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, and carrying out an experiment to obtain rock mechanical parameters and experimental rock physical parameters; 2, establishing a relational expression between rock mechanical parameters and experimental rock physical parameters; 3, establishing a formation pore pressure prediction relational expression based on logging rock physical parameters; 4, establishing a relationship between indoor and field logging rock physical parameters; 5, constructing a ground stress calculation model of the stratum of the research block, and then obtaining related parameters; 6, normalizing the oil production intensity to obtain geomechanical main control factors of shale compressibility; 7, obtaining relatively independent key characterization parameters of the compressibility of the shale and sorting the key characterization parameters; and 8, determining the compressibility index of the shale. According to the shale compressibility evaluation method, multiple methods are cooperated, the shale compressibility evaluation method considering the geomechanical parameters of the fractured layer and the interlayer is established, and the shale compressibility evaluation method has good accuracy.

Description

Technical Field

[0001] The present invention belongs to the field of unconventional oil and gas development, and specifically relates to a shale compressibility logging evaluation method based on a game theory method. Background Art

[0002] At present, the existing exploration and development practices have shown that volume fracturing transformation is one of the key technologies for the efficient development of unconventional oil and gas resources. How to optimize the fracturing layer section of vertical wells and how to divide the fracturing sections of horizontal wells are major key issues faced in the volume fracturing transformation of unconventional oil and gas reservoirs. The solution to these problems is inseparable from the evaluation of reservoir compressibility. It is generally believed that if the reservoir compressibility is good, the brittleness of the formation is high, it is easy to fracture, and it is easy to form a fracture network, which is conducive to the development of the reservoir. At present, a large number of 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 evaluation methods have their own advantages and disadvantages and have also achieved certain application effects on site. However, for shale oil reservoirs with thin interlayer characteristics, in addition to being affected by the geomechanical parameters of the fracturing layer, the geomechanical parameters of the upper and lower interlayers are also affected during the fracturing transformation process. There is no reasonable shale compressibility evaluation method that considers the geomechanical parameters of the fracturing layer and interlayer. Summary of the invention

[0003] The purpose of the present invention is to propose a shale compressibility logging evaluation method based on game theory. By coordinating multiple methods and utilizing up to 19 factors, a shale compressibility evaluation method that takes into account the geomechanical parameters of the fracturing layer and the interlayer is established. The method has good accuracy.

[0004] The technical solution adopted by the present invention is:

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

[0006] Step 1: Prepare experimental core data, geological data, fracturing data, logging data, and production data; describe the lithology of the acquired downhole core data and conduct rock physics-rock mechanics experiments; after processing the original experimental data, obtain rock mechanics parameters and experimental rock physics parameters;

[0007] Step 2: Based on the traditional evaluation method of rock mechanical parameters, the relationship between rock mechanical parameters and experimental rock physical parameters is analyzed to determine the rock physical response law of rock mechanical parameters in the study area, and on this basis, the mathematical relationship between rock mechanical parameters and experimental rock physical parameters is established;

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

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

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

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

[0012]

[0013] Δσ=σ H -σ h (3)

[0014]

[0015] Where: μ is the Poisson's ratio of rock; E is the Young's modulus of rock, MPa; σ H、 σ h are the maximum horizontal principal stress and the minimum horizontal principal stress, MPa; ε H , ε h are the structural strain coefficients along the direction of maximum principal stress and minimum principal stress, respectively; H0 is the depth of the logging starting point, m; ρ0(h) represents the density of the unlogged section at a depth of h, g / cm 3 ; ρ(h) represents the logging density at depth h, g / cm 3 ; g is the acceleration due to gravity, kg.m / s 2 ; △σ is the horizontal principal stress difference of the fracturing layer, MPa; σ h I , σ h R are the minimum principal stress at the interlayer level and the minimum principal stress at the fracturing layer level, MPa; △σh IR is the minimum horizontal principal stress difference between the interlayer and the fracturing layer, MPa;

[0016] Step 6: The fracturing effect can be evaluated by oil production intensity. The higher the value, the better the fracturing effect, and the lower the value, the worse the fracturing effect. In order to avoid the influence of the difference 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 / sand added per meter);

[0017] Extracting geomechanical parameters of each section in the perforation and fracturing section, and then analyzing the relationship between the extracted geomechanical parameters and the normalized oil recovery intensity, to qualitatively understand the influence of the geomechanical parameters on the fracturing effect;

[0018] The grey correlation analysis method is used to obtain the correlation between the influence of various geomechanical parameters on the shale fracturing effect, and the main geomechanical controlling factors of shale compressibility are obtained according to the standard that the correlation value is greater than 0.65.

[0019] Step 7: Using the relationship between the Peason correlation coefficient and geomechanical parameters, analyze the correlation between the main geomechanical control factors of shale compressibility, and further obtain the relatively independent key characterization parameters and ranking of shale compressibility;

[0020] Step 8: The relatively independent key characterization parameter values ​​of different dimensions are normalized by extreme value transformation method, where positive indicators are normalized by positive normalization and negative indicators are normalized by negative normalization. Then, the weight coefficients of different factors affecting fracturability are determined. Finally, the standardized values ​​obtained by normalization are weighted with the weight coefficients to obtain the shale compressibility index. The mathematical model is:

[0021]

[0022] Where: FI is the shale compressibility index, dimensionless; S i is the standardized value of the key characterization parameter, dimensionless; w i are the weight coefficients of the key characterization parameters, and their sum is equal 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 experimental test longitudinal wave velocity, experimental test shear wave velocity, and experimental test density.

[0024] Furthermore, in step 2, a mathematical relationship between some rock mechanical parameters (other parameters are obtained indirectly from these parameters or from other parameters) and experimental rock physical parameters is established, as shown in formula (6):

[0025]

[0026] Where: c is the uniaxial compressive strength, MPa; S t is the tensile strength, MPa; K ic is the fracture toughness of mode I, MPa·m 0.5 ;DEN M For experimental testing of rock density, g / cm 3 ; V pM For experimental testing of longitudinal wave velocity, m / s.

[0027] Furthermore, in step 3, the prediction relationship established is shown in formula (7):

[0028]

[0029] Where: v is the vertical stress, MPa; σ e is the effective stress, MPa; p p is the formation pore pressure, MPa; DEN L is the logging rock density, g / cm 3 ; V pL is the logging compressional wave velocity, m / s.

[0030] Furthermore, in step 4, the established relationship is expressed as:

[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 above.

[0034] Further, in step six, the geomechanical main controlling factors of shale compressibility include the main controlling factors of the fractured layer and the main controlling factors of the compression barrier 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. The main controlling factors of the compression barrier layer include the horizontal minimum principal stress difference between the barrier layer and the fractured layer, the Young's modulus ratio of the barrier layer to the fractured layer, and the tensile strength ratio of the barrier layer to the fractured layer.

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

[0036] Furthermore, the positive indicators include the brittleness index of the fractured layer and the horizontal minimum principal stress difference between the interlayer and the fractured layer, which are normalized using formula (10), while the negative 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, which are normalized using formula (11):

[0037]

[0038]

[0039] Where: is the normalized fracture layer brittleness index; is the normalized horizontal minimum principal stress difference between the isolation layer and the fracture layer; is the normalized horizontal principal stress difference of the fractured layer; is the normalized horizontal minimum principal stress; is the normalized tensile strength; max and min represent the maximum and minimum values ​​of this parameter in the study area, respectively.

[0040] Furthermore, in step nine, the weight coefficients of the influence of different factors on fracturability are determined by:

[0041] The weight vector of each key characterization parameter, i.e., the subjective weight coefficient, is calculated based on the theory of hierarchical analysis, and the weight vector of each key characterization parameter, i.e., the objective weight coefficient, is determined using the information entropy method; 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 comprehensively considered to obtain the comprehensive weight coefficient;

[0042] Based on the obtained comprehensive weight coefficient, the expression of shale compressibility index in the study area is obtained as follows:

[0043]

[0044] Beneficial effects of the present invention:

[0045] The present invention uses mathematical statistics to establish a logging prediction method for rock mechanical parameters of shale formations; uses the effective stress method to establish a logging prediction method for pore pressure of shale formations; based on the constraints of multi-source engineering information such as drilling and fracturing, a spring combination model is used to construct a logging prediction method for geostress of shale formations; uses the grey correlation analysis method to obtain the geomechanical main controlling factors of shale compressibility, and based on the correlation analysis between the Peason correlation coefficient and geomechanical parameters, relatively independent key characterization parameters and ranking of shale compressibility are obtained; uses the hierarchical analysis method to obtain the subjective weights of the key parameters of shale compressibility, uses the entropy weight method to obtain the objective weights of the key parameters of shale compressibility, and uses the game theory method to obtain the comprehensive weights of the constituent elements of shale compressibility. Through the coordination of multiple methods and using up to 19 factors, a shale compressibility evaluation method considering the geomechanical parameters of the fracturing layer and the interlayer is established, and the method has good accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is the result diagram of the main controlling factors obtained based on the grey correlation analysis method;

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

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

[0049] Figure 4 A cross-sectional diagram of the compressibility index of the formation studied for a certain well;

[0050] Figure 5 This is a well logging evaluation diagram for studying the compressibility of the formation. DETAILED DESCRIPTION

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

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

[0053] Step 2: Perform lithology description on the acquired downhole core data and carry out rock physics-rock mechanics experiments, including density test, acoustic wave test, uniaxial or triaxial compression test, tensile strength test, fracture toughness test; after processing the raw experimental data, obtain rock mechanics parameters and experimental rock physics parameters;

[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 experimental test longitudinal wave velocity, experimental test shear wave velocity and experimental test density;

[0055] Step 3: Based on the traditional evaluation method of rock mechanical parameters, the relationship between rock mechanical parameters and experimental rock physical parameters is analyzed to determine the rock physical response law of rock mechanical parameters in the study area. On this basis, a mathematical relationship between some of the rock mechanical parameters (other parameters are obtained indirectly from these parameters or from other parameters) and the experimental test P-wave velocity and experimental test density is established, as shown in formula (1):

[0056]

[0057] Where: c is the uniaxial compressive strength, MPa; S t is the tensile strength, MPa; K ic is the fracture toughness of mode I, MPa·m 0.5 ;DEN M For experimental testing of rock density, g / cm 3 ; V pM For experimental testing, longitudinal wave velocity, m / s;

[0058] Step 4: Based on the formation pressure test data of the study area, the correlation between the formation effective stress (ignoring the influence of the poroelastic coefficient) and the logging rock physical parameters (logging rock density, logging P-wave velocity, logging S-wave velocity, natural gamma, natural potential, neutron, deep and shallow resistivity, etc.) was analyzed, and the response relationship of the formation effective stress was clarified. Combined with the effective stress principle, the formation pore pressure prediction relationship 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 the formation effective stress) was established, see formula (2):

[0059]

[0060] Where: v is the vertical stress, MPa; σ e is the effective stress, MPa; p p is the formation pore pressure, MPa;

[0061] Step 5: Establish the relationship between the rock physical parameters of the indoor experiment and the rock physical parameters of the field logging, 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 For experimental testing of rock density, g / cm 3 ;DEN L is the logging rock density, g / cm 3 ; V pM For experimental testing of longitudinal wave velocity, m / s; V pL is the logging P-wave velocity, m / s;

[0065] Step 6: Based on the hydraulic fracturing construction curve, under the constraints of drilling and fracturing engineering information, the combined spring model is used, see formula (5) and formula (6), and through mathematical and rock mechanics theoretical inversion analysis, the structural strain coefficient of the formation in the study block is obtained (as shown in Table 1), and then the ground stress calculation model of the formation in the study block is constructed to obtain the horizontal minimum principal stress, horizontal maximum principal stress and vertical stress of the study block, and then the horizontal principal stress difference of the fracturing layer and the horizontal minimum principal stress difference between the interlayer and the fracturing layer are further obtained, see formula (7) and formula (8) respectively:

[0066]

[0067] Where: μ is the Poisson's ratio of rock; E is the Young's modulus of rock, MPa; σ H、 σ h are the maximum horizontal principal stress and the minimum horizontal principal stress, MPa; ε H , ε h are the structural strain coefficients along the direction of maximum principal stress and minimum principal stress, respectively; H0 is the depth of the logging starting point, m; ρ0(h) represents the density of the unlogged section at a depth of h, g / cm 3 ; ρ(h) represents the logging density at depth h, g / cm 3 ; g is the acceleration due to gravity, kg.m / s 2 ; △σ is the horizontal principal stress difference of the fracturing layer, MPa; σ h I , σ h R are the minimum principal stress at the interlayer level and the minimum principal stress at the fracturing layer level, MPa; △σ h IR is the minimum horizontal principal stress difference between the interlayer and the fracturing layer, MPa;

[0068] Table 1 Structural strain coefficient of the work area

[0069]

[0070] Step 7: The fracturing effect can be evaluated by oil production intensity. The higher the value, the better the fracturing effect, and the lower the value, the worse the fracturing effect. In order to avoid the influence of the difference 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 / sand added per meter);

[0071] The geomechanical parameters of each section in the perforation fracturing section were extracted, and a total of 19 items were extracted; 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;

[0072] The grey correlation analysis method is used to obtain the correlation between the influence of various geomechanical parameters on the shale fracturing effect, and according to the standard of correlation value greater than 0.65, the main geomechanical controlling factors of shale compressibility are obtained ( Figure 1 ), including the main controlling factors of the fracturing layer and the main controlling factors of the isolation layer. The main controlling factors of the fracturing layer include brittleness index, Young's modulus, horizontal minimum principal stress, horizontal principal stress difference, and tensile strength. The main controlling factors of the isolation layer include the horizontal minimum principal stress difference between the isolation layer and the fracturing layer, the Young's modulus ratio between the isolation layer and the fracturing layer, and the tensile strength ratio between the isolation layer and the fracturing layer;

[0073] exist Figure 1 Middle: B R : Fracture layer brittleness index; E R : Young's modulus of the fracture layer; Horizontal minimum principal stress of the fractured layer; Minimum horizontal principal stress difference between interlayers / fracture layers; Horizontal stress difference in the fractured layer; Young’s modulus ratio of interlayer / fracture layer; Interlayer / fracture layer tensile strength ratio; Fracture toughness of the fractured layer; Fracture layer formation pressure; Vertical stress of fractured layer; Horizontal minimum principal stress of the fractured layer; Maximum principal stress of fractured layer; UCS R : Uniaxial compressive strength of the fractured layer; Uniaxial compressive strength ratio of interlayer / fracture layer; C R : cohesion of the fractured layer; Internal friction angle of fractured layer; ν R : Poisson's ratio of the fractured layer; The ratio of the Poisson's ratio of the barrier layer / fracture layer; Tensile strength of fractured layer; P: oil recovery strength;

[0074] Step 8: Using the relationship between the Peason correlation coefficient and geomechanical parameters, analyze the correlation between the main geomechanical control factors of shale compressibility, and further obtain the relatively independent key characterization parameters and ranking of 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, horizontal minimum principal stress difference between the interlayer and the fracturing layer, horizontal minimum principal stress of the fracturing layer, horizontal stress difference of the fracturing layer, and tensile strength of the fracturing layer.

[0076] Step 9: The relatively independent key characterization parameter values ​​of different dimensions are normalized by extreme value transformation method, where positive indicators are normalized by positive normalization and negative indicators are normalized by negative normalization. Then, the weight coefficients of different factors affecting fracturability are determined. Finally, the standardized values ​​obtained by normalization are weighted with the weight coefficients to obtain the shale compressibility index. The mathematical model is:

[0077]

[0078] Where: FI is the shale compressibility index, dimensionless; S i (i=1,2,3…n) is the standardized value of the key characterization parameter, dimensionless; w i (i=1, 2, 3…n) is the weight coefficient of the key characterization parameter, the sum of which is equal to 1; n is the number of parameters.

[0079] The positive indicators include the brittleness index of the fractured layer and the horizontal minimum principal stress difference between the interlayer and the fractured layer, which are normalized using formula (10), while the negative 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, which are normalized using formula (11):

[0080]

[0081]

[0082] Where: is the normalized fracture layer brittleness index; is the normalized horizontal minimum principal stress difference between the isolation layer and the fracture layer; is the normalized horizontal principal stress difference of the fractured layer; is the normalized horizontal minimum principal stress; is the normalized tensile strength; max and min represent the maximum and minimum values ​​of this parameter in the study area, respectively.

[0083] Among them, in step nine, the weight coefficients of the influence of different factors on fracturability are determined by the following method:

[0084] The weight vector of each key characterization parameter, i.e., the subjective weight coefficient, is calculated based on the theory of analytic hierarchy process. The subjective weight coefficient is shown in Table 2. The weight vector of each key characterization parameter, i.e., the objective weight coefficient, is determined by the information entropy method. The objective weight coefficient is shown in Table 2. 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 comprehensively considered to obtain the comprehensive weight coefficient, as shown in Table 2. It can be seen from Table 2 that the comprehensive weight coefficients of the factors such as the brittleness index of the fracturing layer, the minimum value of the horizontal minimum principal stress difference between the interlayer / fracturing layer, 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 determined based on the idea of ​​game theory are 0.3315, 0.2530, 0.1718, 0.1623, and 0.0814, respectively.

[0085] Table 2 Weight coefficients of key characterization parameters

[0086] Weight B <![CDATA[(△σ h ) min ]]> <![CDATA[σ h ]]> △σ <![CDATA[S t ]]> Analytical 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 approach 0.3315 0.2530 0.1718 0.1623 0.0814

[0087] Based on the comprehensive weight coefficient, the expression of the shale compressibility index in the study area is obtained as follows:

[0088]

[0089] According to the constructed calculation model of shale compressibility index, based on the oil test data of fractured wells, the relationship between the calculated compressibility index and normalized oil production intensity is as follows: Figure 3 As shown. Figure 3 It can be found that there is a good positive correlation between the shale compressibility index and the normalized oil production intensity, that is, the larger the compressibility index, the greater the normalized oil production intensity, and the better the fracturing effect.

[0090] Combining the constructed rock mechanical parameters, formation pressure, geostress and other geomechanical parameter logging evaluation methods with the constructed shale compressibility index, a shale compressibility logging evaluation method is formed to obtain a single well profile of the compressibility index (such as Figure 4 ).

Claims

1. A shale compressibility logging evaluation method based on game theory, characterized in that: The following steps are involved: Step 1: Prepare experimental core data, geological data, fracturing data, logging data, and production data; describe the lithology of the acquired downhole core data and conduct rock physics-rock mechanics experiments; after processing the original 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 area, 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 research block, analyze the correlation between the formation effective stress and the logging rock physical parameters, clarify the response relationship of the formation effective stress, and combine the effective stress principle to establish the formation pore pressure prediction relationship based on the logging rock physical parameters; Step 4: Establish the relationship between the rock physical parameters of the indoor experiment and the rock physical parameters of the field logging; Step 5: Based on the hydraulic fracturing construction curve, under the constraints of drilling and fracturing engineering information, the combined spring model is used, see formula (1) and formula (2), and the structural strain coefficient of the formation in the study block is obtained through mathematical and rock mechanics theory inversion analysis. Then, the ground stress calculation model of the formation in the study block is constructed to obtain the horizontal minimum principal stress, horizontal maximum principal stress and vertical stress of the study block, and then the horizontal principal stress difference of the fracturing layer and the horizontal minimum principal stress difference between the interlayer and the fracturing layer are further obtained, see formula (3) and formula (4) respectively: Ds = s H -s h (3) Where: μ is the Poisson's ratio of rock; E is the Young's modulus of rock, MPa; σ H、 σ h are the horizontal maximum principal stress and the horizontal minimum principal stress, MPa, respectively; ε H , ε h are the structural strain coefficients along the direction of maximum principal stress and minimum principal stress, respectively; H0 is the depth of the logging starting point, m; ρ0(h) represents the density at the depth h of the unmeasured well section, g / cm 3 ; ρ(h) represents the logging density at depth h, g / cm 3 ; g is the acceleration due to gravity, kg.m / s 2 ; △σ is the horizontal principal stress difference of the fracturing layer, MPa; σ h I , σ h R are the minimum principal stress at the interlayer level and the minimum principal stress at the fracturing layer level, MPa; △σ h IR is the minimum horizontal principal stress difference between the interlayer and the fracturing layer, MPa; Step 6: Normalize the oil production intensity to obtain the normalized oil production intensity; extract the geomechanical parameters of each section in the perforation 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 the grey correlation analysis method to obtain the correlation degree of the influence of various geomechanical parameters on the shale fracturing effect, and according to the standard of correlation value greater than 0.65, obtain the geomechanical main controlling factors of shale compressibility; Step 7: Using the relationship between the Peason correlation coefficient and geomechanical parameters, analyze the correlation between the main geomechanical control factors of shale compressibility, and further obtain the relatively independent key characterization parameters and ranking of shale compressibility; Step 8: The relatively independent key characterization parameter values ​​of different dimensions are normalized by extreme value transformation method, where positive indicators are normalized by positive normalization and negative indicators are normalized by negative normalization. Then, the weight coefficients of different factors affecting fracturability are determined. Finally, the standardized values ​​obtained by normalization are weighted with the weight coefficients to obtain the shale compressibility index. The mathematical model is: Where: FI is the shale compressibility index, dimensionless; S i is the standardized value of the key characterization parameter, dimensionless; w i are the weight coefficients of the key characterization parameters, and their sum is equal to 1; n is the number of parameters.

2. A shale compressibility logging evaluation method based on game theory as claimed 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 experimental test longitudinal wave velocity, experimental test shear wave velocity, and experimental test density.

3. The shale compressibility logging evaluation method based on game theory method according to claim 1, characterized in that: In step 2, the mathematical relationship between some rock mechanical parameters and experimental rock physical parameters is established, see formula (6): Where: c is the uniaxial compressive strength, MPa; S t is the tensile strength, MPa; K ic is the fracture toughness of mode I, MPa·m 0.5 ;DEN M For experimental testing of rock density, g / cm 3 ; V pM For experimental testing of longitudinal wave velocity, m / s.

4. The shale compressibility logging evaluation method based on game theory method according to claim 1, characterized in that: In step 3, the prediction relationship established is shown in formula (7): Where: v is the vertical stress, MPa; σ e is the effective stress, MPa; p p is the formation pore pressure, MPa; DEN L is the logging rock density, g / cm 3 ; V pL is the logging compressional wave velocity, m / s.

5. The shale compressibility logging evaluation method based on game theory method according to claim 1, characterized in that: In step 4, the established relationship is expressed as: IT M =1.3064×THE L -0.8763 (8) In pM =1.0234×V pL -126.01 (9) The parameters in the formula are the same as above.

6. The shale compressibility logging evaluation method based on game theory method according to claim 1, characterized in that: In step six, the main geomechanical controlling factors of shale compressibility include the main controlling factors of the fracturing layer and the main controlling factors of the isolation layer. The main controlling factors of the fracturing layer include the brittleness index, Young's modulus, horizontal minimum principal stress, horizontal principal stress difference, and tensile strength. The main controlling factors of the isolation layer include the horizontal minimum principal stress difference between the isolation layer and the fracturing layer, the Young's modulus ratio between the isolation layer and the fracturing layer, and the tensile strength ratio between the isolation layer and the fracturing layer.

7. The shale compressibility logging evaluation method based on game theory method according to claim 1, characterized in that: In step seven, relatively independent key characterization parameters and their order are: brittleness index of the fracturing layer, horizontal minimum principal stress difference between the interlayer and the fracturing layer, horizontal minimum principal stress of the fracturing layer, horizontal stress difference of the fracturing layer, and tensile strength of the fracturing layer.

8. The shale compressibility logging evaluation method based on game theory method according to claim 1, characterized in that: In step eight, the positive indicators include the brittleness index of the fractured layer and the horizontal minimum principal stress difference between the interlayer and the fractured layer, which are normalized using formula (10), while the negative 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, which are normalized using formula (11): Where: is the normalized fracture layer brittleness index; is the normalized horizontal minimum principal stress difference between the isolation layer and the fracture layer; is the normalized horizontal principal stress difference of the fractured layer; is the normalized horizontal minimum principal stress; is the normalized tensile strength; max and min represent the maximum and minimum values ​​of this parameter in the study area, respectively.

9. The shale compressibility logging evaluation method based on game theory method according to claim 1, characterized in that: In step eight, the weight coefficients of the influence of different factors on fracturability are determined by: The weight vector of each key characterization parameter, namely the subjective weight coefficient, is calculated based on the theory of hierarchical analysis, and the weight vector of each key characterization parameter, namely the objective weight coefficient, is determined using the information entropy method; 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 comprehensively considered to obtain the comprehensive weight coefficient; Based on the obtained comprehensive weight coefficient, the expression of shale compressibility index in the study area is obtained as follows:

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