchy process is used for optimally selecting main sweet spot indexes, multiple models are established according to the main sweet spot indexes of lacustrine facies shale bed series in corresponding regions and actually measured gas content data, the evaluation effects of the multiple models are evaluated, and the evaluation model with the best effect is selected. The evaluation model shows better applicability, the data complexity of evaluation by using the model is obviously reduced, the rapid quantitative evaluation of the shale gas sweet spots in the research area can be realized, the working efficiency is greatly improved, the research cost is saved, and the exploration, development and deployment of the shale gas can be efficiently supported.

Description

Technical Field

[0001] This invention relates to the field of exploration and development technology of petroleum and natural gas geological resources, specifically to a method for quantitative evaluation of sweet spots in lacustrine shale gas. Background Technology

[0002] Compared with marine organic-rich shale, lacustrine mudstone and shale are characterized by rapid sedimentary facies transition, diverse lithofacies types (interbedded and interlayered), low organic matter abundance, strong reservoir heterogeneity, and complex source-reservoir configuration. These characteristics have added many difficulties and obstacles to the research on lacustrine shale.

[0003] The paper, "Analysis and Reflection on the Enrichment Mechanism of Marine and Lacustrine Shale Gas: A Case Study of the Longmaxi Formation and the Da'anzhai Section of the Ziliujing Formation in the Sichuan Basin," primarily conducts a systematic evaluation of shale gas based on six properties of shale reservoirs: lithology, physical properties, electrical properties, gas content, organic geochemical characteristics, and brittleness. It analyzes and discusses the basic conditions for formation, enrichment mechanisms, and potential stratigraphic intervals of marine and lacustrine shale gas. However, the evaluation method presented in this paper involves a large amount of basic data and parameters, and the key factors are not clearly defined, limiting it to general directional evaluation rather than quantitative evaluation.

[0004] Therefore, how to quantitatively evaluate the sweet spot of lacustrine shale gas has become an urgent problem to be solved in this field. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method for quantitative evaluation of sweet spots in lacustrine shale gas.

[0006] According to one aspect of the present invention, a method for quantitative evaluation of sweet spots in lacustrine shale gas is provided, comprising the following steps:

[0007] Obtain seismic, logging, well logging, and analytical data of lacustrine shale gas in various regions, and establish a shale gas sweet spot evaluation database based on the data collected.

[0008] Using lacustrine shale gas production as a direct representation of the sweet spot of lacustrine shale gas, the following five main indicators related to lacustrine shale gas production were screened and extracted: total organic carbon (TOC), vitrinite reflectance (R0), and so on. o Clay mineral content, pore specific surface area, and average pore radius;

[0009] The data of the five main indicators included in the shale gas sweet spot evaluation database were checked and cleaned to ensure that the data used was complete and accurate.

[0010] The AHP (Analytical Hierarchical Method) was used to calculate the influence weights of five major indicators on lacustrine shale gas production, and the major indicator with the highest weight was used as the independent variable to establish a quantitative evaluation model for lacustrine shale gas sweet spot.

[0011] For the selected region, linear regression models, power function models, and ExpAssoc models are constructed based on the measured data of the indicators corresponding to the independent variables and the measured gas content data.

[0012] Solve for the parameters in the three models and introduce the coefficient of determination R. 2 To evaluate the performance of the three types of models;

[0013] The model with the best evaluation performance was selected as the quantitative evaluation model for sweet spot in lacustrine shale gas.

[0014] Based on the obtained quantitative evaluation model of lacustrine shale gas sweet spots, a quantitative evaluation is carried out on the region to be evaluated to determine the lacustrine shale gas sweet spots in the region.

[0015] According to one embodiment of the present invention, the data inspection and cleaning of the five main indicators included in the shale gas sweet spot evaluation database includes checking for abnormal or missing data. If abnormal or missing data is found, data processing is performed on the abnormal or missing data.

[0016] According to one embodiment of the present invention, the data processing includes directly deleting abnormal data caused by obvious errors or irrelevant noise; and for missing data, finding an object in the complete data that is most similar to the missing data object and filling it with the value of this similar object.

[0017] According to an embodiment of the present invention, the calculation of the influence weights of five major indicators on lacustrine shale gas production using the AHP (Analytic Hierarchy Process) includes the following steps:

[0018] Construct the judgment matrix;

[0019] Calculate the original weights based on the judgment matrix;

[0020] Then, standardize the original weights to obtain the weight vector;

[0021] Consistency is verified by calculating the largest eigenvalue of the judgment matrix to prove its rationality;

[0022] If the judgment matrix is ​​determined to be reasonable, then the main indicator corresponding to the maximum value in the weight vector will be used as the independent variable for establishing a quantitative evaluation model of lacustrine shale gas sweet spot.

[0023] According to one embodiment of the present invention, the judgment matrix is ​​constructed using a 9-point scaling method.

[0024] According to one embodiment of the present invention, the original weights are calculated based on the judgment matrix using the square root method.

[0025] According to one embodiment of the present invention, the original weight is calculated using the following formula:

[0026]

[0027] Where i represents the row number of the judgment matrix, k represents the column number of the judgment matrix, and w i a represents the original weight of the i-th row of the judgment matrix. ik This represents the element in the i-th row and k-th column of the judgment matrix.

[0028] According to one embodiment of the present invention, the standardization adopts the following calculation formula:

[0029]

[0030] Among them, w i This represents the original weight of the i-th row of the judgment matrix. This represents the standardized weight of the i-th row.

[0031] According to one embodiment of the present invention, the formula for calculating the largest eigenvalue of the matrix is ​​as follows:

[0032]

[0033] Where, λ max A represents the largest eigenvalue, A represents the judgment matrix, and W represents the weight vector.

[0034] According to one embodiment of the present invention, a consistency index is calculated based on the maximum eigenvalue, and a consistency ratio is calculated based on the consistency index and the random consistency index. When the consistency ratio is less than 0.1, the judgment matrix is ​​determined to be reasonable.

[0035] According to one embodiment of the present invention, the formula for calculating the consistency index is as follows:

[0036]

[0037] Where CI represents the consistency index, and n represents the order of the judgment matrix.

[0038] According to one embodiment of the present invention, the formula for calculating the consistency ratio is as follows:

[0039]

[0040] Wherein, CR represents the consistency ratio and RI represents the random consistency index.

[0041] According to one embodiment of the present invention, the linear regression model adopts the following formula:

[0042] Q=a1+b1T Formula (6)

[0043] Where Q represents the measured gas content in scc / g, T represents the main indicator with the highest weight, and a1 and b1 are the constants of the linear regression model.

[0044] According to an embodiment of the present invention, the power function model adopts the following formula:

[0045]

[0046] Where Q represents the measured gas content in scc / g, T represents the main indicator with the highest weight, and a2 and b2 are power function model constants.

[0047] According to an embodiment of the present invention, the ExpAssoc model adopts the following formula:

[0048]

[0049] Where Q represents the measured gas content in scc / g, T represents the main index with the highest weight, and q0, a3, b3, a4, and b4 are constants of the ExpAssoc model.

[0050] According to one embodiment of the present invention, a determination coefficient R is introduced. 2 To evaluate the performance of the three types of models, we need to compare the R-values ​​of the three types of models. 2 Value, in R 2 The model with the largest value is used as a quantitative evaluation model for sweet spots in lacustrine shale gas.

[0051] In the technical solution of this invention, the main sweet spot index is selected by using the AHP (Analytic Hierarchy Process) method. An optimization model is established based on the main sweet spot index of the lacustrine shale strata in the corresponding area and the measured gas content data. This evaluation model shows good applicability and significantly reduces data complexity. It can realize rapid quantitative evaluation of the sweet spot of lacustrine shale gas in the study area, greatly improve work efficiency, save research costs, and efficiently support the deployment of lacustrine shale gas exploration and development. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating the overall process of the quantitative evaluation method for lacustrine shale gas sweet spots according to the present invention.

[0054] Figure 2 This is a diagram showing the relationship between an evaluation model and data points for lacustrine shale layers in a certain region. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific examples.

[0056] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two entities or parameters with the same name but different names. It is clear that "first" and "second" are only for the convenience of expression and should not be construed as limiting the embodiments of the present invention. Subsequent embodiments will not explain this in detail.

[0057] It should be understood that the embodiments of the invention shown in the exemplary embodiments are merely illustrative. Although only a few embodiments have been described in detail in this invention, those skilled in the art will readily recognize that various modifications are possible without substantially departing from the teachings of the invention. Accordingly, all such modifications should be included within the scope of the invention. Other substitutions, modifications, variations, and deletions can be made to the design, operating conditions, and parameters of the following exemplary embodiments without departing from the spirit of the invention.

[0058] Shale oil and gas exploration and development are costly. Shale is highly heterogeneous, and even rocks with the same color can vary greatly in organic matter content and mineral composition. In addition, my country has experienced multiple tectonic movements, resulting in complex geological and surface conditions. Therefore, the selection of the core area and the identification of favorable strata—the "sweet spots"—are crucial for finding the most favorable areas and the richest strata for shale oil and gas in the early stages of exploration, and for achieving large-scale economic development.

[0059] Shale reservoir heterogeneity can manifest as vertical variations in rock composition, organic matter distribution, porosity and permeability characteristics, and gas storage capacity, as well as horizontal differences in the distribution of organic-rich shale. Shale gas "sweet spots" refer to the optimal areas or strata for shale gas exploration and development. These are characterized by large shale thickness and scale, high organic matter content, location within a "gas window," high gas content, strong fracturing ability, and favorable surface conditions.

[0060] The purpose of shale reservoir sweet spot prediction is to identify the most valuable target drilling areas. This usually requires a comprehensive evaluation of multiple reservoir characteristics. These reservoir characteristics depend to varying degrees on different types of data and information. Integrating multi-source information to achieve a comprehensive evaluation of the sweet spot is the key to shale reservoir prediction.

[0061] Shale gas sweet spots can be divided into geological sweet spots and engineering sweet spots. Geological sweet spots refer to areas with high levels of free and adsorbed gas and favorable physical properties. Engineering sweet spots refer to areas conducive to low-cost, high-efficiency fracturing operations. In this invention, the shale gas sweet spot refers to a geological sweet spot. Geological sweet spot parameters include numerous parameters such as clay content, silica content, carbonaceous content, total organic carbon content, kerogen content, thermal maturity, total porosity, gas-bearing porosity, gas saturation, free gas content, adsorbed gas content, matrix porosity, fracture porosity, pore structure, permeability, pore pressure, and the thickness of high-quality reservoirs.

[0062] The purpose of this invention is to provide a quantitative evaluation method for sweet spots in lacustrine shale gas, providing support for the exploration and development of lacustrine shale gas.

[0063] like Figure 1 As shown, the method generally includes the following steps:

[0064] Step S1: Obtain seismic, logging, well logging and analytical data of lacustrine shale gas in various regions, and establish a shale gas sweet spot evaluation database based on the collected data;

[0065] Step S2: Using lacustrine shale gas production as a direct representation of the sweet spot of lacustrine shale gas, the following five main indicators related to lacustrine shale gas production were screened and extracted: total organic carbon (TOC), vitrinite reflectance (R0), and so on. o Clay mineral content, pore specific surface area, and average pore radius;

[0066] Step S3: Check and clean the data of the five main indicators included in the shale gas sweet spot evaluation database to ensure that the data used is complete and accurate;

[0067] Step S4: Use the AHP (Analytical Hierarchy Process) to calculate the influence weights of the five major indicators on lacustrine shale gas production, and use the major indicator with the highest weight as the independent variable to establish a quantitative evaluation model for lacustrine shale gas sweet spot.

[0068] Step S5: For the selected region, construct linear regression models, power function models, and ExpAssoc models based on the measured data of the indicators corresponding to the independent variables and the measured gas content data;

[0069] Step S6: Solve for the parameters in the three models and introduce the coefficient of determination R. 2 To evaluate the performance of the three types of models;

[0070] Step S7: Select the model with the best evaluation effect as the quantitative evaluation model for lacustrine shale gas sweet spot;

[0071] Step S8: Based on the obtained quantitative evaluation model of lacustrine shale gas sweet spots, perform a quantitative evaluation of the area to be evaluated to determine the lacustrine shale gas sweet spots in the area.

[0072] The steps in the above method are described in detail below by way of example.

[0073] In step S1, seismic, logging, well logging, and analytical data of lacustrine shale gas in various regions are acquired, and a shale gas sweet spot evaluation database is established based on the collected data. Seismic data includes, but is not limited to, results of conventional post-stack or pre-stack migration processing, velocity spectrum data, structural interpretation horizons, and fault data. Logging data includes, but is not limited to, information on formation rock type, porosity, permeability, and hydrocarbon content. Well logging data includes, but is not limited to, P-wave transit time, S-wave transit time, density logging, total organic carbon (TOC) content, and gas content interpretation curves. Analytical data includes, but is not limited to, core test data of mudstone and shale. When establishing the database, separate databases corresponding to different regions can be created.

[0074] In step S2, lacustrine shale gas production is used as a direct representation of the sweet spot of lacustrine shale gas. The following five main indicators related to lacustrine shale gas production are screened and extracted: total organic carbon (TOC), vitrinite reflectance (R0), and so on. o Clay mineral content, pore specific surface area, and average pore radius are all important indicators. The ultimate goal of evaluating shale gas sweet spots is to achieve high shale gas production; therefore, lacustrine shale gas production can be considered the most direct indicator of lacustrine shale gas sweet spots. Total organic carbon (TOC) content is generally positively correlated with gas content. Organic matter serves as both a gas adsorption medium and can form numerous organic micropores, becoming a storage space for free gas. Vitrinite reflectance R... o It is an important indicator of the maturity of source rocks. Generally, the evolution of organic matter can be divided into three stages according to the change in vitrinite reflectance. When R... o When the organic matter content is ≤0.5%, it is in an immature stage; 0.5% <R o When the organic matter content is less than 1.6%, the organic matter is in a mature stage, which is conducive to the formation of oil and gas. Among them, R... o When R is 0.5%–0.8%, it enters the early stage of maturity; when R… o When R is between 0.8% and 1.2%, it is in the middle stage of maturity; o When R is between 1.2% and 1.6%, it is in the late stage of maturity. However, when R... oAt a concentration ≥1.6%, the formation of oil and gas ceases. Clay minerals are the most important interstitial and cementing materials in sandstone reservoirs of oil and gas basins, and their formation and distribution are controlled by both sedimentation and diagenesis. The original sedimentary environment determines the assemblage of primary clay minerals; for example, kaolinite is more developed in acidic environments, illite in alkaline environments, and chlorite in iron-magnesium-rich environments. Diagenesis also affects the development of clay minerals; for example, the dissolution of feldspar can produce authigenic kaolinite. Under certain temperature, pressure, and water-based conditions, clay minerals can also undergo interconversion. The main mineral grains in sandstone reservoirs are quartz and feldspar, with chlorite, illite, kaolinite, and clay minerals such as montmorillonite and illite-montmorillonite mixed-layers developing on the grain surface. These minerals exist on the sedimentary rock surface in two forms: clastic and authigenic. The former mainly comes from the supply of sedimentary material, while the latter mostly comes from the dissolution of reservoir minerals or the transformation between clay minerals. Only with a clear understanding of the distribution patterns of clay minerals can we accurately and specifically analyze their impact on reservoir properties, pore structure, and reservoir productivity. This allows for the correct evaluation of the reservoir, the formulation of effective exploration and development measures, and the improvement of oil and gas well productivity. Pore specific surface area and average pore radius affect the adsorption and storage of shale gas.

[0075] In step S3, the data for the five main indicators included in the shale gas sweet spot evaluation database are checked and cleaned to ensure the completeness and accuracy of the data used. This involves checking and cleaning these five types of data in the shale gas sweet spot evaluation database to identify any abnormal or missing data. If any are found, they are processed to ensure the integrity and accuracy of the data used. Specifically, abnormal data with obvious errors or irrelevant noise is directly deleted; for missing data, the most similar object in the complete data is found and the missing data is filled with the value of this similar object.

[0076] In step S4, the Analytic Hierarchy Process (AHP) is used to calculate the influence weights of five main indicators on lacustrine shale gas production, and the main indicator with the highest weight is used as the independent variable in establishing a quantitative evaluation model for lacustrine shale gas sweet spots. The AHP method treats a multi-objective decision problem as a complex system composed of numerous factors, decomposing the elements related to the decision into multiple levels such as objectives, criteria, and alternatives, and then conducting qualitative and quantitative analysis based on this. Due to its strong systematicity, high flexibility, high efficiency, and convenience, this method is widely used in decision analysis.

[0077] Specifically, a judgment matrix is ​​first constructed. Combining the correlation between the five types of data and shale gas production in the database established in step S1, a pairwise comparison is performed and values ​​are assigned according to the 9-point scaling method. Based on the comparison results of each pair of indicators, the decision-maker assigns values ​​to each pair of indicators based on their experience, thereby determining the weight of each type of data (criteria layer) to shale gas production (target layer). Table 1 below shows the meaning of the 9-point scaling method:

[0078] Table 1. Meaning of the 9-point scale

[0079]

[0080] A judgment matrix is ​​constructed based on the weights of various data on shale gas production. Table 2 shows the judgment matrix obtained for a certain region D.

[0081] Table 2. Judgment Matrix Corresponding to Region D

[0082]

[0083]

[0084] Next, the original weights are calculated using the square root method based on the judgment matrix. The calculation formula is as follows:

[0085]

[0086] Where i represents the row number of the judgment matrix, k represents the column number of the judgment matrix, and w i ai represents the original weight of the i-th row of the judgment matrix. k This represents the element in the i-th row and k-th column of the judgment matrix.

[0087] The original weights are then standardized to obtain the weight vector, calculated as follows:

[0088]

[0089] Where wi represents the original weight of the i-th row of the judgment matrix. This represents the standardized weight of the i-th row.

[0090] The weight vector W obtained for region D is shown below:

[0091]

[0092] Consistency is verified by calculating the largest eigenvalue of the judgment matrix, given:

[0093] AW=λ max W

[0094] Transforming the above formula, the formula for calculating the largest eigenvalue of a matrix is ​​as follows:

[0095]

[0096] Where, λ max A represents the largest eigenvalue, A represents the judgment matrix, and W represents the weight vector.

[0097] The maximum eigenvalue λ of region D was calculated. max Approximately 5.30. Consistency verification was performed on this result to evaluate its reasonableness. Based on the largest eigenvalue λ max The consistency index CI is calculated, and the consistency ratio CR is calculated based on the consistency index CI and the random consistency index RI. When the consistency ratio is less than 0.1, the judgment matrix is ​​determined to be reasonable.

[0098] Specifically, the formula for calculating the consistency index (CI) is as follows:

[0099]

[0100] Where CI represents the consistency index, and n represents the order of the judgment matrix, the calculated consistency index CI for region D is 0.075. The random consistency index RI is obtained from a table, which shows the values ​​of the random consistency index in Table 3. Since the judgment matrix is ​​a 5th order matrix, the corresponding random consistency index RI is 1.12.

[0101] Table 3. Random Consistency Index Values

[0102] order 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.52 0.89 1.12 1.24 1.36 1.41 1.46 1.49

[0103] The formula for calculating the consistency ratio is as follows:

[0104]

[0105] Wherein, CR represents the consistency ratio and RI represents the random consistency index. Substituting the values ​​of CR and RI into formula (5), CR is calculated to be approximately 0.067. Since 0.067 < 0.1, this indicates that the consistency of the judgment matrix is ​​within the acceptable range, proving the rationality of the judgment matrix. Therefore, the main sweet spot index TOC corresponding to the row with the highest weight in the judgment matrix is ​​selected to establish a quantitative evaluation model for shale gas sweet spots.

[0106] In step S5, for the selected region, linear regression models, power function models, and ExpAssoc models are constructed based on the measured data of the indicators corresponding to the independent variables and the measured gas content data.

[0107] For region D, TOC (Total Gas Content) was used as the independent variable in establishing a quantitative evaluation model for shale gas sweet spots. TOC and measured gas content data were obtained from desorption experiments of lacustrine shale formations in region D, as shown in Table 4 below.

[0108] Table 4. Desorption Experimental Data of Lacustrine Shale Strata in Region D

[0109]

[0110]

[0111] Based on TOC and measured gas content data, linear regression models, power function models, and nonlinear least squares fitting models of ExpAssoc were constructed respectively.

[0112] The linear regression model uses the following formula:

[0113] Q=a1+b1T Formula (6)

[0114] Where Q represents the measured gas content in scc / g, T represents the main indicator with the highest weight, and a1 and b1 are the constants of the linear regression model.

[0115] The power function model uses the following formula:

[0116]

[0117] Where Q represents the measured gas content in scc / g, T represents the main indicator with the highest weight, and a2 and b2 are power function model constants.

[0118] The ExpAssoc model uses the following formula:

[0119]

[0120] Where Q represents the measured gas content in scc / g, T represents the main index with the highest weight, and q0, a3, b3, a4, and b4 are constants of the ExpAssoc model.

[0121] In step S6, the parameters of each of the three models are solved. Table 5 shows the parameters of each model obtained based on the data from region D.

[0122] Table 5. Parameters of the three types of models

[0123]

[0124]

[0125] Next, we introduce the coefficient of determination R. 2To evaluate the performance of the three types of models, R is calculated using methods known in the art. 2 The value of R. 2 The value of is between 0 and 1; a larger value indicates a better model and a closer fit to the true value. Calculations show that the R-values ​​of the three models are... 2 The values ​​are 0.908, 0.913, and 0.998, respectively.

[0126] In step S7, the model with the best evaluation performance is selected as the quantitative evaluation model for lacustrine shale gas sweet spots. Based on the evaluation results of step S6, the ExpAssoc model is the preferred quantitative evaluation model for lacustrine shale gas sweet spots. After substituting and simplifying the parameters, the following quantitative evaluation model for lacustrine shale gas sweet spots is obtained:

[0127]

[0128] Where Q represents the measured gas content, in scc / g; T represents the TOC data, expressed as a percentage. The fitting relationship between the evaluation model and the data points is as follows: Figure 2 As shown.

[0129] In step S8, the obtained quantitative evaluation model for lacustrine shale gas sweet spots can be used to quantitatively evaluate other blocks in region D, identifying the block with the highest gas production as the lacustrine shale gas sweet spot region. For example, when using the above model to quantitatively evaluate the lacustrine shale gas sweet spot of block E in region D, all TOC data for block E are first collected, and the data is thoroughly checked to ensure its completeness and accuracy. The average value of all TOC values ​​is calculated, and the average TOC value for block E is 1.5. Using the quantitative evaluation model for lacustrine shale gas sweet spots established in step S7, substituting the average TOC value into the independent variable T, the following model can be obtained:

[0130]

[0131] The gas content Q of block E can be calculated using this formula to be approximately 1.52 scc / g. Therefore, a rapid quantitative evaluation of the shale gas content in block E can be achieved. By comparing the gas production of different blocks, the lacustrine shale gas sweet spot in region D can be quickly determined.

[0132] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the disclosed embodiments of the present invention is limited to these examples. Within the framework of the present invention, technical features of the above embodiments or different embodiments can also be combined, and many other variations of different aspects of the present invention as described above exist, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for quantitative evaluation of sweet spots in lacustrine shale gas, characterized in that, Includes the following steps: Obtain seismic, logging, well logging, and analytical data of lacustrine shale gas in various regions, and establish a shale gas sweet spot evaluation database based on the data collected. Using lacustrine shale gas production as a direct representation of the sweet spot of lacustrine shale gas, the following five main indicators related to lacustrine shale gas production were screened and extracted: total organic carbon (TOC), vitrinite reflectance (R0), and so on. o Clay mineral content, pore specific surface area, and average pore radius; The data of the five main indicators included in the shale gas sweet spot evaluation database were checked and cleaned to ensure that the data used was complete and accurate. The AHP (Analytical Hierarchical Method) was used to calculate the influence weights of five major indicators on lacustrine shale gas production, and the major indicator with the highest weight was used as the independent variable to establish a quantitative evaluation model for lacustrine shale gas sweet spot. For the selected region, linear regression models, power function models, and ExpAssoc models are constructed based on the measured data of the indicators corresponding to the independent variables and the measured gas content data. Solve for the parameters in the three models and introduce the coefficient of determination R. 2 To evaluate the performance of the three types of models; The model with the best evaluation performance was selected as the quantitative evaluation model for sweet spot in lacustrine shale gas. Based on the obtained quantitative evaluation model of lacustrine shale gas sweet spots, a quantitative evaluation is carried out on the region to be evaluated to determine the lacustrine shale gas sweet spots in the region.

2. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 1, characterized in that, The process of checking and cleaning the data of the five main indicators included in the shale gas sweet spot evaluation database includes checking for abnormal or missing data. If abnormal or missing data is found, data processing is performed on the abnormal or missing data.

3. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 2, characterized in that, The data processing includes directly deleting abnormal data that is obviously erroneous or caused by irrelevant noise; for missing data, finding the object most similar to the missing data object in the complete data and filling it with the value of this similar object.

4. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 1, characterized in that, The calculation of the influence weights of five key indicators on lacustrine shale gas production using the AHP (Analytic Hierarchy Process) includes the following steps: Construct the judgment matrix; Calculate the original weights based on the judgment matrix; Then, standardize the original weights to obtain the weight vector; Consistency is verified by calculating the largest eigenvalue of the judgment matrix to prove its rationality; If the judgment matrix is ​​determined to be reasonable, then the main indicator corresponding to the maximum value in the weight vector will be used as the independent variable for establishing a quantitative evaluation model of lacustrine shale gas sweet spot.

5. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 4, characterized in that, The judgment matrix is ​​constructed using a 9-point scaling method.

6. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 4, characterized in that, The original weights are calculated using the square root method based on the judgment matrix.

7. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 6, characterized in that, The original weights are calculated using the following formula: Where i represents the row number of the judgment matrix, k represents the column number of the judgment matrix, and w i a represents the original weight of the i-th row of the judgment matrix. ik This represents the element in the i-th row and k-th column of the judgment matrix.

8. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 4, characterized in that, The calculation formula used for the standardization is as follows: Among them, w i This represents the original weight of the i-th row of the judgment matrix. This represents the standardized weight of the i-th row.

9. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 4, characterized in that, The formula for calculating the largest eigenvalue of the judgment matrix is ​​as follows: Where, λ max A represents the largest eigenvalue, A represents the judgment matrix, and W represents the weight vector.

10. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 9, characterized in that, The consistency index is calculated based on the largest eigenvalue, and the consistency ratio is calculated based on the consistency index and the random consistency index. When the consistency ratio is less than 0.1, the judgment matrix is ​​determined to be reasonable.

11. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 10, characterized in that, The formula for calculating the consistency index is as follows: Where CI represents the consistency index, and n represents the order of the judgment matrix.

12. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 11, characterized in that, The formula for calculating the consistency ratio is as follows: Wherein, CR represents the consistency ratio and RI represents the random consistency index.

13. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 1, characterized in that, The linear regression model uses the following formula: Q=a1+b1T Formula (6) Where Q represents the measured gas content in scc / g, T represents the main indicator with the highest weight, and a1 and b1 are the constants of the linear regression model.

14. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 1, characterized in that, The power function model uses the following formula: Where Q represents the measured gas content in scc / g, T represents the main indicator with the highest weight, and a2 and b2 are power function model constants.

15. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 1, characterized in that, The ExpAssoc model uses the following formula: Where Q represents the measured gas content in scc / g, T represents the main index with the highest weight, and q0, a3, b3, a4, and b4 are constants of the ExpAssoc model.

16. The method for quantitative evaluation of sweet spots in lacustrine shale gas according to claim 1, characterized in that, Introducing the coefficient of determination R 2 To evaluate the performance of the three types of models, we need to compare the R-values ​​of the three types of models. 2 Value, in R 2 The model with the largest value is used as a quantitative evaluation model for sweet spots in lacustrine shale gas.

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