A game-theoretic approach to evaluating shale compressibility logging

Abstract

A method for evaluating shale compressibility logging based on game theory, comprising: step 1 of preparing rock core data, conducting experiments, and obtaining rock mechanical parameters and experimental rock physical parameters; step 2 of establishing a relationship between the rock mechanical parameters and the experimental rock physical parameters; step 3 of establishing a relationship between the rock mechanical parameters and the experimental rock physical parameters to predict the formation pore pressure based on the well logging rock physical parameters; step 4 of establishing a relationship between the laboratory and field logging rock physical parameters; step 5 of constructing a geostress calculation model for the research block formation and further obtaining related parameters; step 6 of obtaining the main controlling factors of shale compressibility geomechanics by normalizing the oil recovery intensity; step 7 of obtaining the main expression parameters and ranking of the relatively independent shale compressibility; and step 8 of determining the shale compressibility index. [Effects] A method for evaluating shale compressibility has been established with high accuracy.

Description

[Technical Field]

[0001] The present invention relates to the field of unconventional oil and gas development, and in particular to a method for evaluating shale compressibility logging based on game theory. [Background technology]

[0002] As evidenced by current exploration and development practices, volume fracturing conversion is a key technology for the efficient development of unconventional oil and gas resources. How to select fracturing formation segments for vertical wells and how to divide fracturing segments for horizontal wells are major problems faced in volume fracturing conversion of unconventional oil and gas reservoirs. To solve these problems, it is necessary to evaluate the compressibility of the reservoir. Generally, if the reservoir compressibility is good, the formation is more brittle, more prone to fractures, and more likely to form fracture networks, which is favorable for reservoir development. Currently, a large number of reservoir compressibility evaluation methods have been proposed for unconventional oil and gas resources, such as shale oil and gas and dense oil and gas. Each of these evaluation methods has its own advantages and disadvantages, and they have achieved certain practical applications in the field. However, for shale oil reservoirs with thin interlayer structures, the fracturing process is influenced not only by the geomechanical parameters of the fractured layer but also by the geomechanical parameters of the upper and lower layers, and there is no method yet available for evaluating the compressibility of shale that reasonably takes into account the geomechanical parameters of the fractured layer and intermediate layer. Summary of the Invention

[0003] The objective of this invention is to propose a method for evaluating shale compressibility based on game theory. By combining multiple methods and utilizing up to 19 factors, a method for evaluating shale compressibility that takes into account fractured and intermediate layer geomechanical parameters is established. This method has high accuracy.

[0004] The technical solutions adopted by the present invention are as follows: A method for evaluating shale compressibility logging based on game theory, comprising the following steps:

[0005] Step 1: Prepare experimental rock core data, geological data, fracturing data, logging data, and production data, and conduct rock physics-rock mechanics experiments to obtain rock mechanical parameters and experimental rock physical parameters.

[0006] Step 2: Determine the rock physics response rules of the rock mechanical parameters of the study block (study target section), and establish the mathematical relationship between the rock mechanical parameters and the experimental rock physical parameters.

[0007] Step 3: Based on the formation pressure test data of the study block, analyze the correlation between the formation effective stress and the well-logged rock physical parameters, clarify the response relationship of the formation effective stress, and combine it with the effective stress principle to construct a formation pore pressure prediction relationship based on the well-logged rock physical parameters.

[0008] Step 4: Establish the relationship between the petrophysical parameters in laboratory experiments and those in field logging.

[0009] Step 5: Based on the hydraulic fracturing construction curve, under the constraints of the well drilling and fracturing construction information, use the combined spring model to obtain the structural strain coefficient of the research block stratum through back analysis using mathematics and rock mechanics theory, and then construct a geostress calculation model of the research block stratum to obtain the horizontal minimum principal stress, horizontal maximum principal stress and vertical stress of the research block, as well as the horizontal principal stress difference of the fracturing layer and the horizontal minimum principal stress difference between the intermediate layer and the fracturing layer. JPEG0007820876000002.jpg54170In the formula, μ is the Poisson's ratio of the rock, E is the Young's modulus of the rock (MPa), and σ H , σ h are the horizontal maximum principal stress and horizontal minimum principal stress (MPa), respectively, and ε H , ε his the structural strain coefficient along the maximum principal stress direction and the minimum principal stress direction, H0 is the logging start point depth (m), and ρ0(h) is the density (g / cm) at point h of the unlogged segment depth. 3 ) and ρ(h) is the logging density at depth h (g / cm 3 ) and g is the acceleration due to gravity (kg.m / s 2 ) △σ is the horizontal principal stress difference in the fractured layer (MPa), and σ h I , σ h R are the horizontal minimum principal stresses in the intermediate layer and fractured layer (MPa), respectively, and △σ h IR is the minimum horizontal principal stress difference between the intermediate layer and the fractured layer (MPa).

[0010] Step 6: The evaluation of fracturing effectiveness can be performed using the oil production intensity, where the higher the value, the better the fracturing effectiveness, and the lower the value, the worse the fracturing effectiveness. To avoid the influence of differences in sand addition amounts between different fracturing wells, a normalization process is performed on the oil production intensity to obtain the normalized oil production intensity (daily oil production per meter / sand addition amount per meter). After extracting the geomechanical parameters of each section in the borehole fracturing section, the relationship between the extracted geomechanical parameters and the normalized oil recovery intensity is analyzed to qualitatively understand the influence of the geomechanical parameters on the fracturing effect. The grey correlation analysis method is used to obtain the magnitude of the correlation degree that each geomechanical parameter affects the shale fracturing effect, and when the correlation degree value is greater than 0.65, the main geomechanical factor controlling shale compressibility is identified.

[0011] Step 7: Using the relationship between the Peason correlation coefficient and geomechanical parameters, analyze the correlation between the geomechanical main control factors of shale compressibility, and further obtain the relatively independent main expression parameters and order of shale compressibility.

[0012] Step 8: The relatively independent main expression parameter values ​​of different dimensions are normalized by the extreme value transformation method. At this time, positive indicators are normalized in the positive direction, and negative indicators are normalized in the negative direction. Then, the weighting coefficients of the influence of different factors on the crushability are determined. Finally, the normalized value obtained by the normalization process is weighted with the weighting coefficient to obtain the shale compressibility index. The mathematical model is as follows: JPEG0007820876000003.jpg12170In the formula, FI is the shale compressibility index (dimensionless), and S i is the standardized value (dimensionless) of the main expression parameter, and w i are the weighting coefficients of the main expression parameters, the sum of which is equal to 1, and n is the number of parameters.

[0013] Further, the rock physics-rock mechanics experiments include density tests, sonic tests, uniaxial or triaxial compression tests, tensile strength tests, and fracture toughness tests, and the rock mechanics parameters include compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, brittleness index, and fracture toughness, and the experimental rock physics parameters include experimental test longitudinal wave velocity, experimental test shear wave velocity, and experimental test density.

[0014] Furthermore, in step 2, mathematical relationships between some rock mechanical parameters (which are indirectly obtained by other parameters or these parameters, or obtained by other parameters) and experimental rock physical parameters are established, as shown in Equation (6). JPEG0007820876000004.jpg28170In the formula, σ c is the uniaxial compressive strength (MPa), and S t is the tensile strength (MPa), and K ic is the type I fracture toughness (MPa m 0.5 ) and DEN M is the experimental test rock density (g / cm 3 ) and V pM is the experimental test longitudinal wave velocity (m / s).

[0015] Furthermore, in step 3, the predictive relationship established is shown in equation (7). JPEG0007820876000005.jpg8170In the formula, σ ν is the normal stress (MPa), and σ e is the effective stress (MPa), and P p is the formation pore pressure (MPa), and DEN L is the logging rock density (g / cm 3 ) and V pL is the logging longitudinal wave velocity (m / s).

[0016] Furthermore, in step 4, the established relationship is expressed as follows: JPEG0007820876000006.jpg16170 The parameters in the formula are the same as above.

[0017] Furthermore, in step 6, the geomechanical main control factors of the shale compressibility include the main control factors of the fractured layer and the main control factors of the pressure intermediate layer, the main control factors of the fractured layer include the brittleness index, Young's modulus, horizontal minimum principal stress, horizontal principal stress difference, and tensile strength, and the main control factors of the pressure intermediate layer include the horizontal minimum principal stress difference between the intermediate layer and the fractured layer, the Young's modulus ratio between the intermediate layer and the fractured layer, and the tensile strength ratio between the intermediate layer and the fractured layer.

[0018] Furthermore, in step 7, the relatively independent main expression parameters and order are the brittleness index of the fractured layer, the horizontal minimum principal stress difference between the intermediate layer and the fractured layer, the horizontal minimum principal stress of the fractured layer, the horizontal stress difference of the fractured layer, and the tensile strength of the fractured layer.

[0019] Furthermore, the positive direction index includes the brittleness index of the fractured layer and the horizontal minimum principal stress difference between the intermediate layer and the fractured layer, and is normalized using equation (10). The negative direction index includes the horizontal minimum principal stress of the fractured layer, the horizontal stress difference of the fractured layer, and the tensile strength of the fractured layer, and is normalized using equation (11). JPEG0007820876000007.jpg72170JPEG0007820876000008.jpg37170max and min respectively represent the maximum and minimum values ​​of this type of parameter in the research block.

[0020] Furthermore, in step 9, the method for determining the weighting coefficients of the influence of different factors on the friability is as follows:

[0021] Based on the theory of the Analytic Hierarchy Process, the weight vector of each key expression parameter, i.e., the subjective weight coefficient, is calculated. In addition, the information entropy method is used to determine the weight vector of each key expression parameter, i.e., the objective weight coefficient. Based on this, and based on the concept of game theory, the subjective weight coefficient and the objective weight coefficient of each key expression parameter are comprehensively considered to obtain the comprehensive weight coefficient.

[0022] Based on the obtained comprehensive weighting coefficient, the expression to obtain the shale compressibility index of the study block is: JPEG0007820876000009.jpg9170. [Effects of the Invention]

[0023] This invention utilizes mathematical statistics to establish a shale formation rock mechanical parameter logging prediction method, an effective stress method to establish a shale formation pore pressure logging prediction method, and a combined spring model to establish a shale formation geostress logging prediction method under the constraints of multi-source engineering information such as well drilling and fracturing. Gray correlation analysis is used to obtain the main geomechanical controlling factors of shale compressibility. Based on the correlation analysis between Peason correlation coefficients and geomechanical parameters, the relatively independent main expression parameters and order of shale compressibility are obtained. Analytic hierarchy process is used to obtain subjective weights of shale compressibility main parameters. Entropy method is used to obtain objective weights of shale compressibility main parameters. Game theory method is used to obtain comprehensive weights of shale compressibility constituent factors. By combining multiple methods, a shale compressibility evaluation method is established that takes into account fractured and intermediate layer geomechanical parameters using up to 19 factors. This method has high accuracy. [Brief explanation of the drawings]

[0024] [Figure 1] FIG. 1 shows the results of the main control factors obtained based on the gray correlation analysis method. [Figure 2] The main expression parameter analysis results based on the Pearson correlation coefficient. [Figure 3] The relationship between compressibility index and normalized oil yield strength. [Figure 4] FIG. 1 is a cross-sectional view of the compressibility index of the studied formation at one well. [Figure 5] This is a logging evaluation diagram of the compressibility of the formation. DETAILED DESCRIPTION OF THE INVENTION

[0025] A method for evaluating shale compressibility logging based on game theory, comprising the following steps:

[0026] Step 1: Prepare experimental rock core data, geological data, fracturing data, logging data, and production data.

[0027] Step 2: Perform lithology description on the acquired downhole rock core data, and perform rock physics-rock mechanics experiments including density test, sonic test, uniaxial or triaxial compression test, tensile strength test, and fracture toughness test. After processing the experimental raw data, obtain rock mechanical parameters and experimental rock physical parameters. The rock mechanical parameters include compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, brittleness index, and fracture toughness, and the experimental rock physical parameters include experimental test longitudinal wave velocity, experimental test shear wave velocity, and experimental test density.

[0028] Step 3: Based on the traditional method for evaluating mechanical parameters, analyze the relationship between the rock mechanical parameters and the experimental rock physical parameters, determine the rock physical response law of the rock mechanical parameters of the study block, and based on this, establish the mathematical relationship between some rock mechanical parameters (indirectly obtained by other parameters or these parameters, or obtained by other parameters) and the experimental test longitudinal wave velocity and the experimental test density, as shown in Equation (1). JPEG0007820876000010.jpg28170In the formula, σ c is the uniaxial compressive strength (MPa), and S t is the tensile strength (MPa), and K ic is the type I fracture toughness (MPa m 0.5 ) and DEN M is the experimental test rock density (g / cm 3 ) and V pM is the experimental test longitudinal wave velocity (m / s).

[0029] Step 4: Based on the formation pressure test data of the study block, the correlation between the formation effective stress (ignoring the effect of poroelastic modulus) and the logging rock physics parameters (logging rock density, logging longitudinal wave velocity, logging shear wave velocity, natural gamma, natural potential, neutrons, density resistivity, etc.) was analyzed to clarify the response relationship of the formation effective stress. In combination with the effective stress principle, a formation pore pressure prediction relationship based on several logging rock physics parameters (only analyzing the response relationship of other logging rock physics parameters such as logging shear wave velocity and natural gamma) was constructed, as shown in Equation (2). JPEG0007820876000011.jpg9170In the formula, σ ν is the normal stress (MPa), and σ e is the effective stress (MPa), and P p is the formation pore pressure (MPa).

[0030] Step 5: Establish the relationship between the petrophysical parameters in laboratory experiments and those in field logging, which is expressed as follows: JPEG0007820876000012.jpg16170DEN M is the experimental test rock density (g / cm 3 ) and DEN L is the logging rock density (g / cm 3 ) and V pM is the experimental test longitudinal wave velocity (m / s), and V pL is the logging longitudinal wave velocity (m / s).

[0031] Step 6: Based on the hydraulic fracturing construction curve and the constraints of the well drilling and fracturing process information, the combined spring (see Equation (5) and Equation (6)) is used to obtain the structural strain coefficient of the research block stratum (shown in Table 1) through back analysis using mathematical and rock mechanics theory, and then a geostress calculation model of the research block stratum is constructed to obtain the horizontal minimum principal stress, horizontal maximum principal stress, and vertical stress of the research block, as well as the horizontal principal stress difference in the fractured layer and the horizontal minimum principal stress difference between the intermediate layer and the fractured layer, which are shown in Equation (7) and Equation (8), respectively. JPEG0007820876000013.jpg53170In the formula, μ is the Poisson's ratio of the rock, E is the Young's modulus of the rock (MPa), and σ H , σ h are the horizontal maximum principal stress and horizontal minimum principal stress (MPa), respectively, and ε H , ε h is the structural strain coefficient along the maximum principal stress direction and the minimum principal stress direction, H0 is the logging start point depth (m), and ρ0(h) is the density (g / cm) at point h of the unlogged segment depth. 3 ) and ρ(h) is the logging density at depth h (g / cm 3 ) and g is the acceleration due to gravity (kg.m / s 2 ) △σ is the horizontal principal stress difference in the fractured layer (MPa), and σ h I , σ h R are the horizontal minimum principal stresses in the intermediate layer and fractured layer (MPa), respectively, and △σ h IR is the minimum horizontal principal stress difference between the intermediate layer and the fractured layer (MPa).

[0032] JPEG0007820876000014.jpg27170

[0033] Step 7: The evaluation of fracturing effectiveness can be performed using the oil production intensity, where the higher the value, the better the fracturing effectiveness, and the lower the value, the worse the fracturing effectiveness. To avoid the influence of differences in sand addition amounts between different fracturing wells, a normalization process is performed on the oil production intensity to obtain the normalized oil production intensity (daily oil production per meter / sand addition amount per meter). The geomechanical parameters of each section in the radial hole fracturing section are extracted, and a total of 19 items are extracted. After that, the relationship between the extracted geomechanical parameters and the normalized oil recovery intensity is analyzed to qualitatively understand the influence of the geomechanical parameters on the fracturing effect. Gray correlation analysis was used to obtain the magnitude of correlation between each geomechanical parameter and the shale fracturing effect. If the correlation value exceeded 0.65, the geomechanical main controlling factors of shale compressibility (Figure 1) were obtained. These main controlling factors included the main controlling factors of the fractured layer and the main controlling factors of the pressure interlayer. The main controlling factors of the fractured layer included the brittleness index, Young's modulus, horizontal minimum principal stress, horizontal principal stress difference, and tensile strength. The main controlling factors of the pressure interlayer included the horizontal minimum principal stress difference between the interlayer and the fractured layer, the Young's modulus ratio between the interlayer and the fractured layer, and the tensile strength ratio between the interlayer and the fractured layer. In FIG. 1, the meanings of the symbols are as follows: JPEG0007820876000015.jpg85170

[0034] Step 8: Using the relationship between the Peason correlation coefficient and geomechanical parameters, the correlation between the main geomechanical control factors of shale compressibility is analyzed to further obtain the relatively independent main expression parameters and order of shale compressibility. The results are shown in Figure 2. The relatively independent main expression parameters and sequences are the brittleness index of the fractured layer, the minimum horizontal principal stress difference between the intermediate layer and the fractured layer, the minimum horizontal principal stress of the fractured layer, the horizontal stress difference of the fractured layer, and the tensile strength of the fractured layer. Step 9: The relatively independent main expression parameter values ​​of different dimensions are normalized by the extreme value transformation method. At this time, the positive indicators are normalized in the positive direction, and the negative indicators are normalized in the negative direction. Then, the weighting coefficients that affect different factors on crushability are determined. Finally, the normalized values ​​obtained by the normalization process are weighted with the weighting coefficients to obtain the shale compressibility index. The mathematical model is as follows: JPEG0007820876000016.jpg12170In the formula, FI is the shale compressibility index (dimensionless), and S i (i=1, 2, 3……n) is the standardized value (dimensionless) of the main expression parameter, and w i(i=1, 2, 3...n) are the weighting coefficients of the main expression parameters, the sum of which is equal to 1, and n is the number of parameters.

[0035] Here, the positive direction index includes the brittleness index of the fractured layer and the horizontal minimum principal stress difference between the intermediate layer and the fractured layer, and is normalized using equation (10). The negative direction index includes the horizontal minimum principal stress of the fractured layer, the horizontal stress difference of the fractured layer, and the tensile strength of the fractured layer, and is normalized using equation (11). JPEG0007820876000017.jpg71170JPEG0007820876000018.jpg45170max and min respectively represent the maximum and minimum values ​​of this type of parameter in the research block.

[0036] Here, in step 9, the method for determining the weighting coefficients for the influence of various factors on the crushability is as follows:

[0037] The weight vector for each key parameter, i.e., the subjective weight coefficient, was calculated based on the analytical hierarchy process (AHP) theory. The subjective weight coefficients are shown in Table 2. The information entropy method was used to determine the weight vector for each key parameter, i.e., the objective weight coefficient. The objective weight coefficients are shown in Table 2. Based on these, the subjective and objective weight coefficients for each key parameter were comprehensively considered based on the concept of game theory to obtain the overall weight coefficient. The overall weight coefficients are shown in Table 2. As can be seen from Table 2, the overall weight coefficients used to determine factors such as the brittleness index of the fractured layer, the minimum value of the minimum horizontal principal stress difference between the interlayer and fractured layer, the minimum horizontal principal stress of the fractured layer, the horizontal stress difference of the fractured layer, and the tensile strength of the fractured layer based on the concept of game theory are 0.3315, 0.2530, 0.1718, 0.1623, and 0.0814, respectively.

[0038] JPEG0007820876000019.jpg25170

[0039] JPEG0007820876000020.jpg21170The relationship between the compressibility index calculated using the test oil data from the fracturing well and the normalized oil yield strength based on the constructed shale compressibility index calculation model is shown in Figure 3. Figure 3 shows that there is a good positive correlation between the shale compressibility index and the normalized oil yield strength, i.e., the larger the compressibility index, the greater the normalized oil yield strength and the better the fracturing effect.

[0040] The evaluation method of geomechanical parameter logging, such as rock mechanical parameters, formation pressure, and geostress, can be combined with the constructed shale compressibility index to form an evaluation method of shale compressibility logging, and the compressibility index single-well cross section (Fig. 4) can be obtained.

Claims

1. A method for evaluating shale compressibility logging based on game theory, comprising the steps of: Step 1: Prepare experimental rock core data, geological data, fracturing data, logging data and production data, perform lithology description on the acquired downhole rock core data, conduct rock physics-rock mechanics experiments, and obtain rock mechanical parameters and experimental rock physical parameters after processing the experimental raw data; Step 2: Analyzing the relationship between the rock mechanical parameters and the experimental rock physical parameters, determining the rock physical response rules of the study block rock mechanical parameters, and establishing the mathematical relationship between the rock mechanical parameters and the experimental rock physical parameters; Step 3: Based on the formation pressure test data of the research block, analyze the correlation between the formation effective stress and the well-logged rock physical parameters, clarify the response relationship of the formation effective stress, and combine it with the effective stress principle to establish a formation pore pressure prediction relationship based on the well-logged rock physical parameters; Step 4: Establishing the relationship between petrophysical parameters in laboratory experiments and those in field logs; Step 5: Based on the hydraulic fracturing construction curve, under the information constraints of the well drilling and fracturing process, use the combined spring (see Equation (1) and Equation (2)) to obtain the structural strain coefficient of the research block stratum through back analysis using mathematics and rock mechanics theory, and further construct a geostress calculation model of the research block stratum to obtain the horizontal minimum principal stress, horizontal maximum principal stress and vertical stress of the research block, and further obtain the horizontal principal stress difference of the fractured layer and the horizontal minimum principal stress difference between the intermediate layer and the fractured layer (see Equation (3) and Equation (4) respectively); In the formula, μ is the Poisson's ratio of the rock, E is the Young's modulus of the rock (MPa), σH and σh are the horizontal maximum principal stress and horizontal minimum principal stress (MPa), respectively, and ε Ｈ , ε ｈ are the structural strain coefficients along the maximum principal stress direction and the minimum principal stress direction, respectively, and H ０ is the logging start depth (m), and ρ ０ (h) is the density (g / cm) at point h in the unlogged segment depth. ３ ) and ρ(h) is the logging density at depth h (g / cm ３ ) and g is the acceleration due to gravity (kg.m / s ２ ) , Δσ is the horizontal principal stress difference in the fractured layer (MPa), and σ ｈ Ｉ , σ ｈ Ｒ are the horizontal minimum principal stresses in the intermediate layer and the fractured layer (MPa), respectively, and △σ ｈ ＩＲ is the horizontal minimum principal stress difference between the intermediate layer and the fractured layer (MPa), and σ ν is the normal stress (MPa), and P ｐ is the formation pore pressure (MPa), Step 6: perform normalization processing on the oil yield intensity to obtain the normalized oil yield intensity, extract the geomechanical parameters of each section in the borehole fracturing section, then analyze the relationship between the extracted geomechanical parameters and the normalized oil yield intensity to qualitatively understand the influence of the geomechanical parameters on the fracturing effect, and use gray correlation analysis to obtain the correlation degree of the influence of each geomechanical parameter on the shale fracturing effect, and if the correlation degree value is greater than 0.65, obtain the geomechanical main controlling factors of shale compressibility, the geomechanical main controlling factors of shale compressibility include the main controlling factors of the fractured layer and the main controlling factors of the pressure intermediate layer, the main controlling factors of the fractured layer include the brittleness index, Young's modulus, horizontal minimum principal stress, horizontal principal stress difference and tensile strength, and the main controlling factors of the pressure intermediate layer include the horizontal minimum principal stress difference between the intermediate layer and the fractured layer, the Young's modulus ratio between the intermediate layer and the fractured layer, and the tensile strength ratio between the intermediate layer and the fractured layer; Step 7: Using the relationship between the Peason correlation coefficient and the geomechanical parameters, analyze the correlation between the geomechanical main control factors of shale compressibility, and further obtain the relatively independent main expression parameters and order of shale compressibility, including the brittleness index of the fractured layer, the minimum horizontal principal stress difference between the intermediate layer and the fractured layer, the minimum horizontal principal stress of the fractured layer, the horizontal stress difference of the fractured layer, and the tensile strength of the fractured layer; Step 8: The relatively independent main expression parameter values ​​of different dimensions are normalized by extreme value transformation, where the positive direction indicators are normalized by positive direction normalization, and the negative direction indicators are normalized by negative direction normalization, where the positive direction indicators include the brittleness index of the fractured layer, the horizontal minimum principal stress difference between the intermediate layer and the fractured layer, and are normalized by formula (10); the negative direction indicators include the horizontal minimum principal stress of the fractured layer, the horizontal stress difference of the fractured layer, and the tensile strength of the fractured layer, and are normalized by formula (11); Then, the comprehensive weighting coefficient of the impact of different factors on the crushability is determined. The method adopted is to calculate the weight vector of each main expression parameter, i.e., the subjective weighting coefficient, based on the theory of analytical hierarchy process, and then use the information entropy method to determine the weight vector of each main expression parameter, i.e., the objective weighting coefficient. Based on this, the subjective weighting coefficient and the objective weighting coefficient of each main expression parameter are comprehensively considered based on the idea of ​​game theory to obtain the comprehensive weighting coefficient. Finally, the standardized value obtained by the normalization process is weighted by the comprehensive weighting factor to obtain the compressibility index according to the mathematical model shown in Equation (5): During the ceremony, max and min represent the maximum and minimum values ​​of this type of parameter in the study block, respectively, FI is the shale compressibility index (dimensionless), and S ｉ is the standardized value (dimensionless) of the main expression parameter, and w ｉ is the weight coefficient of the main expression parameters, the sum of which is equal to 1, and n is the number of parameters.

2. 2. The method for evaluating shale compressibility logging based on game theory according to claim 1, wherein the rock physics-rock mechanics experiments include density tests, sonic tests, uniaxial or triaxial compression tests, tensile strength tests and fracture toughness tests, the rock mechanics parameters include compressive strength, elastic modulus, Poisson's ratio, cohesion, internal friction angle, tensile strength, brittleness index and fracture toughness, and the experimental rock physics parameters include experimental test longitudinal wave velocity, experimental test shear wave velocity and experimental test density.

3. In step 2, establish mathematical relationships between some rock mechanics parameters and experimental rock physical parameters, as shown in equation (6): In the formula, σ ｃ is the uniaxial compressive strength (MPa), and S ｔ is the tensile strength (MPa), and K ic is type I fracture toughness (MPa) m ０．５ and DEN Ｍ is the experimental test rock density (g / cm ３ ) and V ｐＭ 2. The method for evaluating shale compressibility logging based on game theory according to claim 1, characterized in that:

4. In step 3, the constructed prediction relational equation is expressed by equation (7), In the formula, σ ν is the normal stress (MPa), and σ e is the effective stress (MPa), and P ｐ is the pore pressure of the formation (MPa), and DEN Ｌ is the logging rock density (g / cm ３ ) and V ｐＬ 2. The method for evaluating shale compressibility logging based on game theory according to claim 1, characterized in that: is the longitudinal wave velocity of the logging (m / s).

5. In step 4, the established relationship is expressed as follows: In the ceremony, DEN Ｍ is the experimental test rock density (g / cm ３ ) and V ｐＭ is the experimental test longitudinal wave velocity (m / s), and Ｌ is the logging rock density (g / cm ３ ) and V ｐＬ 2. The method for evaluating shale compressibility logging based on game theory according to claim 1, characterized in that: is the longitudinal wave velocity of the logging (m / s).

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