Continental facies shale reservoir compressibility evaluation model weight calculation method

Abstract

The invention discloses a continental facies shale reservoir compressibility evaluation model weight calculation method, and belongs to the technical field of hydraulic fracturing. By fusing geological modeling and fracturing simulation results and introducing weight calculation methods such as an analytic hierarchy process and a grey correlation method, the weight of each index is scientifically determined, the degree of dependence on field monitoring data is reduced, meanwhile, quantitative correlation with actual productivity is established, contribution of three types of capability indexes to the productivity is quantified, and the method is suitable for mass production. And finally, a compressibility comprehensive evaluation model with engineering guidance value is determined, the fracturing transformation potential of the reservoir can be represented more accurately, and a scientific theoretical basis is provided for reservoir optimization and fracturing design.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic fracturing, and in particular to a method for calculating the weight of a continental shale reservoir compressibility evaluation model. Background Art

[0002] Continental shale reservoirs are characterized by low brittleness, strong heterogeneity, and complex geostress conditions. This not only limits the vertical extension of hydraulic fractures but also hinders fracture branching and connectivity, reducing the probability of forming complex fracture networks. The currently widely used staged multi-cluster fracturing technology for horizontal wells is susceptible to reservoir heterogeneity in practice, resulting in ineffective activation of some clusters and uneven fracture expansion, thus compromising the overall stimulation effect. Therefore, developing a comprehensive, multi-factor, and highly adaptable compressibility evaluation method is of great engineering significance for reservoir optimization and fracturing design.

[0003] In the prior art, the invention patent with publication number CN119378785A, entitled "A Method and System for Evaluating the Compressibility of Continental Shale Reservoirs," proposes a method for evaluating the compressibility of continental shale reservoirs based on a multivariate linear regression approach to calculate weight coefficients. However, this method is highly dependent on field monitoring data such as microseismicity and output contribution rate, and has limitations. The invention patent with publication number CN119393108A, entitled "A Method for Optimizing Volume Fracturing Technology for Continental Shales," optimizes the volume fracturing process by comparing the fracture complexity index, the interlayer extension index, and the equilibrium extension index. However, the relative weights of the three indices on the reconstruction effect are not clearly defined, making it difficult to guide actual compressibility evaluation.

[0004] Although some studies on the compressibility of continental shales have introduced multidimensional indicators such as "fracture complexity", "vertical expansion capacity" and "multi-cluster balance" (such as the invention patents with publication numbers CN119378785A and CN119393108A), the following problems are common: First, most of them use linear superposition or empirical weighting methods to determine indicator weights, which are highly subjective and difficult to adapt to the requirements of fracturing design under different geological conditions; second, there is a lack of quantitative correlation mechanism between the indicator system and actual production capacity, and it cannot fully reflect the fracturing response capacity and transformation potential of the reservoir.

[0005] Therefore, how to provide a weight calculation method for the continental shale reservoir compressibility evaluation model that can scientifically determine the weights of each indicator, has low reliance on field monitoring data, and establishes a quantitative correlation with actual production capacity is an urgent problem that technicians in this field need to solve. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for calculating the weight of a continental shale reservoir compressibility evaluation model to solve the problems in the background technology.

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

[0008] S1. Collect multi-dimensional geological characteristics of the target block of the continental shale reservoir and construct a three-dimensional geological model based on the geological characteristics;

[0009] Determine the capability indicators that affect hydraulic fracturing results, including the planar fracture network construction capability, the vertical expansion capability of hydraulic fractures, and the balanced development capability of multiple fracture clusters. Each capability indicator includes multiple key influencing factors.

[0010] S2. Based on the three-dimensional geological model, orthogonal tests, numerical fracturing simulations, range analysis, and analytic hierarchy process calculations are performed on key influencing factors in turn to establish a weighted calculation model for each capability indicator and obtain the corresponding capability index;

[0011] S3. Use grey correlation analysis to calculate the grey correlation between each capability indicator and production capacity, and determine the relative weight of the capability indicator;

[0012] Among them, the experimental samples of the grey correlation 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 to output the compressibility index of the continental shale reservoir.

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

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

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

[0017] S21. Orthogonal Experiment and Numerical Fracturing Simulation: Based on key influencing factors, a three-level orthogonal experimental scheme corresponding to each capability indicator is designed, numerical fracturing simulation is performed, and multiple simulation response parameters under each scheme are output;

[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. Calculation of AHP: Use AHP to process the extreme value and 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 simulation response parameter corresponding to the plane fracture network construction capability index is the intra-segment fracture density, the simulation response parameter corresponding to the hydraulic fracture vertical expansion 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.

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

[0023] Table 1 Three-factor orthogonal test table of plane seam network construction capability index

[0024]

[0025] Among them, A is the normalized natural fracture density, dimensionless; B is the normalized horizontal stress difference, dimensionless; C is the normalized brittleness index, dimensionless; Y is the representation of the intra-segment fracture density; A1 represents a low level of natural fracture density, B2 represents a medium level of horizontal stress difference, and C3 represents a high level of brittleness index.

[0026] Preferably, in said S22, the range analysis table of the plane seam network construction capability index is shown in Table 2;

[0027] Table 2 Range analysis table of plane seam network 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 S23, the specific process of using the hierarchical analysis method to process the extreme difference value is:

[0031] (1) Determine the ratio of the extreme values ​​of different key influencing factors in the same capability indicator, convert the ratio into a scale of 1 to 9, and then construct the initial judgment matrix;

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

[0033] Table 3 Initial judgment matrix of plane seam network 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 a consistency check again until the consistency check result is less than 0.1, thereby obtaining a judgment matrix that meets the consistency requirements.

[0036] The consistency test result is expressed as:

[0037]

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

[0039] (3) The eigenvalue method is used to calculate the eigenvalue corresponding to the maximum eigenvalue of the judgment matrix, and the eigenvalue is normalized to obtain the weight value.

[0040] Preferably, in said S24, the calculation formulas for the capability index corresponding to the plane fracture network construction capability index, the hydraulic fracture vertical expansion capability index, and the multi-cluster fracture balanced development capability 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] Where, F net is the plane seam network construction capability index, F cc is the vertical expansion capacity index of hydraulic fractures, F jhis the index of the ability of balanced development of multiple clusters of cracks, A is the normalized natural crack density, dimensionless; B is the normalized horizontal stress difference, dimensionless; C is the normalized brittleness index, dimensionless; D is the normalized interlayer interface friction coefficient, dimensionless; E is the normalized interlayer interface 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; w A 、w B 、w C 、w D 、w E 、w F 、w G 、w H 、w I 、w J The weight values ​​corresponding to A, B, C, D, E, F, G, H, I, and J are shown in order.

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

[0046] Preferably, the experimental design scheme of the grey relational analysis method is shown in Table 4;

[0047] Table 4 Experimental design of grey relational analysis method

[0048]

[0049] Among them, X1 is the fracture density within the segment, X2 is the fracture height, X3 is the degree of balanced opening of multiple clusters, which is dimensionless; X0 is the cumulative oil production in M ​​years, 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 S3, the calculation formula of grey relational degree is:

[0051]

[0052] Where, γ i Represents the grey relational degree of the i-th capability index, i = 1, 2, 3; N is the total number of test samples, ξ i (k) represents the grey relational coefficient of the i-th capability index, k = 1, ..., N;

[0053] The calculation formula of grey relational coefficient is:

[0054]

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

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

[0057] Δ i (k)=|X0(k)-X i (k)|;

[0058] The calculation formulas for the maximum difference and the minimum difference are:

[0059] Δmax=maxΔ i (k);

[0060] Δmin=minΔ i (k).

[0061] Preferably, in S3, the calculation formula for the relative weight of the capability index is:

[0062]

[0063] Where w i Represents the relative weight of the i-th capability indicator.

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

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

[0066] Where FI is the compressibility index, F cc is the vertical expansion capacity index of hydraulic fractures, F jh It is the index of the ability of balanced development of multiple clusters of cracks.

[0067] Therefore, the present invention provides a weight calculation method for the continental shale reservoir compressibility evaluation model, which scientifically integrates geological modeling and numerical simulation technology, combines the hierarchical analysis method and the grey correlation method to construct a weight calculation system, thereby significantly improving the objectivity and practicality of the continental shale reservoir compressibility evaluation: based on geological modeling, by carrying out fracturing simulation and production capacity prediction, and integrating the hierarchical analysis method and the grey correlation method to scientifically calculate the weights of multidimensional indicators, it effectively avoids the subjectivity of traditional experience weighting, and at the same time uses the simulation results to replace the actual monitoring needs, significantly reducing the dependence on high-cost field monitoring data (such as microseismic); at the same time, it innovatively establishes a quantitative correlation model between capacity indicators and actual production capacity, so that the evaluation results not only reflect the reservoir transformation potential, but also can directly predict the 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 is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of a flow chart of an embodiment of the present invention;

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

[0071] Figure 3 Comparison diagrams of hydraulic fracture network expansion under different horizontal stress difference conditions in the orthogonal test of the embodiment of the present invention, where (a) is a horizontal stress difference of 2 MPa, (b) is a horizontal stress difference of 4 MPa, and (c) is a horizontal stress difference of 8 MPa;

[0072] Figure 4 Comparison of vertical expansion of hydraulic fractures under different interlaminar stress difference conditions in the orthogonal test of the embodiment of the present invention, where the interlaminar stress difference in (a) is 0 MPa, the interlaminar stress difference in (b) is 2 MPa, and the interlaminar stress difference in (c) is 4 MPa;

[0073] Figure 5 Comparison of the balanced expansion of hydraulic fractures under different Young's modulus conditions in the orthogonal test of the embodiment of the present invention, where (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 gray relational analysis result of the embodiment of the present invention is shown in FIG.

[0075] Figure 7 Schematic diagram of a complex fracture 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 the electromagnetic monitoring and microseismic monitoring results according to an embodiment of the present invention;

[0077] Figure 9 The verification results of the plane fracture network construction capability of the embodiment of the present invention are shown in FIG. (a) is a comparison chart of the plane fracture network construction capability, and (b) is a fitting result of the plane fracture network construction capability.

[0078] Figure 10 The verification results of the vertical expansion capacity of hydraulic fractures in the embodiment of the present invention are shown in Figure 1, where (a) is a comparison chart of the vertical expansion capacity of hydraulic fractures, and (b) is a fitting result of the vertical expansion capacity of hydraulic fractures;

[0079] Figure 11 The verification results of the balanced development capability of multiple clusters of cracks in the embodiment of the present invention are shown in Figure 1, where (a) is a comparison chart of the balanced development capability of multiple clusters of cracks, and (b) is a fitting result of the balanced development capability of multiple clusters of cracks.

[0080] Figure 12 1 is the verification result of the compressibility index of the embodiment 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 DESCRIPTION

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

[0082] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0083] Example

[0084] like Figure 1 As shown, the present invention provides a method for calculating the weight of a continental shale reservoir compressibility evaluation model. Taking a continental shale horizontal 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 undergo 22 fracturing stages, but casing deformation was discovered after the seventh stage, resulting in the completion of only 16 fracturing stages (1-7, 14-22). Microseismic and wide-area electromagnetic methods were used for fracture monitoring during the operation. The division of each fracturing stage and the monitoring results are shown in Table 5.

[0086] Table 5 Statistics of the FY Well segmentation and on-site fracture monitoring results

[0087]

[0088] S1. Based on the geological data of the block, such as well logging, seismic and core data, a three-dimensional geological model (such as Figure 2 The model takes into account geological parameters such as reservoir thickness, lithology distribution, fault structure, and interlayer interface to ensure that the fracturing simulation has a realistic geological background.

[0089] S2. Based on the three-dimensional geological model, orthogonal tests, numerical fracturing simulations, range analysis, and analytic hierarchy process calculations were performed on the key influencing factors in turn. A weighted calculation model for each capability indicator was established to obtain the corresponding capability index, specifically:

[0090] S21. Based on the established 3D geological model, key influencing factors such as natural fracture density, horizontal stress difference, and brittleness index were selected to design a three-level (low, medium, and high) orthogonal test scheme. Figure 3-Figure 5 As shown in the figure, 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 capacity indicators respectively.

[0091] S22. Based on the simulation results, a range analysis is performed to determine the importance ranking of the influencing factors in the plane fracture network construction capability, the vertical expansion capability of hydraulic fractures, and the balanced development capability of multiple cluster fractures.

[0092] S23. Based on the range ratio, a judgment matrix was constructed. The analytic hierarchy process was applied to calculate the relative weight of each factor in each capability indicator. The evaluation models for the plane fracture network construction capability, the vertical expansion capability of hydraulic fractures, and the balanced development capability of multiple cluster fractures were then determined 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] Where, 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, the 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, and the 10-year cumulative oil production was selected as the reference sequence. Using the grey correlation analysis method, 16 experimental schemes were designed to calculate the correlation between the three types of capability indicators and production capacity, and the objective weight distribution of each capability indicator was obtained. The correlation calculation results are shown in Figure 2. Figure 6 , the scheme design is as follows:

[0098] Table 6 Three-year cumulative oil production under different indicators

[0099]

[0100] S4. Combine the indicator models obtained in S23 with the weight distribution in S3 to determine the compressibility evaluation model:

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

[0102] Thus, the calculation results of the compressibility index of the entire well section of Well FY are obtained, as shown in Table 7:

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

[0104]

[0105]

[0106] Field data (Table 5) were used for verification. The microseismic event point density (the ratio of the number of microseismic event points to the reconstruction volume) was used to compare the plane fracture network construction capability index; the fracture height was used to compare the hydraulic fracture vertical expansion capability index; Figure 8The electromagnetic monitoring and microseismic monitoring results of different clusters are displayed, in which the position of the ball indicates the location of the microseismic event, the size of the ball corresponds to the energy or magnitude released by the microseismic event, and the broken line area at the bottom of the ball is the fracture morphology obtained by wide-area electromagnetic monitoring; the cluster hole liquid inflow wave length obtained by wide-area electromagnetic monitoring can be used to calculate the cluster balanced liquid inflow (the shortest fracture length in the segment / the average fracture length in the segment), which is used to compare the balanced development capacity index of multiple cluster fractures. In order to further improve the accuracy of the evaluation, this embodiment proposes the concept of "effective reconstruction volume (E-SRV)", which is only used when the density of microseismic event points in a certain area is greater than a specific threshold (such as 0.03 event points / m 3 ) is considered to have formed a complex fracture network in the region and is included in the Figure 7 E-SRV calculation in .

[0107] In order to evaluate the consistency between the calculated results and the on-site monitoring data, each capability index and the corresponding on-site monitoring parameter were plotted in the same coordinate system, with the calculated index as the horizontal axis and the monitoring result as the vertical axis, and a linear fit was performed, with the correlation coefficient R 2 Measure the degree of matching. Among them: the comparison and fitting results of the plane crack network construction ability are shown in Figure 9 ; The comparison and fitting results of the vertical expansion capacity of hydraulic fractures are shown in Figure 10 The comparison and fitting results of the balanced development ability of multiple clusters of fractures are shown in Figure 11 ; Comparison and fitting results of compressibility index are shown in Figure 12 .

[0108] Application results of this example in the corresponding fracturing section of Well FY show that the index calculated by the compressibility evaluation method provided by the present invention agrees with on-site fracture monitoring results by ≥80%, demonstrating strong field application value. This demonstrates that this method provides a new decision-making approach for well and layer selection for fracturing in continental shale oil and gas reservoirs.

[0109] Therefore, the present invention provides a weight calculation method for the terrestrial shale reservoir compressibility evaluation model. Based on geological modeling, it carries out fracturing simulation and production capacity prediction, and combines the hierarchical analysis method and the grey correlation method to scientifically calculate the weights of multidimensional indicators. It avoids the subjectivity of traditional empirical weighting methods and reduces the dependence on high-cost field monitoring data such as microseismic data. At the same time, it establishes a quantitative relationship between the evaluation indicators and the actual production capacity, making the evaluation results more predictive and engineering guiding, which helps to accurately identify high-quality transformation targets and improve the pertinence 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 rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements 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 weight of a continental shale reservoir compressibility evaluation model, characterized in that: The following steps are involved: S1. Collect multi-dimensional geological characteristics of the target block of the continental shale reservoir and construct a three-dimensional geological model based on the geological characteristics; Determine the capability indicators and key influencing factors that affect hydraulic fracturing results; capability indicators include the capability indicator for plane fracture network construction, the capability indicator for vertical expansion of hydraulic fractures, and the capability indicator for balanced development of multiple fracture clusters; S2. Based on the three-dimensional geological model, orthogonal tests, numerical fracturing simulations, range analysis, and analytic hierarchy process calculations are performed on key influencing factors in turn to establish a weighted calculation model for each capability indicator and obtain the corresponding capability index; S3. Use grey correlation analysis to calculate the grey correlation between each capability indicator and production capacity, and determine the relative weight of the capability indicator; Among them, the experimental samples of the grey correlation 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 to output the compressibility index of the continental shale reservoir.

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

3. The method for calculating the weight of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that: The specific steps of S2 are: S21. Orthogonal Experiment and Numerical Fracturing Simulation: Based on key influencing factors, a three-level orthogonal experimental scheme corresponding to each capability indicator is designed, numerical fracturing simulation is performed, and multiple simulation response parameters under each scheme are output; 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. Calculation of AHP: Use AHP to process the extreme value and obtain the weight value of each key influencing factor; S24. Construct a capability index calculation formula based on weight values ​​and key influencing factors.

4. The method for calculating weights of a continental shale reservoir compressibility evaluation model according to claim 3, characterized in that: In S21, the simulation response parameter corresponding to the plane fracture network construction capability index is the intra-segment fracture density, the simulation response parameter corresponding to the hydraulic fracture vertical expansion 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.

5. The method for calculating the weight of a continental shale reservoir compressibility evaluation model according to claim 3, characterized in that: In S23, the specific process of using the hierarchical analysis method to process the extreme difference value is as follows: (1) Determine the ratio relationship of the extreme values ​​of different key influencing factors in the same capability indicator, convert the ratio relationship into a scale, and construct the initial judgment matrix; (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 a consistency check again until the consistency check result is less than 0.1, thereby obtaining a judgment matrix that meets the consistency requirements. The consistency test result is expressed as: In the formula, RI represents the average random consistency index, CI represents the consistency coefficient, λ max is the maximum eigenvalue, n is the order of the initial judgment matrix; (3) Using the eigenvalue method, the eigenvalue vector corresponding to the maximum eigenvalue of the judgment matrix is ​​calculated, and the eigenvalue vector is normalized to obtain the weight value.

6. The method for calculating the weight of a continental shale reservoir compressibility evaluation model according to claim 3, characterized in that: In S24, the capability index calculation formula corresponding to the plane seam network construction capability index is expressed as: F net =w A A+w B B+w C C; Where, F net is the plane fracture network construction capability index, A is the normalized natural fracture density, B is the normalized horizontal stress difference, and C is the normalized brittleness index; w A 、w B 、w C These are the weight values ​​corresponding to A, B, and C respectively.

7. The method for calculating weights of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that: In S3, the reference sequence of the grey correlation analysis method is the cumulative oil production over M years, and the comparison sequence is the simulated response parameters of the three capability indicators.

8. The method for calculating weights of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that: In S3, the calculation formula of grey relational degree is: Where, γ i Represents the grey relational degree of the i-th capability index, i = 1, 2, 3; N is the total number of test samples, ξ i (k) represents the grey relational coefficient of the i-th capability index, k = 1, ..., N; The calculation formula of grey relational coefficient is: Where Δmin is the minimum difference, ρ is the resolution coefficient, Δmax is the maximum difference, Δ i (k) is the absolute value of the difference.

9. The method for calculating weights of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that: In S3, the calculation formula for the relative weight of the capability index is: Where w i Represents the relative weight of the i-th capability indicator.

10. The method for calculating weights of a continental shale reservoir compressibility evaluation model according to claim 1, characterized in that: The compressibility evaluation model in S4 is expressed as: <h2 style=";text-align:left;direction:ltr">FI=w1F<h2 style=";text-align:left;direction:ltr"> net <h2 style=";text-align:left;direction:ltr"> +w2F<h2 style=";text-align:left;direction:ltr"> cc <h2 style=";text-align:left;direction:ltr"> +w3F<h2 style=";text-align:left;direction:ltr"> jh <h2 style=";text-align:left;direction:ltr"> ; Where FI is the compressibility index, F cc is the vertical expansion capacity index of hydraulic fractures, F jh It is the index of the ability of balanced development of multiple clusters of cracks.

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