A multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins

Abstract

A multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in continental oil and gas basins belongs to the field of comprehensive oil and gas resource evaluation technology. This method focuses on the six major elements of hydrocarbon accumulation—"source, reservoir, caprock, migration, reservoir, and conservation"—as its core research area. It establishes a dynamic process of hydrocarbon accumulation, assigns weights to geological parameters using discovered oil reservoirs as benchmarks, and implements a quantitative evaluation calculation process for favorable hydrocarbon accumulation zones through computer algorithms. The method includes the following steps: 1) Identifying the main controlling factors of hydrocarbon accumulation; 2) Screening the main controlling parameters; 3) Parameter gridding and normalization; 4) Assigning corresponding weighting coefficients to each parameter; 5) Calculating favorable zones by overlaying multiple parameters; 6) Using the known distribution of oil-bearing zones to determine whether the evaluation results meet the actual geological conditions; 7) If not, readjusting the weight values ​​or normalizing the parameters until the actual geological conditions are met; 8) Outputting the calculation results and determining the favorable hydrocarbon accumulation zones based on a percentage score.

Description

Technical Field

[0001] This invention relates to the field of comprehensive evaluation technology of oil and gas resources, and in particular to a method for quantitatively evaluating favorable hydrocarbon accumulation zones in rift basins using multiple geological factors. Background Technology

[0002] The accumulation and formation of hydrocarbon reservoirs in continental basins is a dynamic and complex geological process involving numerous geological factors. It primarily involves research in six geological aspects: the six elements of hydrocarbon accumulation—source, reservoir, caprock, hydrocarbon sphere, migration, and conservation. How to effectively guide the exploration of favorable hydrocarbon accumulation areas has always been a concern for geologists. Current methods for evaluating regional hydrocarbon accumulation mostly involve overlaying multiple accumulation elements on a plane to qualitatively evaluate the hydrocarbon accumulation zones in the study area. Specifically, parameters for each accumulation element are selected and mapped separately, and then these geological maps are manually overlaid for qualitative analysis of favorable areas. This evaluation process is time-consuming, has low predictive accuracy, and requires a high level of research expertise from researchers.

[0003] As exploration continues and data becomes increasingly abundant, there is an urgent need for a fast and efficient method that can fully utilize and organically combine multidisciplinary data from the study area to make quantitative and accurate predictions of hydrocarbon accumulation zones. Summary of the Invention

[0004] The present invention aims to overcome the problems existing in the background technology and provide a multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins. This multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins can organically combine various geological data to achieve quantitative evaluation of favorable hydrocarbon accumulation zones in the study area. The evaluation process is time-saving, the results are objective, and the prediction accuracy is high.

[0005] A multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins includes the following steps:

[0006] Step 1: Identify the main controlling factors of hydrocarbon accumulation: By comprehensively analyzing the six elements of hydrocarbon accumulation in the study area, namely "genesis, reservoir, caprock, reservoir, migration and conservation", we can form an understanding of the hydrocarbon accumulation patterns in the study area and identify the main controlling factors of hydrocarbon accumulation.

[0007] Step 2: Screening key hydrocarbon accumulation parameters: Based on the clearly identified main hydrocarbon accumulation controlling factors, determine the key hydrocarbon accumulation parameters;

[0008] Step 3, parameter meshing and normalization: By determining the main control parameters, the corresponding main control parameter data are collected. The determined main control parameter data is meshed to form a parameter data volume, and then dimensionless normalization is performed to obtain multiple meshed and normalized three-dimensional dimensionless parameter data volumes.

[0009] Step 4: Assign corresponding weighting coefficients to each dimensionless parameter data volume: Using the discovered oil reservoirs as the standard, and based on the analysis results of the main control factors of reservoir formation, assign a certain weighting coefficient to each three-dimensional dimensionless parameter data volume according to its importance.

[0010] Step 5: Superimpose multiple dimensionless parameter data volumes to calculate favorable areas: Multiply the three-dimensional dimensionless parameter data volumes by weighting coefficients and then superimpose them to calculate the evaluation scores of each zone. The zones with higher evaluation scores are the favorable hydrocarbon accumulation zones.

[0011] Step 6: Verify the evaluation results: Use known favorable hydrocarbon accumulation zones to determine whether the evaluation results meet the actual geological conditions. If not, readjust the weight values ​​or perform normalization again until the actual geological conditions are met, and then determine the final evaluation score.

[0012] Step 7: Obtain the final evaluation conclusion: Based on the percentage score, the areas with high evaluation scores are the favorable zones for hydrocarbon accumulation.

[0013] Preferably, the main reservoir-controlling parameters collected in step 3 are as follows: source rock evaluation parameters, including thickness and oil discharge intensity; reservoir evaluation parameters, including thickness, porosity, and sand-to-soil ratio; caprock evaluation parameters, including thickness; trap effectiveness evaluation parameters, including fault-to-sand ratio (vertical fault displacement / sand layer thickness calculated from structural maps); and oil and gas preservation conditions (caprock damage degree and fault conditions, etc.), including fault-to-caprock ratio (vertical fault displacement / caprock thickness calculated from structural maps).

[0014] Preferably, the parameter meshing process in step 3 is as follows: the meshing function of software with three-dimensional data volume meshing function is called to perform planar meshing processing on the collected main control parameters one by one to form multiple parameter data volumes;

[0015] Preferably, the software with three-dimensional data volume meshing function is the DoubleFox geological mapping system or Surfer software, etc.

[0016] Preferably, the dimensionless normalization process for the gridded parameter data volume of each major reservoir-controlling parameter is as follows: The gridded parameter data volume is scored according to the geological parameter characteristics of the study area, and different evaluation coefficients are assigned to different ranges of different parameters. If the evaluation coefficient range of a certain parameter is [a, b], and the corresponding parameter value range is [Z1, Z2], then when the parameter value Z ∈ [Z1, Z2], the normalized parameter value Z′ is:

[0017]

[0018] The normalized parameter values ​​are assigned a percentage system, which is to multiply the normalized parameter values ​​above by 100 to maintain their evaluation significance, ultimately resulting in multiple gridded, normalized three-dimensional dimensionless parameter data volumes.

[0019] The calculation of the vertical displacement requires the use of collected structural maps to calculate the sand-to-sand ratio and the cap-to-cover ratio. Specific formulas and algorithms are used in the statistical calculation of the vertical displacement. The sand-to-sand ratio is the ratio of the vertical displacement calculated from the structural maps to the sand layer thickness. The cap-to-cover ratio is the ratio of the vertical displacement calculated from the structural maps to the cap layer thickness.

[0020] Preferably, the vertical displacement statistical process is as follows:

[0021] (1) Using vector construction maps, extract the fault plane distribution range (fault polygon data) and the contour lines of the construction map depth or elevation, and densify the data points according to certain accuracy requirements;

[0022] (2) For each point on the encrypted fault polygon, find the two closest points on the other side of the same fault. Draw perpendicular lines from this point to the other two closest points. The distance between this point and the foot of the perpendicular is the horizontal fault displacement. The absolute value of the difference between the depth or elevation value of this point and the foot of the perpendicular projected on the structural map is the vertical fault displacement.

[0023] (3) Perform the same calculation on each point on the fault polygon after plane densification to obtain the planar distribution data of vertical fault displacement.

[0024] The preferred method for calculating the horizontal displacement is as follows:

[0025] A point P1(x1,y1) outside the line belongs to a point on the first block of the fault, and the line Ax+By+C=0(A 2 +B 2 ≠0) is the straight line containing the two points (P2(x2,y2), P3(x3,y3)) closest to P1(x1,y1) on the other side of the fault. The formula for the distance from point P1(x1,y1) to the above straight line is used to calculate the horizontal displacement d:

[0026]

[0027] For a point on one side of a fault, we can use vector methods to determine if it corresponds to a point on the other side. Vectors have direction; when determining if a point is on the left or right side of a line, the left and right directions are relative to the direction of travel. Once the direction of travel is specified, the left and right sides can be determined. Let's define three points on the plane: P1(x1,y1), P2(x2,y2), and P3(x3,y3). These three points form a triangle with an area of:

[0028]

[0029] When P1, P2, P3 are counterclockwise, S is positive; when P1, P2, P3 are clockwise, S is negative. Let the starting point of the vector be P2 and the ending point be P3. Let the point to be judged be P1. If S(P1,P2,P3) is positive, then P1 is to the left of vector P2P3. If S(P1,P2,P3) is negative, then P1 is to the right of vector P2P3. If S(P1,P2,P3) is 0, then P1 is on the line P2P3. This can be used to determine the corresponding points on the two sides of the fault, so as to avoid selecting the two closest points as points on the same side of the fault.

[0030] Preferably, the process of calculating the sand-to-fracture ratio is as follows: the planar distribution data of the vertical fault distance and the planar distribution data of the sand body thickness obtained above are respectively gridded. For each corresponding grid point, the fault distance is divided by the sand body thickness to obtain the planar distribution gridded data of the sand-to-fracture ratio.

[0031] Preferably, the formula for performing three-dimensional weighted superposition calculation on multiple gridded and normalized dimensionless parameter data volumes in step 5 is as follows:

[0032]

[0033] Where: Z' ij Z represents the comprehensive evaluation score of a certain grid node j, n represents the number of parameter types involved in the evaluation, and Z represents the comprehensive evaluation score of a certain grid node j. ij k represents the value of the j-th grid node in the i-th dimensionless parametric data volume. i is the weighting coefficient for the i-th dimensionless parameter data volume.

[0034] The multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins of this invention has the following advantages compared with the aforementioned background technology:

[0035] (1) Multiple geological parameters can be organically combined. The data processing capabilities of computers can save the huge workload of mapping and manually overlaying individual parameters one by one. The evaluation method provided is more scientific, the prediction results are more in line with the actual geological conditions, and the accuracy of prediction of favorable oil and gas accumulation zones is improved.

[0036] (2) Since the plane distribution of fault displacement, fault-sand ratio and fault-cover ratio parameters is calculated and a relatively high weighting coefficient is given, the influence of faults can be considered more, making it more suitable for oil and gas basins or strata with strong fault activity, such as rift basins. Attached Figure Description

[0037] To better explain the evaluation method and its advantages provided by the present invention, the accompanying drawings used in the description of the embodiments are briefly introduced below, so that those skilled in the art can further understand the present invention.

[0038] Figure 1 It is a structural schematic diagram of the present invention;

[0039] Figure 2 This is a reservoir model diagram of the region in an embodiment of the present invention;

[0040] Figure 3 This is a plan view of the thickness of high-quality hydrocarbon source rocks in the region described in this embodiment of the invention;

[0041] Figure 4 This is a plan view of the source rock oil drainage intensity in the region described in this embodiment of the invention;

[0042] Figure 5 This is a plan view of the thickness of the small sand body in the region described in this embodiment of the invention;

[0043] Figure 6 This is a plan view of the sand layer ratio in the region of this invention embodiment;

[0044] Figure 7 This is a planar diagram of the porosity of the sublayer in an embodiment of the present invention;

[0045] Figure 8 This is a plan view of the cover layer thickness in an area according to an embodiment of the present invention;

[0046] Figure 9 This is a plan view of the vertical displacement of a small layer in the region according to an embodiment of the present invention;

[0047] Figure 10 This is a plan view of the sand ratio of the small layer in the region of this invention embodiment;

[0048] Figure 11 This is a plan view of the small-layer cross-section ratio in an area according to an embodiment of the present invention;

[0049] Figure 12 This is a plan view of the initial calculated advantageous area of ​​the region in an embodiment of the present invention;

[0050] Figure 13 This invention provides a plan view of the advantageous area calculated after adjusting the parameter weights for the region in an embodiment of the invention.

[0051] Figure 14 This is a schematic diagram illustrating the calculation of horizontal dislocation according to the present invention. Detailed Implementation

[0052] Modifications and variations made by those skilled in the art based on this invention are within the scope of protection of this invention.

[0053] A multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins includes the following steps:

[0054] Step 1: Identify the main controlling factors of hydrocarbon accumulation: By comprehensively analyzing the six elements of hydrocarbon accumulation in the study area, namely "genesis, reservoir, caprock, reservoir, migration and conservation", we can form an understanding of the hydrocarbon accumulation patterns in the study area and identify the main controlling factors of hydrocarbon accumulation.

[0055] Step 2: Screening key hydrocarbon accumulation parameters: Based on the clearly identified main hydrocarbon accumulation controlling factors, determine the key hydrocarbon accumulation parameters;

[0056] Step 3, parameter meshing and normalization: By determining the main control parameters, the corresponding main control parameter data are collected. The determined main control parameter data is meshed to form a parameter data volume, and then dimensionless normalization is performed to obtain multiple meshed and normalized three-dimensional dimensionless parameter data volumes.

[0057] The main reservoir-controlling parameters collected include: parameters related to source rock evaluation, such as thickness and oil discharge intensity; parameters related to reservoir evaluation, such as thickness, porosity, and sand-to-soil ratio; parameters related to caprock evaluation, such as thickness; parameters related to trap effectiveness evaluation, such as fault-to-sand ratio (vertical fault displacement / sand layer thickness calculated from structural maps); and oil and gas preservation conditions (degree of caprock damage and fault conditions, etc.), including fault-to-caprock ratio (vertical fault displacement / caprock thickness calculated from structural maps).

[0058] The process of gridding the main gas-controlling parameters is as follows: the gridding function of software with three-dimensional data volume gridding function (such as Shuanghu Geological Mapping System or Surfer software) is called to perform planar gridding on the collected main gas-controlling parameter data one by one to form multiple parameter data volumes;

[0059] The process of dimensionless normalization of the gridded data for each major geological parameter is as follows: The gridded parameter data is scored according to the geological parameter characteristics of the study area. Different evaluation coefficients are assigned to different ranges of different parameters. If the evaluation coefficient range of a certain parameter is [a, b], and the corresponding parameter value range is [Z1, Z2], then when the parameter value is Z∈[Z1, Z2], the normalized parameter value Z′ is:

[0060]

[0061] The normalized parameter values ​​are assigned a percentage system, which is to multiply the normalized parameter values ​​above by 100 to maintain their evaluation significance, ultimately resulting in multiple gridded, normalized three-dimensional dimensionless parameter data volumes.

[0062] The parameters collected in this step, such as vertical displacement, sand ratio, and cover ratio, are used to calculate the vertical displacement, and then to calculate the sand ratio and cover ratio parameters. Specific formulas and algorithms are used in the statistical calculation of the vertical displacement.

[0063] The vertical displacement statistics process is as follows:

[0064] (1) A vector construction map is needed to extract the fault plane distribution range (fault polygon data) and the contour lines of the construction map depth or elevation, and to densify the data points according to certain accuracy requirements;

[0065] (2) For each point on the encrypted fault polygon, find the two closest points on the other side of the same fault. Draw perpendicular lines from this point to the other two closest points. The distance between this point and the foot of the perpendicular is the horizontal fault displacement. The absolute value of the difference between the depth or elevation value of this point and the foot of the perpendicular projected on the structural map is the vertical fault displacement.

[0066] (3) Perform the same calculation on each point on the fault polygon after plane densification to obtain the planar distribution data of vertical fault displacement.

[0067] The calculation process and method for horizontal displacement are as follows (e.g.) Figure 14 As shown):

[0068] A point P1(x1,y1) outside the line belongs to a point on the first block of the fault, and the line Ax+By+C=0(A 2 +B 2 ≠0) is the straight line containing the two points (P2(x2,y2), P3(x3,y3)) closest to P1(x1,y1) on the other side of the fault. The formula for the distance from point P1(x1,y1) to the above straight line is used to calculate the horizontal displacement d:

[0069]

[0070] For a point on one side of a fault, we can use vector methods to determine if it corresponds to a point on the other side. Vectors have direction; when determining if a point is on the left or right side of a line, the left and right directions are relative to the direction of travel. Once the direction of travel is specified, the left and right sides can be determined. Let's define three points on the plane: P1(x1,y1), P2(x2,y2), and P3(x3,y3). These three points form a triangle with an area of:

[0071]

[0072] When P1, P2, P3 are counterclockwise, S is positive; when P1, P2, P3 are clockwise, S is negative. Let the starting point of the vector be P2 and the ending point be P3. Let the point to be judged be P1. If S(P1,P2,P3) is positive, then P1 is to the left of vector P2P3. If S(P1,P2,P3) is negative, then P1 is to the right of vector P2P3. If S(P1,P2,P3) is 0, then P1 is on the line P2P3. This can be used to determine the corresponding points on the two sides of the fault, so as to avoid selecting the two closest points as points on the same side of the fault.

[0073] The statistical process for the sand breakage ratio is as follows:

[0074] The planar distribution data of vertical fault displacement and sand body thickness obtained above are gridded. For each corresponding grid point, the fault displacement is divided by the sand body thickness to obtain the planar distribution gridded data of the fault-sand ratio.

[0075] Step 4: Assign corresponding weighting coefficients to each dimensionless parameter data volume: Using the discovered oil reservoirs as the standard, and based on the analysis results of the main control factors of reservoir formation, assign a certain weighting coefficient to each three-dimensional dimensionless parameter data volume according to its importance.

[0076] Step 5: Superimpose multiple dimensionless parameter data volumes to calculate favorable areas: Multiply the three-dimensional dimensionless parameter data volumes by weighting coefficients and then superimpose them to calculate the evaluation scores of each zone. The zones with higher evaluation scores are the favorable hydrocarbon accumulation zones.

[0077] The formula for calculating the favorable region by superimposing multiple dimensionless parameter data volumes is as follows:

[0078]

[0079] Where: Z' ij Z represents the comprehensive evaluation score of a certain grid node j, n represents the number of parameter types involved in the evaluation, and Z represents the comprehensive evaluation score of a certain grid node j. ij k represents the value of the j-th grid node in the i-th dimensionless parametric data volume. i is the weighting coefficient for the i-th dimensionless parameter data volume.

[0080] Step 6: Verify the evaluation results: Use known favorable hydrocarbon accumulation zones to determine whether the evaluation results meet the actual geological conditions. If not, readjust the weight values ​​or perform normalization again until the actual geological conditions are met, and then determine the final evaluation score.

[0081] Step 7: Obtain the final evaluation conclusion: Based on the percentage score, the areas with high evaluation scores are the favorable zones for hydrocarbon accumulation.

[0082] Example 1

[0083] The following example, using the N161 main oil layer in the Wuerxun-Beer Depression of the Hailar Basin, illustrates the application of the multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in continental oil and gas basins according to the present invention.

[0084] like Figure 1 As shown, the specific implementation process of the multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins of the present invention includes the following steps:

[0085] S1. Based on the multi-factor geological analysis of the study area, and taking the six elements of hydrocarbon accumulation (generation, storage, cap, reservoir, transport, and conservation) as the main research line, relevant geological parameter maps and data affecting hydrocarbon content in the study area are selected.

[0086] Each influencing factor can be further subdivided to identify the main sub-factors, so as to avoid some parameters being inaccurate or data being incomplete, which could affect the role of a certain type of parameter.

[0087] S2. Screening of reservoir-controlling geological factors: Based on the characteristics of the reservoir formation model in the study area, the reservoir model in the study area is as follows... Figure 2 As shown, parameters are used to select the main factors controlling hydrocarbon accumulation, and parameters that are seriously irrelevant or have minimal impact on hydrocarbons in this area are eliminated.

[0088] Based on previous research, the main reservoir type in the study area is identified as a structural-lithological reservoir. It is believed that the influence of faults is relatively large, while the caprock is generally well-developed and has a relatively small influence. When considering the influence of fault parameters, the fault-sand ratio and fault-caprock ratio are mainly considered. According to the geological conditions of the study area, the normalized evaluation coefficient of each parameter is determined and assigned. The parameter normalization standard of the study area is shown in Table 1.

[0089] Table 1

[0090]

[0091] Based on the geological conditions of the study area, the main parameters selected for source rock conditions are source rock thickness and oil discharge intensity.

[0092] The parameters selected for reservoir conditions are reservoir thickness, sand-to-soil ratio, and porosity;

[0093] The parameter selected for the cap layer condition is the cap layer thickness;

[0094] The parameter selected for evaluating the effectiveness of the trap is the sand breakage ratio;

[0095] In the embodiments, the oil and gas migration conditions in the rift basin are mainly based on faults, and since the fault-sand ratio parameter is available, other parameters are not needed.

[0096] The degree of cap layer damage is used as a preservation condition, and the cap layer ratio parameter is selected.

[0097] S3. Multi-parameter data processing yields new geologically significant parameters. Existing multi-geological parameter data is processed through data operations to obtain new and more effective parameters. During the evaluation process, parameters need to be redefined to more accurately assess the characteristics of relevant geological factors.

[0098] In the embodiments, the newly defined parameters include three parameters: sand-to-land ratio, sand-to-fracture ratio, and cover-to-fracture ratio.

[0099] The sand-to-soil ratio is the ratio of the thickness of sandstone to the thickness of strata in the evaluation zone;

[0100] The fault-sand ratio is the ratio of the vertical displacement of the fault to the thickness of the sandstone.

[0101] The fault-to-cover ratio is the ratio of the vertical displacement of a fault to the thickness of the cover layer.

[0102] S4. Dimensionless parameter data volume processing: Dimensionless parameter data volume is generated by performing dimensionless normalization on each gridded parameter data volume (partial data of the three-dimensional gridded data volume of the thickness of high-quality source rocks in the study area is shown in Table 2).

[0103] Table 2

[0104]

[0105]

[0106] Table 3

[0107] X Y After normalization 514881.0813 5292070.5 77.584335 514974.9 5292070.5 77.65964106 515068.7188 5292070.5 77.73494712 515162.5375 5292070.5 77.81025317 514974.9 5292164.319 77.58520635 515068.7188 5292164.319 77.65289683 515162.5375 5292164.319 77.72058731 514974.9 5292258.138 77.51077163 515068.7188 5292258.138 77.57084654 515162.5375 5292258.138 77.63092144

[0108] All selected parameters in the embodiments were statistically analyzed, gridded, and normalized according to the standards in Table 1.

[0109] S5. Using the discovered oil reservoirs as benchmarks, and based on the results revealed by the drilling data in the study area, assign weights to the reservoir-controlling factors. By dissecting the existing reservoir geological parameters, analyze the main reservoir-forming control factors and assign weights to the geological parameters according to their importance.

[0110] In this embodiment, the source rock and faults have a significant effect on reservoir control, followed by the reservoir itself, and then trap conditions and caprock preservation conditions (see...). Figures 3-11 ); whereby, the thickness planar diagram of high-quality source rock in the example area is as follows: Figure 3 The source rock oil drainage intensity planar diagram of the example area is shown below. Figure 4 The plan view of the thickness of the small sand body in the example area is shown below. Figure 5 ; Example: Plan view of the small-layer sandy soil in the region Figure 6 The surface porosity diagram of the small layer in the embodiment area is shown below. Figure 7 The plan view of the cover layer thickness in the embodiment area is as follows: Figure 8 The vertical displacement plan of the small-layer area in the example area is as follows: Figure 9 ; Plan view of the small-layer sand ratio in the example area is as follows Figure 10 ; Example: Plan view of the small-layer cross-section ratio of the region. Figure 11 As shown.

[0111] S6. Multi-parameter fusion: Calculate the evaluation zone score by weighting and merging multiple dimensionless parameter data volumes to determine the favorable hydrocarbon accumulation zone.

[0112] Preliminary weighting coefficients were assigned (source rock thickness 0.1, oil discharge intensity 0.1, reservoir thickness 0.2, sand-to-soil ratio 0.1, reservoir porosity 0.2, caprock thickness 0.1, sand-to-caprock ratio 0.1, caprock-to-soil ratio 0.1), and initial evaluation calculation results were obtained. The initial calculation plan of the favorable area in the study area is shown below. Figure 12 As shown;

[0113] S7. Observe and evaluate whether the results meet the actual geological conditions of the oil-bearing zone revealed by the known wells;

[0114] Among them, there is a large error between the initial evaluation results and the actual drilling results. Figure 12 );

[0115] S8. Readjust the weight values ​​until they meet the actual geological conditions;

[0116] The weights in the embodiments are adjusted, and the evaluation is recalculated as follows;

[0117] S6. Multi-parameter fusion: Calculate the evaluation zone score by weighting and merging multiple dimensionless parameter data volumes to determine the favorable hydrocarbon accumulation zone.

[0118] After several adjustments to the weighting coefficients, a final set of weighting coefficients was determined (source rock thickness 0.1, oil discharge intensity 0.15, reservoir thickness 0.1, sand-to-soil ratio 0.15, reservoir porosity 0.15, caprock thickness 0.1, sand-to-caprock ratio 0.2, caprock-to-soil ratio 0.05). The final evaluation calculation results were obtained, and the favorable area plan map after adjusting the parameter weights is shown below. Figure 13 As shown;

[0119] S7. Observe and evaluate whether the results meet the actual geological conditions of the oil-bearing zone revealed by the known wells;

[0120] Among them, the final evaluation results showed a high degree of matching with the actual drilling results, and the prediction effect was good.

[0121] S9. Output the calculation results and determine the ranking of the favorable hydrocarbon accumulation zones based on the percentage scores.

[0122] As can be seen from the above technical solution of the present invention, this method can make scientific and relatively reliable predictions of favorable hydrocarbon accumulation zones in basins. The prediction results are closely related to the research data of the actual study area. In the past, manual mapping of individual parameters and overlay of regions could only qualitatively analyze zones, resulting in low prediction accuracy. This method, through the powerful computing capabilities of computers, can modify parameter weights in real time, perform multiple calculations in a short period of time, and select different geological parameters according to the geological conditions of different study areas, saving geological researchers a lot of time for repetitive work.

Claims

1. A multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins, characterized in that... Includes the following steps: Step 1: Identify the main controlling factors of hydrocarbon accumulation: By comprehensively analyzing the six elements of hydrocarbon accumulation in the study area, namely "genesis, reservoir, caprock, reservoir, migration and conservation", we can form an understanding of the hydrocarbon accumulation patterns in the study area and identify the main controlling factors of hydrocarbon accumulation. Step 2: Screening key hydrocarbon accumulation parameters: Based on the clearly identified main hydrocarbon accumulation controlling factors, determine the key hydrocarbon accumulation parameters; Step 3, parameter meshing and normalization: By determining the main control parameters, the corresponding main control parameter data are collected. The determined main control parameter data is meshed to form a parameter data volume, and then dimensionless normalization is performed to obtain multiple meshed and normalized three-dimensional dimensionless parameter data volumes. The parameter meshing process in step 3 is as follows: the meshing function of software with three-dimensional data volume meshing function is called to perform planar meshing processing on the collected main control parameters one by one to form multiple parameter data volumes; Step 4: Assign corresponding weighting coefficients to each dimensionless parameter data volume: Using the discovered oil reservoirs as the standard, and based on the analysis results of the main control factors of reservoir formation, assign a certain weighting coefficient to each three-dimensional dimensionless parameter data volume according to its importance. Step 5: Superimpose multiple dimensionless parameter data volumes to calculate favorable areas: Multiply the three-dimensional dimensionless parameter data volumes by weighting coefficients and then superimpose them to calculate the evaluation scores of each zone. The zones with higher evaluation scores are the favorable hydrocarbon accumulation zones. Step 6: Verify the evaluation results: Use known favorable hydrocarbon accumulation zones to determine whether the evaluation results meet the actual geological conditions. If not, readjust the weight values ​​or perform normalization again until the actual geological conditions are met, and then determine the final evaluation score. Step 7: Obtain the final evaluation conclusion: Based on the percentage score, determine that the areas with high evaluation scores are favorable zones for hydrocarbon accumulation; The main reservoir control parameters collected in step 3 are as follows: parameters related to source rock evaluation include thickness and oil discharge intensity; parameters related to reservoir evaluation include thickness, porosity, and sand-to-soil ratio; parameters related to caprock evaluation include thickness; parameters related to trap effectiveness evaluation include sand-to-fracture ratio; and parameters related to oil and gas preservation conditions include caprock-to-fracture ratio.

2. The method for quantitative evaluation of favorable hydrocarbon accumulation zones in continental oil and gas basins according to claim 1, characterized in that: Step 3, the dimensionless normalization process for the gridded parameter data volume of each major resource-controlling parameter, involves: scoring the gridded parameter data volume according to the geological parameter characteristics of the study area, assigning different evaluation coefficients to different parameters and different ranges; if the evaluation coefficient range for a certain parameter is... The given range of corresponding parameter values ​​is [ When the parameter value is ] At that time, the normalized parameter values : ； The normalized parameter values ​​are assigned a percentage system, which is to multiply the normalized parameter values ​​above by 100 to maintain their evaluation significance, ultimately resulting in multiple gridded, normalized three-dimensional dimensionless parameter data volumes.

3. The method for quantitative evaluation of favorable hydrocarbon accumulation zones in terrestrial oil and gas basins according to claim 1, characterized in that, The formula for calculating the favorable region by superimposing multiple dimensionless parameter data volumes in step 5 is as follows: ； Where: Z' ij This represents the comprehensive evaluation score of a certain grid node j. Z represents the number of parameter types involved in the evaluation. ij k represents the value of the j-th grid node in the i-th dimensionless parametric data volume. i is the weighting coefficient for the i-th dimensionless parameter data volume.

4. The method for quantitative evaluation of favorable hydrocarbon accumulation zones in terrestrial oil and gas basins according to claim 1, characterized in that, The calculation of the sand-to-cover ratio and the cover-to-cover ratio requires the application of vertical displacement, and the calculation of vertical displacement requires the use of collected structural maps; the sand-to-cover ratio is the ratio of vertical displacement to sand layer thickness calculated from the structural maps; the cover-to-cover ratio is the ratio of vertical displacement to cover layer thickness calculated from the structural maps.

5. The method for quantitative evaluation of favorable hydrocarbon accumulation zones in terrestrial oil and gas basins according to claim 4, characterized in that, The vertical displacement statistics process is as follows: (1) Using vector construction maps, extract the fault plane distribution range and the contour lines of the construction map depth or elevation, and densify the data points according to certain accuracy requirements; (2) For each point on the encrypted fault polygon, find the two closest points on the other side of the same fault. Draw perpendicular lines from this point to the other two closest points. The distance between this point and the foot of the perpendicular is the horizontal fault displacement. The absolute value of the difference between the depth or elevation value of this point and the foot of the perpendicular projected on the structural map is the vertical fault displacement. (3) Perform the same calculation on each point on the fault polygon after plane densification to obtain the planar distribution data of vertical fault displacement.

6. The method for quantitative evaluation of favorable hydrocarbon accumulation zones in terrestrial oil and gas basins according to claim 5, characterized in that, The calculation process and method for horizontal displacement are as follows: A point outside the line It belongs to a point on the fault block, a straight line It is the distance on the other side of the fault. Point the two nearest points ( The line containing the point The distance formula is used to calculate the horizontal break distance. : ； For a point on one side of a fault, we can use vector methods to determine if it is a corresponding point on the other side. Vectors have direction; when determining if a point is on the left or right side of a line, the left and right directions are relative to the direction of travel. Once the direction of travel is specified, the left and right sides can be determined. We define three points on the plane... The three points form a triangle with an area of: ； when When rotating counterclockwise, S is positive. When the direction is clockwise, S is negative. Let the starting point of the vector be... The destination is The point to be judged is If S ( If ) is a positive number, then In vector If S( If ) is negative, then In vector On the right side, if S ( If ) is 0, then In a straight line This allows us to determine the corresponding points on the two sides of the fault, thus avoiding selecting the two closest points as points on the same side of the fault.

7. A multi-factor quantitative evaluation method for favorable hydrocarbon accumulation zones in terrestrial oil and gas basins according to claim 1 or 5, characterized in that, The process of calculating the sand-to-fracture ratio is as follows: the planar distribution data of the vertical fault distance obtained in step 7 and the planar distribution data of the sand body thickness obtained in step 3 are respectively gridded. For each corresponding grid point, the fault distance is divided by the sand body thickness to obtain the planar distribution gridded data of the sand-to-fracture ratio.

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