A method for calculating weights of a continental shale reservoir compressibility evaluation model

Abstract

The application discloses a weight calculation method of a continental shale reservoir fracturability evaluation model, and belongs to the technical field of hydraulic fracturing. The application scientifically determines the weight of each index by fusing geological modeling and fracturing simulation results, introducing weight calculation methods such as the analytic hierarchy process and the grey correlation method, reduces the dependence degree on field monitoring data, simultaneously, establishes a quantitative correlation with actual productivity, quantifies the contribution of three types of capacity indexes to productivity, and finally determines a fracturability comprehensive evaluation model with engineering guiding value, so that the fracturing reconstruction potential of a reservoir can be more accurately represented, and a scientific theoretical basis can be provided for reservoir optimization and fracturing design.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic fracturing, in particular to a weight calculation method of a continental shale reservoir fracturability evaluation model. BACKGROUND

[0002] The continental shale reservoir has the characteristics of low brittleness, strong heterogeneity and complex ground stress conditions, which not only limits the vertical extension ability of the hydraulic fracture, but also hinders the branching and connection of the fracture, and reduces the formation probability of the complex fracture network. The current widely used horizontal well staged multi-cluster fracturing technology is easily affected by the reservoir heterogeneity in actual construction, resulting in that part of the cluster cannot be effectively started, the fracture expansion is uneven, and the overall transformation effect is affected. Therefore, it is of important engineering significance to construct a comprehensive multi-factor and strong adaptability fracturability evaluation method for reservoir optimization and fracturing design.

[0003] In the prior art, the patent for invention with publication number CN119378785A and the name of "an evaluation method and system for the fracturability of continental shale reservoirs" proposes a method for evaluating the fracturability of continental shale reservoirs based on a multiple linear regression method to calculate the weight coefficient, but this method has high dependence on field monitoring data such as microseismic and production contribution rate, and has limitations. The patent for invention with publication number CN119393108A and the name of "a method for optimizing the volume fracturing technology of continental shale" compares the fracture complexity index, the through-layer expansion index and the balanced expansion index to optimize the volume fracturing technology, but does not explicitly indicate the relative weights of the three indexes on the transformation effect, which is difficult to guide the actual fracturability evaluation.

[0004] Although some studies on the fracturability of continental shale have introduced multi-dimensional indexes such as "fracture complexity", "vertical extension ability" and "multi-cluster balanced degree" (such as the patents for invention with publication numbers CN119378785A and CN119393108A), there are generally the following problems: first, most of them use linear superposition or empirical weighting method to determine the index weight, which is highly subjective and difficult to adapt to the fracturing design requirements under different geological conditions; second, the index system lacks a quantitative correlation mechanism with actual productivity, and cannot fully reflect the fracturing response ability and transformation potential of the reservoir.

[0005] Therefore, how to provide a weight calculation method of a continental shale reservoir fracturability evaluation model that can scientifically determine the weight of each index, has a small dependence on field monitoring data, and establishes a quantitative correlation with actual productivity, is a problem that needs to be solved by those skilled in the art. SUMMARY

[0006] The purpose of the present application is to provide a weight calculation method of a continental shale reservoir fracturability evaluation model to solve the problems in the background art.

[0007] To achieve the above objectives, this invention provides a method for calculating the weights of a continental shale reservoir compressibility evaluation model, comprising the following steps:

[0008] S1. Collect multi-dimensional geological features of target blocks of continental shale reservoirs and construct a three-dimensional geological model based on the geological features;

[0009] The capability indicators affecting the hydraulic fracturing effect were determined, including the planar fracture network construction capability indicator, the hydraulic fracture vertical propagation capability indicator, and the multi-cluster fracture balanced development capability indicator. Each capability indicator includes multiple key influencing factors.

[0010] S2. Based on the three-dimensional geological model, orthogonal experiments, numerical fracturing simulation, range analysis and analytic hierarchy process were carried out sequentially to calculate the key influencing factors, and a weighted calculation model for each capability index was established to obtain the corresponding capability index.

[0011] S3. Use grey relational analysis to calculate the grey relational degree between each capacity indicator and production capacity, and determine the relative weight of the capacity indicators.

[0012] Among them, the experimental samples of the grey relational analysis method are derived from the field fracturing response or the numerical fracturing simulation in S2;

[0013] S4. Combining the results of S2 and S3, a compressibility evaluation model is established, and the compressibility index of continental shale reservoirs is output.

[0014] Preferably, in S1, the multidimensional geological features include lithological assemblage, sedimentary facies type, bedding structure, and natural fracture development characteristics;

[0015] Key influencing factors for the planar fracture network construction capability index include natural fracture density, brittleness index, and horizontal stress difference; key influencing factors for the vertical propagation capability index of hydraulic fractures include friction coefficient, shear strength, tensile strength difference, and vertical stress difference; key influencing factors for the balanced development capability index of multiple fracture clusters include minimum horizontal stress difference between clusters, fracture toughness difference, and Young's modulus.

[0016] Preferably, the specific steps of S2 are as follows:

[0017] S21. Orthogonal test and numerical fracturing simulation: Based on key influencing factors, design a three-level orthogonal test scheme for each capability index, conduct numerical fracturing simulation, and output multiple simulation response parameters under each scheme.

[0018] S22. Range Analysis: Calculate the average value of the simulated response parameters at different levels, perform range analysis on the average value, and output the range value of each key influencing factor.

[0019] S23. Analytic Hierarchy Process (AHP) Calculation: The range value is processed using the AHP to obtain the weight value of each key influencing factor;

[0020] S24. Construct a capability index calculation formula based on weight values ​​and key influencing factors.

[0021] Preferably, in S21, the simulated response parameter corresponding to the planar fracture network construction capability index is the intra-segment fracture density, the simulated response parameter corresponding to the hydraulic fracture vertical propagation capability index is the fracture height, and the simulated response parameter corresponding to the multi-cluster fracture balanced development capability index is the multi-cluster balanced opening degree.

[0022] Preferably, in S21, the three-level orthogonal test table of the planar seam mesh construction capability index is shown in Table 1;

[0023] Table 1. Three-factor orthogonal test table of planar seam mesh construction capability index

[0024]

[0025] Where A is the normalized natural crack density, dimensionless; B is the normalized horizontal stress difference, dimensionless; C is the normalized brittleness index, dimensionless; Y is the crack density characterization within the segment; A1 represents a low level of natural crack density, B2 represents a medium level of horizontal stress difference, and C3 represents a high level of brittleness index.

[0026] Preferably, in step S22, the range analysis table of the planar seam mesh construction capability index is shown in Table 2;

[0027] Table 2 Range Analysis of Planar Joint Mesh Construction Capability Indicators

[0028]

[0029] Among them, I, II, and III represent low, medium, and high levels, respectively, and R represents the range value.

[0030] Preferably, in step S23, the specific process of processing the range value using the analytic hierarchy process is as follows:

[0031] (1) Determine the ratio of the range values ​​of different key influencing factors in the same capability index, convert the ratio relationship into a 1-9 scale, and construct an initial judgment matrix;

[0032] Among them, when the ratio of the range values ​​R A :R B :R C When k:m:1, the initial judgment matrix of the planar seam mesh construction capability index is shown in Table 3;

[0033] Table 3 Initial Judgment Matrix of Planar Joint Mesh Construction Capability Indicators

[0034] A B C A 1 k / m k / 1 B m / k 1 m / 1 C 1 / k 1 / m 1

[0035] (2) Perform a consistency check on the initial judgment matrix. When the consistency check result is greater than or equal to 0.1, return to step (1) to adjust the initial judgment matrix and perform the consistency check again until the consistency check result is less than 0.1, and obtain a judgment matrix that meets the consistency requirements.

[0036] The consistency test results are expressed as follows:

[0037]

[0038] In the formula, RI represents the average random consistency index, and CI represents the consistency coefficient. λ max The largest eigenvalue is n, and n is the order of the initial judgment matrix.

[0039] (3) The eigenvector corresponding to the largest eigenvalue of the judgment matrix is ​​calculated by using the eigenvalue method, and the eigenvector is normalized to obtain the weight value.

[0040] Preferably, in step S24, the calculation formulas for the capacity index, hydraulic fracture vertical propagation capacity index, and multi-cluster fracture balanced development capacity index corresponding to the planar fracture network construction capacity index are expressed as follows:

[0041] F net =w A A+w B B+w C C;

[0042] F cc =w D D+w E E+w F F+w G G;

[0043] F jh =w H H+w I I+w J J;

[0044] In the formula, F net F represents the construction capability index of planar seam mesh. cc F is the index of the vertical propagation capacity of hydraulic fractures. jhThe index represents the ability to achieve balanced development of multiple fracture clusters. A is the normalized natural fracture density (dimensionless); B is the normalized horizontal stress difference (dimensionless); C is the normalized brittleness index (dimensionless); D is the normalized interlayer friction coefficient (dimensionless); E is the normalized interlayer shear strength (dimensionless); F is the normalized interlayer tensile strength difference (dimensionless); G is the normalized vertical stress difference (dimensionless); H is the normalized minimum horizontal stress difference between clusters (dimensionless); I is the normalized fracture toughness difference (dimensionless); J is the normalized Young's modulus (dimensionless). A w B w C w D w E w F w G w H w I w J The weight values ​​are A, B, C, D, E, F, G, H, I, and J, respectively.

[0045] Preferably, in S3, the reference sequence for the grey relational analysis method is the cumulative oil production in year M, and the comparison sequence is the simulated response parameters of the three capability indicators.

[0046] The preferred experimental design scheme for the grey relational analysis method is shown in Table 4.

[0047] Table 4 Experimental Design Scheme for Grey Relational Analysis

[0048]

[0049] Where X1 is the fracture density within the segment, X2 is the fracture height, X3 is the degree of uniform opening of multiple clusters (dimensionless); X0 is the cumulative oil production in year M, X... i (k) represents the difference between the i-th comparison sequence and the reference sequence in the k-th test sample.

[0050] Preferably, in step S3, the formula for calculating the grey relational degree is:

[0051]

[0052] In the formula, γ i ξ represents the grey relational degree of the i-th capability indicator, where i = 1, 2, 3; N is the total number of experimental samples, and ξ is the grey relational degree of the i-th capability indicator. i (k) represents the grey relational coefficient of the i-th capability indicator, k = 1, ..., N;

[0053] The formula for calculating the grey relational coefficient is:

[0054]

[0055] In the formula, Δmin is the minimum difference, ρ is the resolution coefficient, ρ∈[0,1], usually taken as 0.5, Δmax is the maximum difference, Δ i (k) represents the absolute value of the difference;

[0056] The formula for calculating the absolute value of the difference is:

[0057] Δ i (k)=|X0(k)-X i (k)|;

[0058] The formulas for calculating the maximum difference and the minimum difference are as follows:

[0059] Δmax=maxΔ i (k);

[0060] Δmin=minΔ i (k).

[0061] Preferably, in step S3, the formula for calculating the relative weights of the capability indicators is as follows:

[0062]

[0063] In the formula, w i This represents the relative weight of the i-th capability indicator.

[0064] Preferably, the compressibility evaluation model in S4 is expressed as follows:

[0065] FI = w1F net +w2F cc +w3F jh ;

[0066] In the formula, FI is the compressibility index, F cc F is the index of the vertical propagation capacity of hydraulic fractures. jh It is an index representing the ability to achieve balanced development of multiple crack clusters.

[0067] Therefore, this invention provides a weight calculation method for a continental shale reservoir compressibility evaluation model. By scientifically integrating geological modeling and numerical simulation technologies, and combining the analytic hierarchy process (AHP) and grey relational analysis (GRA) to construct a weight calculation system, it significantly improves the objectivity and practicality of continental shale reservoir compressibility evaluation. Based on geological modeling, it conducts fracturing simulation and production prediction, and integrates the AHP and GRA to scientifically calculate the weights of multidimensional indicators, effectively avoiding the subjectivity of traditional experience-based weighting. At the same time, it uses simulation results to replace actual monitoring needs, significantly reducing the reliance on high-cost field monitoring data (such as microseismic data). Furthermore, it innovatively establishes a quantitative correlation model between capacity indicators and actual production capacity, enabling the evaluation results to not only reflect the reservoir stimulation potential but also directly predict engineering benefits, providing a decision-making basis with both theoretical depth and engineering guidance value for reservoir optimization and fracturing scheme design.

[0068] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0070] Figure 2 This is a schematic diagram of the three-dimensional geological model of the target block in an embodiment of the present invention;

[0071] Figure 3 The above are comparison diagrams of hydraulic fracture network propagation under different horizontal stress differences in the orthogonal experiment of the present invention. In (a), the horizontal stress difference is 2MPa, in (b), the horizontal stress difference is 4MPa, and in (c), the horizontal stress difference is 8MPa.

[0072] Figure 4 The figures show a comparison of the vertical propagation of hydraulic cracks under different interlayer stress differences in the orthogonal test of the present invention. In (a), the interlayer stress difference is 0 MPa, in (b), the interlayer stress difference is 2 MPa, and in (c), the interlayer stress difference is 4 MPa.

[0073] Figure 5 The above are comparison diagrams of the equilibrium propagation of hydraulic cracks under different Young's modulus conditions in the orthogonal experiment of the present invention. Among them, (a) has a Young's modulus of 15 GPa, (b) has a Young's modulus of 30 GPa, and (c) has a Young's modulus of 45 GPa.

[0074] Figure 6 The results of the grey relational analysis in this embodiment of the invention;

[0075] Figure 7 This is a schematic diagram of a complex crack network inclusion based on E-SRV according to an embodiment of the present invention, wherein (a) is a top view and (b) is a side view;

[0076] Figure 8 This is an overlay diagram of electromagnetic monitoring and microseismic monitoring results from an embodiment of the present invention;

[0077] Figure 9 The following are verification results of the planar crack network construction capability of the present invention, wherein (a) is a comparison diagram of the planar crack network construction capability, and (b) is the fitting result of the planar crack network construction capability;

[0078] Figure 10 The following are verification results of the vertical propagation capability of hydraulic fractures in the embodiments of the present invention, wherein (a) is a comparison diagram of the vertical propagation capability of hydraulic fractures, and (b) is the fitting result of the vertical propagation capability of hydraulic fractures.

[0079] Figure 11 The following are verification results of the balanced development ability of multiple crack clusters in the embodiments of the present invention: (a) is a comparison diagram of the balanced development ability of multiple crack clusters, and (b) is the fitting result of the balanced development ability of multiple crack clusters.

[0080] Figure 12 The following are the verification results of the compressibility index in the embodiments of the present invention, wherein (a) is a comparison chart of the compressibility index and (b) is the fitting result of the compressibility index. Detailed Implementation

[0081] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0082] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0083] Example

[0084] like Figure 1 As shown, this invention provides a weight calculation method for a continental shale reservoir compressibility evaluation model. Taking a horizontal continental shale well FY in Block Y of the Sichuan Basin as an example, the accuracy of the compressibility evaluation model is verified, as follows:

[0085] Well FY was designed to fracture 22 segments, but casing deformation was discovered after the 7th segment was fractured, resulting in only 16 segments actually being fractured (1–7, 14–22). Microseismic and wide-area electromagnetic methods were used for fracture monitoring during the construction process. The segment divisions and monitoring results are shown in Table 5.

[0086] Table 5. Fracturing of FY Well and Statistical Data of On-site Fracture Monitoring Results

[0087]

[0088] S1. Based on geological data such as well logging, seismic data, and core samples from this block, a three-dimensional geological model is established using Petrel geological modeling software (e.g., Figure 2 As shown in the figure, the model considers geological parameters such as reservoir thickness, lithology distribution, fault structure, and interlayer interfaces to ensure that the fracturing simulation has a realistic geological background.

[0089] S2. Based on a three-dimensional geological model, orthogonal experiments, numerical fracturing simulations, range analysis, and analytic hierarchy process (AHP) were sequentially performed on key influencing factors to establish a weighted calculation model for each capability index, yielding the corresponding capability index, specifically:

[0090] S21. Based on the established three-dimensional geological model, select key influencing factors such as natural fracture density, horizontal stress difference, and brittleness index, and design a three-level (low, medium, and high) orthogonal test scheme. For example... Figures 3-5 As shown, fracturing simulation was performed using Kinetix, and three response parameters were extracted based on the simulation results of all groups: intra-segment fracture density, fracture height, and multi-cluster balanced opening degree, which correspond to the three major capability indicators.

[0091] S22. Based on the simulation results, range analysis was performed to determine the importance ranking of each influencing factor in the planar fracture network construction capability, the vertical propagation capability of hydraulic fractures, and the balanced development capability of multi-cluster fractures.

[0092] S23. Construct a judgment matrix based on the range ratio, apply the analytic hierarchy process (AHP) to calculate the relative weight of each factor in each capability index, and then determine the evaluation models for planar fracture network construction capability, hydraulic fracture vertical propagation capability, and multi-cluster fracture balanced development capability, respectively:

[0093] F net =0.5396ρ nf +0.297Δσ+0.1634B rit ;

[0094] F cc =0.5195μ+0.2598Δσ in +0.14ΔT in +0.0808Δσ v ;

[0095] f jh =0.5584Δσ h +0.3196ΔK+0.122YM;

[0096] In the formula, ρ nf , Δσ, B rit μ, Δσ in ΔT in , Δσv , Δσ h ΔK and YM correspond to A, B, C, D, E, F, G, H, I, and J respectively.

[0097] S3. To achieve objective weighting of the three core capability indicators in the comprehensive compressibility model, representative response parameters of the three indicators [fracture density within a segment (number of fractures / segment length), fracture height, and multi-cluster balanced opening degree (shortest fracture length within a segment / average fracture length within a segment)] were used as comparison sequences. The cumulative oil production over 10 years was selected as a reference sequence. Sixteen experimental schemes were designed using grey relational analysis to calculate the correlation between the three capability indicators and production capacity, obtaining the objective weight distribution of each capability indicator. The correlation calculation results are as follows: Figure 6 The design scheme is as follows:

[0098] Table 6. Cumulative oil production over three years under different indicators

[0099]

[0100] S4. Combining the indicator models obtained in S23 with the weight allocation in S3, determine the compressibility evaluation model:

[0101] FI = 0.5396F net +0.297F cc +0.163.4F jh ;

[0102] Therefore, the compressibility index of the entire FY well section was calculated, as shown in Table 7:

[0103] Table 7 Calculation results of the compressibility index of FY well

[0104]

[0105]

[0106] The field data (Table 5) was used for verification. The density of microseismic event points (the ratio of the number of microseismic event points to the volume of the modified area) was used to compare the planar crack network construction capacity index; the crack height was used to compare the hydraulic crack vertical propagation capacity index. Figure 8The results of electromagnetic and microseismic monitoring for different clusters are presented. The position of the spheres indicates the location of microseismic events, and the size of the spheres corresponds to the energy or magnitude released by the microseismic events. The polygonal region at the bottom of the spheres represents the fracture morphology detected by wide-area electromagnetic monitoring. The cluster equilibrium fluid inflow rate (shortest fracture length in the segment / average fracture length in the segment) obtained from wide-area electromagnetic monitoring can be used to calculate the cluster equilibrium fluid inflow rate, which is used to compare the multi-cluster fracture equilibrium development capacity index. To further improve the accuracy of the evaluation, this embodiment proposes the concept of "Effective Modification Volume (E-SRV)," which is defined as the volume of microseismic event points in a certain area exceeding a specific threshold (e.g., 0.03 event points / m²). 3 Only when this was considered that a complex network of fractures had formed in the area was it included. Figure 7 E-SRV calculation in the context of this.

[0107] To assess the agreement between the calculated results and the field monitoring data, each capability index and its corresponding field monitoring parameter were plotted on the same coordinate system, with the calculated index on the x-axis and the monitoring result on the y-axis. A linear fit was then performed, and the correlation coefficient R0 was used as the plotting result. 2 The degree of matching was measured. Specifically, the comparison and fitting results of the planar crack network construction capability are shown below. Figure 9 The comparison and fitting results of the vertical propagation capacity of hydraulic fractures are shown in [reference needed]. Figure 10 The comparison and fitting results of the balanced development capacity of multiple crack clusters are shown in [reference needed]. Figure 11 Comparison and fitting results of compressibility indices are shown in [link to relevant documentation]. Figure 12 .

[0108] The application results of this embodiment in the corresponding fractured section of well FY show that the index calculated by the compressibility evaluation method provided by this invention has a consistency of ≥80% with the field fracture monitoring results, demonstrating strong field application value. This indicates that this method can provide a new decision-making approach for well and layer selection in fracturing of continental shale oil and gas reservoirs.

[0109] Therefore, this invention provides a weight calculation method for a continental shale reservoir compressibility evaluation model. Based on geological modeling, it conducts fracturing simulation and production prediction, and scientifically calculates the weights of multidimensional indicators by combining the analytic hierarchy process (AHP) and grey relational analysis. This method avoids the subjectivity of traditional empirical weighting methods and reduces reliance on high-cost field monitoring data such as microseismic data. At the same time, it establishes a quantitative relationship between evaluation indicators and actual production capacity, making the evaluation results more predictive and providing engineering guidance. This helps to accurately identify high-quality stimulation targets and improve the targeting and effectiveness of fracturing design.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for calculating the weights of a continental shale reservoir compressibility evaluation model, characterized in that, Includes the following steps: S1. Collect multi-dimensional geological features of target blocks of continental shale reservoirs and construct a three-dimensional geological model based on the geological features; The key influencing factors of hydraulic fracturing performance were identified, including the planar fracture network formation capacity, the vertical propagation capacity of hydraulic fractures, and the balanced development capacity of multiple fracture clusters. S2. Based on the three-dimensional geological model, orthogonal experiments, numerical fracturing simulation, range analysis and analytic hierarchy process were carried out sequentially to calculate the key influencing factors, and a weighted calculation model for each capability index was established to obtain the corresponding capability index. The specific steps of S2 are as follows: S21. Orthogonal Experiment and Numerical Fracturing Simulation: Based on key influencing factors, a three-level orthogonal experimental scheme is designed for each capability index, and numerical fracturing simulation is performed to output multiple simulation response parameters under each scheme. In S21, the simulation response parameter corresponding to the planar fracture network construction capability index is the intra-segment fracture density; the simulation response parameter corresponding to the hydraulic fracture vertical propagation capability index is the fracture height; and the simulation response parameter corresponding to the multi-cluster fracture balanced development capability index is the multi-cluster balanced opening degree. S22. Range Analysis: Calculate the average value of the simulated response parameters at different levels, perform range analysis on the average value, and output the range value of each key influencing factor. S23. Analytic Hierarchy Process (AHP) Calculation: The range values ​​are processed using the AHP to obtain the weight value of each key influencing factor; the specific process of processing the range values ​​using the AHP in S23 is as follows: (1) Determine the ratio of the range values ​​of different key influencing factors in the same capability index, and construct the initial judgment matrix after scaling the ratio; (2) Perform a consistency check on the initial judgment matrix. When the consistency check result is greater than or equal to 0.1, return to step (1) to adjust the initial judgment matrix and perform the consistency check again until the consistency check result is less than 0.1, and obtain a judgment matrix that meets the consistency requirements. The consistency test results are expressed as follows: ； In the formula, This represents the average random consistency index. Represents the consistency coefficient. ; It is the largest eigenvalue. The order of the initial judgment matrix; (3) Using the eigenvalue method, calculate the eigenvector corresponding to the largest eigenvalue of the judgment matrix, and normalize the eigenvector to obtain the weight value; S24. Construct a formula for calculating the capability index based on weight values ​​and key influencing factors; S3. Use grey relational analysis to calculate the grey relational degree between each capacity indicator and production capacity, and determine the relative weight of the capacity indicators. Among them, the experimental samples of the grey relational analysis method are derived from the field fracturing response or the numerical fracturing simulation in S2; S4. Combining the results of S2 and S3, a compressibility evaluation model is established, and the compressibility index of continental shale reservoirs is output.

2. The method for calculating the weights of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that: In S1, the multidimensional geological features include lithological assemblage, sedimentary facies type, bedding structure and natural fracture development characteristics; Key influencing factors for the planar fracture network construction capability index include natural fracture density, brittleness index, and horizontal stress difference; key influencing factors for the vertical propagation capability index of hydraulic fractures include friction coefficient, shear strength, tensile strength difference, and vertical stress difference; key influencing factors for the balanced development capability index of multiple fracture clusters include minimum horizontal stress difference between clusters, fracture toughness difference, and Young's modulus.

3. The method for calculating the weights of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that, In S24, the formula for calculating the capability index corresponding to the planar seam mesh construction capability index is expressed as follows: ； In the formula, This is the index of planar seam mesh construction capability. The normalized density of natural cracks. The normalized horizontal stress difference This is the normalized fragility index; , , In order , , The corresponding weight value.

4. The method for calculating the weights of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that: In S3, the reference sequence for grey relational analysis is: The annual cumulative oil production is compared with the simulated response parameters of three capacity indicators.

5. The method for calculating the weights of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that, In S3, the formula for calculating the grey relational degree is: ； In the formula, Indicates the first Grey correlation of individual capability indicators ; The total number of test samples, Indicates the first Grey relational coefficients of individual capability indicators ; The formula for calculating the grey relational coefficient is: ； In the formula, To be the minimum difference, The resolution coefficient, For the maximum difference, This is the absolute value of the difference.

6. The method for calculating the weights of a continental shale reservoir compressibility evaluation model according to claim 5, characterized in that, In S3, the formula for calculating the relative weights of the capability indicators is as follows: ； In the formula, Indicates the first The relative weights of each capability indicator.

7. The method for calculating the weights of a continental shale reservoir compressibility evaluation model according to claim 6, characterized in that, The compressibility evaluation model in S4 is expressed as follows: ； In the formula, The compressibility index, The vertical propagation capacity index of hydraulic fractures. It is an index representing the ability to achieve balanced development of multiple crack clusters.

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