A shale compressibility logging evaluation method based on game theory method

Abstract

A shale compressibility logging evaluation method based on a game theory method belongs to the field of unconventional oil and gas development and comprises the following steps: 1, preparing core data, carrying out experiments, obtaining rock mechanics parameters and experimental rock physical parameters; 2, establishing a rock mechanics parameter and experimental rock physical parameter relationship formula; 3, establishing a formation pore pressure prediction relationship formula based on logging rock physical parameters; 4, establishing a relationship between indoor and field logging rock physical parameters; 5, constructing a formation stress calculation model of a research block, and then obtaining related parameters; 6, normalizing the oil production intensity to obtain the geological mechanics main control factor of shale compressibility; 7, obtaining key characterization parameters and sorting of shale compressibility which are relatively independent; and 8, determining a shale compressibility index. The shale compressibility evaluation method considering the geological mechanics parameters of fracturing layers and barrier layers is established by cooperating multiple methods, and the shale compressibility evaluation method has good accuracy.

Description

Technical Field

[0001] This invention belongs to the field of unconventional oil and gas development, specifically relating to a well logging evaluation method for shale compressibility based on game theory. Background Technology

[0002] Current exploration and development practices demonstrate that volumetric fracturing is one of the key technologies for the efficient development of unconventional oil and gas resources. How to select the optimal fracturing intervals in vertical wells and how to divide the fracturing intervals in horizontal wells are major and critical issues faced in the volumetric fracturing of unconventional oil and gas reservoirs. Solving these problems is inseparable from the evaluation of reservoir compressibility. It is generally believed that good reservoir compressibility indicates higher brittleness, ease of fracturing, and easier formation of fracture networks, which is beneficial for the development of the reservoir. Currently, numerous reservoir compressibility evaluation methods have been proposed for unconventional oil and gas resources such as shale oil and gas and tight oil and gas. These methods each have their own advantages and disadvantages and have achieved certain application results in the field. However, for shale oil reservoirs with thin interbedded layers, the fracturing process is affected not only by the geomechanical parameters of the fracturing layer but also by the geomechanical parameters of the upper and lower interlayers. There is still no reasonable shale compressibility evaluation method that takes into account the geomechanical parameters of the fracturing layer and the interlayers. Summary of the Invention

[0003] The purpose of this invention is to propose a well logging evaluation method for shale compressibility based on game theory. This method combines multiple methods and utilizes up to 19 factors to establish a shale compressibility evaluation method that considers the geomechanical parameters of fractured layers and interlayers. This method has good accuracy.

[0004] The technical solution adopted in this invention is:

[0005] A well logging evaluation method for shale compressibility based on game theory includes the following steps:

[0006] Step 1: Prepare experimental core data, geological data, fracturing data, well logging data, and production data; perform lithological description on the obtained downhole core data and conduct rock physics-rock mechanics experiments; after processing the raw experimental data, obtain rock mechanics parameters and experimental rock physics parameters;

[0007] Step 2: Based on traditional evaluation methods for rock mechanics parameters, analyze the relationship between rock mechanics parameters and experimental rock physical parameters, determine the rock physical response law of rock mechanics parameters in the study block, and establish the mathematical relationship between rock mechanics parameters and experimental rock physical parameters.

[0008] Step 3: Based on the formation pressure test data of the study block, analyze the correlation between the effective formation stress (ignoring the influence of the pore elastic coefficient) and the logging rock physical parameters (logging rock density, logging P-wave velocity, logging S-wave velocity, natural gamma, spontaneous potential, neutron, shallow and deep resistivity, etc.), clarify the response relationship of the effective formation stress, and establish the formation pore pressure prediction formula based on the logging rock physical parameters in combination with the effective stress principle.

[0009] The formula is based on only some logging rock physics parameters. Other parameters, such as logging shear wave velocity and natural gamma, are only used to analyze the response relationship of effective formation stress.

[0010] Step 4: Establish the relationship between indoor experimental rock physical parameters and field logging rock physical parameters;

[0011] Step 5: Based on the hydraulic fracturing construction curve, under the constraints of drilling and fracturing engineering information, using the combined spring model (see formulas (1) and (2), the tectonic strain coefficient of the strata in the study block is obtained through mathematical and rock mechanics theory inversion analysis. Then, a geostress calculation model of the strata in the study block is constructed to obtain the minimum horizontal principal stress, maximum horizontal principal stress, and vertical stress of the study block. Furthermore, the difference in horizontal principal stress of the fracturing layer and the difference in horizontal principal stress between the interlayer and the fracturing layer are obtained (see formulas (3) and (4) respectively).

[0012]

[0013] Δσ=σ H -σ h (3)

[0014]

[0015] Where: μ is the Poisson's ratio of the rock; E is the Young's modulus of the rock, MPa; σ H、 σ h These represent the maximum and minimum horizontal principal stresses, respectively, in MPa; ε H ε h Here, ρ0 represents the tectonic strain coefficients along the directions of maximum and minimum principal stress, respectively; H0 is the logging initiation depth, in meters; ρ0(h) represents the density at depth h in the unlogged section, in g / cm2. 3 ρ(h) represents the logging density at depth h, in g / cm³. 3 g is the acceleration due to gravity, kg·m / s². 2 ; △σ represents the horizontal principal stress difference in the fracturing layer, MPa; σ h I σ h R These represent the minimum principal stresses of the interlayer and the fracturing layer, respectively, in MPa; Δσh IR The minimum principal stress difference between the interlayer and the fracturing layer, in MPa;

[0016] Step Six: The fracturing effect can be evaluated using the oil production intensity. The higher the value, the better the fracturing effect, and the lower the value, the worse the fracturing effect. To avoid the influence of differences in the amount of sand added in different fracturing wells, the oil production intensity is normalized to obtain the normalized oil production intensity (daily oil production per meter / amount of sand added per meter).

[0017] Geomechanical parameters of each segment were extracted in the perforation fracturing section, and then the relationship between the extracted geomechanical parameters and the normalized oil recovery intensity was analyzed to qualitatively understand the influence of the geomechanical parameters on the fracturing effect.

[0018] Grey relational analysis was used to obtain the correlation degree of the influence of various geological and mechanical parameters on the fracturing effect of shale, and the main geomechanical controlling factors of shale compressibility were obtained based on the standard that the correlation degree value is greater than 0.65.

[0019] Step 7: Utilize the relationship between Peason correlation coefficient and geomechanical parameters to analyze the correlation among the main geomechanical controlling factors of shale compressibility, and further obtain the key characterization parameters of shale compressibility that are relatively independent and their ranking.

[0020] Step 8: Normalize the relatively independent key characterization parameter values ​​of different dimensions using the extreme value transformation method. Positive indices are normalized positively, and negative indices are normalized negatively. Then, determine the weighting coefficients for the influence of different factors on fracturing compressibility. Finally, weight the normalized values ​​with the weighting coefficients to obtain the shale compressibility index. The mathematical model is as follows:

[0021]

[0022] In the formula: FI is the shale compressibility index, which is dimensionless; S i The standardized values ​​of the key characterization parameters are dimensionless; w i The weight coefficients of the key characterization parameters are summed to 1; n is the number of parameters.

[0023] Furthermore, the rock physics-rock mechanics experiments include density testing, acoustic wave testing, uniaxial or triaxial compression testing, tensile strength testing, and fracture toughness testing; the rock mechanics parameters include compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, brittleness index, and fracture toughness; the experimental rock physics parameters include experimentally tested longitudinal wave velocity, experimentally tested transverse wave velocity, and experimentally tested density.

[0024] Furthermore, in step two, a mathematical relationship is established between some rock mechanical parameters (other parameters are obtained indirectly from these parameters or from other parameters) and experimental rock physical parameters, as shown in formula (6):

[0025]

[0026] Where: σ c The uniaxial compressive strength is expressed in MPa; S t For tensile strength, MPa; K ic Type I fracture toughness, MPa·m 0.5 ;DEN M To test the density of the rock in experiments, g / cm³ 3 V pM For experimental testing of longitudinal wave velocity, m / s.

[0027] Furthermore, in step three, the established prediction relationship is shown in formula (7):

[0028]

[0029] Where: σ v For vertical stress, MPa; σ e Effective stress, MPa; p p DEN represents formation pore pressure, in MPa. L For logging rock density, g / cm³ 3 V pL The value is the longitudinal wave velocity in well logging, in m / s.

[0030] Furthermore, in step four, the established relational expression is represented as follows:

[0031] DEN M =1.3064×DEN L -0.8763 (8)

[0032] V pM =1.0234×V pL -126.01 (9)

[0033] The parameters in the formula are the same as those mentioned above.

[0034] Furthermore, in step six, the geomechanical controlling factors of shale compressibility include the controlling factors of the fracturing layer and the controlling factors of the pressure barrier. The controlling factors of the fracturing layer include the brittleness index, Young's modulus, minimum horizontal principal stress, difference in horizontal principal stress, and tensile strength. The controlling factors of the pressure barrier include the difference in minimum horizontal principal stress between the barrier and the fracturing layer, the ratio of Young's modulus between the barrier and the fracturing layer, and the ratio of tensile strength between the barrier and the fracturing layer.

[0035] Furthermore, in step seven, the relatively independent key characterization parameters and their order are: brittleness index of the fracturing layer, minimum horizontal principal stress difference between the interlayer and the fracturing layer, minimum horizontal principal stress of the fracturing layer, horizontal stress difference of the fracturing layer, and tensile strength of the fracturing layer.

[0036] Furthermore, the positive indices include the brittleness index of the fracturing layer and the difference in the minimum horizontal principal stress between the interlayer and the fracturing layer, which are normalized using formula (10). The negative indices include the minimum horizontal principal stress of the fracturing layer, the horizontal stress difference of the fracturing layer, and the tensile strength of the fracturing layer, which are normalized using formula (11).

[0037]

[0038]

[0039] In the formula: The normalized fracturing layer brittleness index; The normalized minimum principal stress difference between the interlayer and the fracturing layer; The normalized horizontal principal stress difference of the fracturing layer; The normalized minimum principal stress; The normalized tensile strength is denoted by ; max and min represent the maximum and minimum values ​​of this type of parameter in the study area, respectively.

[0040] Furthermore, in step nine, the weighting coefficients for the influence of different factors on fracturing capability are determined using the following method:

[0041] The weight vectors of each key representation parameter are calculated based on the theory of the Analytic Hierarchy Process (AHP), i.e., the subjective weight coefficients. The weight vectors of each key representation parameter are determined using the information entropy method, i.e., the objective weight coefficients. Based on this, according to the idea of ​​game theory, the subjective weight coefficients and objective weight coefficients of each key representation parameter are considered together to obtain the comprehensive weight coefficients.

[0042] Based on the obtained comprehensive weighting coefficients, the expression for the compressibility index of the shale in the study block is as follows:

[0043]

[0044] The beneficial effects of this invention are:

[0045] This invention establishes a well logging prediction method for shale formation rock mechanics parameters using mathematical statistics; a well logging prediction method for shale formation pore pressure using the effective stress method; and a well logging prediction method for shale formation geostress using a spring combination model under the constraints of multi-source engineering information such as drilling and fracturing. It also uses grey relational analysis to obtain the main geomechanical control factors of shale compressibility, and obtains and ranks the relatively independent key characterization parameters of shale compressibility based on Peason correlation coefficient and correlation analysis between geomechanical parameters. Furthermore, it uses the analytic hierarchy process (AHP) to obtain the subjective weights of the key parameters of shale compressibility, the entropy weight method to obtain the objective weights of the key parameters of shale compressibility, and game theory to obtain the comprehensive weights of the constituent elements of shale compressibility. Through the synergy of multiple methods and utilizing up to 19 factors, a shale compressibility evaluation method considering the geomechanical parameters of fracturing layers and interlayers is established, demonstrating good accuracy. Attached Figure Description

[0046] Figure 1 This is a diagram showing the results of the main controlling factors obtained based on grey relational analysis.

[0047] Figure 2 The results are based on the analysis of key characterization parameters according to the Pearson correlation coefficient;

[0048] Figure 3 The relationship between compressibility index and normalized oil recovery intensity;

[0049] Figure 4 A cross-sectional view of the compressibility index of a formation in a certain well;

[0050] Figure 5 Well logging evaluation map for studying the compressibility of formations. Detailed Implementation

[0051] A well logging evaluation method for shale compressibility based on game theory includes the following steps:

[0052] Step 1: Prepare experimental core data, geological data, fracturing data, well logging data, and production data;

[0053] Step 2: Lithological description of the acquired downhole core data and rock physics-rock mechanics experiments, including density testing, acoustic wave testing, uniaxial or triaxial compression testing, tensile strength testing, and fracture toughness testing; after processing the raw experimental data, rock mechanics parameters and experimental rock physics parameters are obtained.

[0054] The rock mechanical parameters include compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, brittleness index, and fracture toughness; the experimental rock physical parameters include experimentally tested longitudinal wave velocity, experimentally tested transverse wave velocity, and experimentally tested density.

[0055] Step 3: Based on the traditional evaluation method of rock mechanics parameters, analyze the relationship between rock mechanics parameters and experimental rock physical parameters, determine the rock physical response law of rock mechanics parameters in the study block, and on this basis, establish the mathematical relationship between a part of the rock mechanics parameters (other parameters are obtained indirectly from these parameters or from other parameters) and the experimentally tested longitudinal wave velocity and experimentally tested density, as shown in formula (1):

[0056]

[0057] Where: σ c The uniaxial compressive strength is expressed in MPa; S t For tensile strength, MPa; K ic Type I fracture toughness, MPa·m 0.5 ;DEN M To test the density of the rock in experiments, g / cm³ 3 V pM For experimental testing of P-wave velocity, m / s;

[0058] Step 4: Based on the formation pressure test data of the study block, the correlation between formation effective stress (ignoring the influence of pore elastic coefficient) and logging rock physical parameters (logging rock density, logging P-wave velocity, logging S-wave velocity, natural gamma, spontaneous potential, neutron, shallow and deep resistivity, etc.) was analyzed, clarifying the response relationship of formation effective stress. Combining the effective stress principle, a formation pore pressure prediction formula based on some logging rock physical parameters (logging S-wave velocity, natural gamma, and other logging rock physical parameters are only used to analyze the response relationship of formation effective stress) was established, as shown in formula (2):

[0059]

[0060] Where: σ v For vertical stress, MPa; σ e Effective stress, MPa; p p Formation pore pressure, MPa;

[0061] Step 5: Establish the relationship between indoor experimental rock physical parameters and field logging rock physical parameters, expressed as:

[0062] DEN M =1.3064×DEN L -0.8763 (3)

[0063] V pM =1.0234×V pL -126.01 (4)

[0064] Where: DENM To test the density of the rock in experiments, g / cm³ 3 ;DEN L For logging rock density, g / cm³ 3 V pM For experimental testing of P-wave velocity, m / s; V pL The value is the P-wave velocity in well logging, in m / s;

[0065] Step 6: Based on the hydraulic fracturing construction curve, under the constraints of drilling and fracturing engineering information, using the combined spring model (see formulas (5) and (6), the tectonic strain coefficients of the strata in the study block are obtained through mathematical and rock mechanics theory inversion analysis (as shown in Table 1). Then, a geostress calculation model for the strata in the study block is constructed to obtain the minimum horizontal principal stress, maximum horizontal principal stress, and vertical stress of the study block. Furthermore, the difference in horizontal principal stress of the fracturing layer and the difference in horizontal principal stress between the interlayer and the fracturing layer are obtained, as shown in formulas (7) and (8), respectively.

[0066]

[0067] Where: μ is the Poisson's ratio of the rock; E is the Young's modulus of the rock, MPa; σ H、 σ h These represent the maximum and minimum horizontal principal stresses, respectively, in MPa; ε H ε h Here, ρ0 represents the tectonic strain coefficients along the directions of maximum and minimum principal stress, respectively; H0 is the logging initiation depth, in meters; ρ0(h) represents the density at depth h in the unlogged section, in g / cm2. 3 ρ(h) represents the logging density at depth h, in g / cm³. 3 g is the acceleration due to gravity, kg·m / s². 2 ; △σ represents the horizontal principal stress difference in the fracturing layer, MPa; σ h I σ h R These represent the minimum principal stresses of the interlayer and the fracturing layer, respectively, in MPa; Δσ h IR The minimum principal stress difference between the interlayer and the fracturing layer, in MPa;

[0068] Table 1. Structural strain coefficients of the work area

[0069]

[0070] Step 7: The fracturing effect can be evaluated using the oil production intensity. The higher the value, the better the fracturing effect, and the lower the value, the worse the fracturing effect. To avoid the influence of differences in the amount of sand added in different fracturing wells, the oil production intensity is normalized to obtain the normalized oil production intensity (daily oil production per meter / amount of sand added per meter).

[0071] Geomechanical parameters were extracted from each perforated fracturing section, totaling 19 parameters. The relationship between the extracted geomechanical parameters and the normalized oil recovery intensity was then analyzed to qualitatively understand the impact of the geomechanical parameters on the fracturing effect.

[0072] Grey relational analysis was used to obtain the correlation degree between the influence of various geological and mechanical parameters on shale fracturing effect. Based on the standard that the correlation degree value is greater than 0.65, the main geomechanical controlling factors of shale compressibility were obtained. Figure 1 The main controlling factors of the fracturing layer and the main controlling factors of the pressure barrier layer are included. The main controlling factors of the fracturing layer include the brittleness index, Young's modulus, minimum horizontal principal stress, difference between horizontal principal stresses, and tensile strength. The main controlling factors of the pressure barrier layer include the difference between the minimum horizontal principal stresses of the barrier layer and the fracturing layer, the ratio of Young's modulus of the barrier layer and the fracturing layer, and the ratio of tensile strength of the barrier layer and the fracturing layer.

[0073] exist Figure 1 Chinese: B R : Brittleness index of the fracturing layer; E R Young's modulus of the fractured layer; Minimum principal stress in the horizontal fracture layer; Minimum horizontal principal stress difference between interlayer / fractured layers; Horizontal stress difference in the fracturing layer; Young's modulus ratio of the interlayer / fracture layer; The tensile strength ratio of the interlayer / fracture layer; Fracture toughness of the fracturing layer; Formation pressure in the fractured layer; Vertical stress in the fracturing layer; Minimum principal stress in the horizontal fracture layer; Maximum principal stress in the horizontal fracture layer; UCS R : Uniaxial compressive strength of the fracturing layer; Uniaxial compressive strength ratio of the interlayer / fracture layer; C R : Cohesion within the fracturing layer; Friction angle within the fracturing layer; ν R Poisson's ratio of the fractured layer; The ratio of Poisson's ratio of the interlayer to the fracturing layer; Tensile strength of the fracturing layer; P: oil production strength;

[0074] Step 8: Utilizing the relationship between Peason correlation coefficient and geomechanical parameters, analyze the correlation among the main geomechanical controlling factors of shale compressibility, and further obtain the relatively independent key characterization parameters and their ranking for shale compressibility. The results are as follows: Figure 2 As shown;

[0075] The relatively independent key characterization parameters and their order are: brittleness index of the fracturing layer, minimum horizontal principal stress difference between the interlayer and the fracturing layer, minimum horizontal principal stress of the fracturing layer, horizontal stress difference of the fracturing layer, and tensile strength of the fracturing layer.

[0076] Step Nine: Normalize the relatively independent key characterization parameter values ​​of different dimensions using the extreme value transformation method. Positive indices are normalized positively, and negative indices are normalized negatively. Then, determine the weighting coefficients for the influence of different factors on fracturing compressibility. Finally, weight the normalized values ​​with the weighting coefficients to obtain the shale compressibility index. The mathematical model is as follows:

[0077]

[0078] In the formula: FI is the shale compressibility index, which is dimensionless; S i (i = 1, 2, 3, ..., n) are the standardized values ​​of the key characterization parameters, which are dimensionless; w i (i = 1, 2, 3, ..., n) are the weight coefficients of the key characterization parameters, and their sum equals 1; n is the number of parameters.

[0079] The positive indices include the brittleness index of the fracturing layer and the difference in the minimum horizontal principal stress between the interlayer and the fracturing layer, which are normalized using formula (10). The negative indices include the minimum horizontal principal stress of the fracturing layer, the horizontal stress difference of the fracturing layer, and the tensile strength of the fracturing layer, which are normalized using formula (11).

[0080]

[0081]

[0082] In the formula: The normalized fracturing layer brittleness index; The normalized minimum principal stress difference between the interlayer and the fracturing layer; The normalized horizontal principal stress difference of the fracturing layer; The normalized minimum principal stress; The normalized tensile strength is denoted by ; max and min represent the maximum and minimum values ​​of this type of parameter in the study area, respectively.

[0083] In step nine, the weighting coefficients for the influence of different factors on fracturing capability are determined using the following method:

[0084] The weight vectors of each key characterization parameter, i.e., subjective weight coefficients, are calculated based on the Analytic Hierarchy Process (AHP) theory, as shown in Table 2. The weight vectors of each key characterization parameter, i.e., objective weight coefficients, are determined using the information entropy method, as shown in Table 2. Based on this, and according to game theory, the subjective and objective weight coefficients of each key characterization parameter are comprehensively considered to obtain the comprehensive weight coefficients, as shown in Table 2. Table 2 shows that the comprehensive weight coefficients for factors such as the brittleness index of the fracturing layer, the minimum horizontal principal stress difference between the interlayer / fracturing layer, the minimum horizontal principal stress of the fracturing layer, the horizontal stress difference of the fracturing layer, and the tensile strength of the fracturing layer, determined based on game theory, are 0.3315, 0.2530, 0.1718, 0.1623, and 0.0814, respectively.

[0085] Table 2. Weighting coefficients of each key characterization parameter

[0086] Weight B <![CDATA[(△σ h ) min ]]> <![CDATA[σ h ]]> △σ <![CDATA[S t ]]> Analytic Hierarchy Process 0.4155 0.2610 0.1589 0.1072 0.0574 Information entropy method 0.1068 0.2315 0.2064 0.3098 0.1455 Game theory methods 0.3315 0.2530 0.1718 0.1623 0.0814

[0087] Based on the comprehensive weighting coefficients, the expression for the compressibility index of the shale in the study block is as follows:

[0088]

[0089] Based on the constructed shale compressibility index calculation model, and using oil testing data from fractured wells, the relationship between the calculated compressibility index and normalized recovery intensity is as follows: Figure 3 As shown. From Figure 3 It can be found that there is a good positive correlation between the shale compressibility index and the normalized oil recovery intensity, that is, the larger the compressibility index, the greater the normalized oil recovery intensity, and the better the fracturing effect.

[0090] Combining the constructed well logging evaluation methods for rock mechanics parameters, formation pressure, and geostress, with the constructed shale compressibility index, a well logging evaluation method for shale compressibility was formed, thereby obtaining a single-well profile of the compressibility index (e.g., Figure 4 ).

Claims

1. A well logging evaluation method for shale compressibility based on game theory, characterized in that, Includes the following steps: Step 1: Prepare experimental core data, geological data, fracturing data, well logging data, and production data; perform lithological description on the obtained downhole core data and conduct rock physics-rock mechanics experiments; after processing the raw experimental data, obtain rock mechanics parameters and experimental rock physics parameters; Step 2: Analyze the relationship between rock mechanical parameters and experimental rock physical parameters, determine the rock physical response law of rock mechanical parameters in the study block, and establish the mathematical relationship between rock mechanical parameters and experimental rock physical parameters; Step 3: Based on the formation pressure test data of the study block, analyze the correlation between formation effective stress and well logging rock physical parameters, clarify the response relationship of formation effective stress, and establish a formation pore pressure prediction formula based on well logging rock physical parameters in combination with the effective stress principle. Step 4: Establish the relationship between indoor experimental rock physical parameters and field logging rock physical parameters; Step 5: Based on the hydraulic fracturing construction curve, under the constraints of drilling and fracturing engineering information, using the combined spring model (see formulas (1) and (2), the tectonic strain coefficient of the strata in the study block is obtained through mathematical and rock mechanics theory inversion analysis. Then, a geostress calculation model of the strata in the study block is constructed to obtain the minimum horizontal principal stress, maximum horizontal principal stress, and vertical stress of the study block. Furthermore, the difference in horizontal principal stress of the fracturing layer and the difference in horizontal minimum principal stress between the interlayer and the fracturing layer are obtained (see formulas (3) and (4) respectively). （1） （2） （3） （4） In the formula: Poisson's ratio of the rock; σ is the Young's modulus of the rock, MPa; H、 σ h These are the maximum and minimum horizontal principal stresses, respectively, in MPa; , These are the structural strain coefficients along the directions of maximum and minimum principal stress, respectively; The depth of the well logging starting point, in meters; Indicates the depth of the unlogged section. Density of a point, g / cm³ 3 ; Indicates depth as Point logging density, g / cm³ 3 ; The acceleration due to gravity is expressed in kg·m / s². 2 ; △σ represents the horizontal principal stress difference in the fracturing layer, MPa; σ h I 、 σ h R These represent the minimum principal stresses of the interlayer and the fracturing layer, respectively, in MPa; Δσ h IR The minimum principal stress difference between the interlayer and the fracturing layer, in MPa; The vertical stress is measured in MPa. Formation pore pressure, MPa; Step Six: Normalize the oil production intensity to obtain the normalized oil production intensity; extract the geomechanical parameters of each segment in the perforated fracturing section, and then analyze the relationship between the extracted geomechanical parameters and the normalized oil production intensity to qualitatively understand the influence of the geomechanical parameters on the fracturing effect; use grey relational analysis to obtain the correlation degree of the influence of each geomechanical parameter on the shale fracturing effect, and according to the standard of correlation degree greater than 0.65, obtain the main geomechanical control factors of shale compressibility. The main geomechanical control factors of shale compressibility include the main control factors of the fracturing layer and the main control factors of the isolation layer. The main control factors of the fracturing layer include brittleness index, Young's modulus, minimum horizontal principal stress, difference of horizontal principal stress, and tensile strength. The main control factors of the isolation layer include the difference of minimum horizontal principal stress between the isolation layer and the fracturing layer, the ratio of Young's modulus between the isolation layer and the fracturing layer, and the ratio of tensile strength between the isolation layer and the fracturing layer. Step 7: Using the relationship between Peason correlation coefficient and geomechanical parameters, analyze the correlation between the main geomechanical control factors of shale compressibility, and further obtain the key characterization parameters of shale compressibility that are relatively independent and ranked as follows: brittleness index of the fracture layer, minimum horizontal principal stress difference between the interlayer and the fracture layer, minimum horizontal principal stress of the fracture layer, horizontal stress difference of the fracture layer, and tensile strength of the fracture layer. Step 8: The values ​​of the relatively independent key characterization parameters with different dimensions are normalized by the extreme value transformation method. Positive indicators are normalized by positive normalization and negative indicators are normalized by negative normalization. The positive indicators include the brittleness index of the fracturing layer and the difference between the horizontal minimum principal stress of the interlayer and the fracturing layer, which are normalized by formula (10). The negative indicators include the horizontal minimum principal stress of the fracturing layer, the horizontal stress difference of the fracturing layer and the tensile strength of the fracturing layer, which are normalized by formula (11). Then, the comprehensive weight coefficients of the influence of different factors on fracturability are determined. The method adopted is as follows: the weight vector of each key characterization parameter is calculated based on the theory of analytic hierarchy process, that is, the subjective weight coefficient. The weight vector of each key characterization parameter is determined by the information entropy method, that is, the objective weight coefficient. On this basis, according to the idea of ​​game theory, the subjective weight coefficient and the objective weight coefficient of each key characterization parameter are considered together to obtain the comprehensive weight coefficient. Finally, the standardized value obtained by normalization is weighted by the comprehensive weight coefficient to obtain the shale compressibility index. The mathematical model is shown in formula (5): (10) (11) （5） In the formula: The normalized fracturing layer brittleness index; The normalized minimum principal stress difference between the interlayer and the fracturing layer; The normalized horizontal principal stress difference of the fracturing layer; The normalized minimum principal stress; S is the normalized tensile strength; max and min represent the maximum and minimum values ​​of this type of parameter in the study area, respectively; FI is the shale compressibility index, which is dimensionless; S i The standardized values ​​of the key characterization parameters are dimensionless; w i The weight coefficients of the key characterization parameters are summed to 1; n is the number of parameters.

2. The shale compressibility logging evaluation method based on game theory as described in claim 1, characterized in that, The rock physics-rock mechanics experiments include density testing, acoustic wave testing, uniaxial or triaxial compression testing, tensile strength testing, and fracture toughness testing; the rock mechanics parameters include compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, brittleness index, and fracture toughness; the experimental rock physics parameters include experimentally tested longitudinal wave velocity, experimentally tested transverse wave velocity, and experimentally tested density.

3. The shale compressibility logging evaluation method based on game theory as described in claim 1, characterized in that, In step two, a mathematical relationship is established between some rock mechanical parameters and experimental rock physical parameters, as shown in formula (6): （6） In the formula: Uniaxial compressive strength, MPa; Tensile strength, MPa; Type I fracture toughness, MPa▪m 0.5 ; To test the density of the rock in experiments, g / cm³ 3 ; For experimental testing of longitudinal wave velocity, m / s.

4. The shale compressibility logging evaluation method based on game theory as described in claim 1, characterized in that, In step three, the established prediction relationship is shown in formula (7): （7） In the formula: The vertical stress is measured in MPa. Effective stress, MPa; DEN represents formation pore pressure, in MPa. L For logging rock density, g / cm³ 3 V pL The value is the longitudinal wave velocity in well logging, in m / s.

5. The shale compressibility logging evaluation method based on game theory as described in claim 1, characterized in that, In step four, the established relational expression is represented as follows: （8） （9） In the formula: To test the density of the rock in experiments, g / cm³ 3 ; For experimental testing of longitudinal wave velocity, m / s; DEN L For logging rock density, g / cm³ 3 V pL The value is the longitudinal wave velocity in well logging, in m / s.

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