Quantitative characterization method of structural crack parameters based on coupling of minimum energy dissipation and minimum action principle

Abstract

This invention belongs to the field of petroleum exploration and discloses a quantitative characterization method for structural fracture parameters based on the coupling of the minimum energy consumption and minimum action principle. To overcome the shortcomings of existing technologies in achieving quantitative prediction of structural fractures from qualitative description, this invention first calculates the energy consumption of fracture rupture through rock mechanics experiments; then, it couples the minimum energy consumption principle and the minimum action principle to establish a rock fracture criterion; based on this, it derives and establishes quantitative characterization models for fracture aperture, volumetric density, and linear density; subsequently, it constructs a heterogeneous mechanical parameter volumetric model and performs elastoplastic geostress field simulation; finally, it achieves batch prediction of structural fracture parameters through programming, and verifies the reliability of the prediction results using core, imaging logging, and production dynamic data. This invention, starting from the physical essence of energy distribution and path selection, provides a unified theoretical framework for rock fracture and significantly improves the prediction accuracy of fracture "sweet spots."

Description

Technical Field

[0001] This invention belongs to the field of petroleum exploration, specifically, it relates to a quantitative characterization method for structural fracture parameters based on the principle of minimum energy consumption and minimum action coupling. Background Technology

[0002] Tectonic fractures, discontinuous structures formed in crustal rocks under tectonic stress, are key geological factors controlling the permeability and storage space of oil and gas reservoirs. Particularly in low-permeability reservoirs such as tight sandstone and shale, the degree of tectonic fracture development directly determines the distribution of reservoir "sweet spots" and single-well productivity. A complete fracture network can even transform economically worthless "dead" reservoirs into commercially viable "live" reservoirs. In the development of unconventional oil and gas reservoirs (such as shale gas), the effectiveness of artificial fracturing also highly depends on the pre-existing natural fracture system. Therefore, achieving accurate prediction of tectonic fracture parameters is of great significance for selecting optimal drilling targets, reducing exploration risks, and improving development efficiency.

[0003] Currently, methods for predicting tectonic fractures can be mainly categorized as follows: qualitative-semi-quantitative prediction based on geological modeling and seismic attributes; numerical simulation based on rock mechanics and geostress fields; and fracture identification and parameter calculation based on well logging data. However, these existing technologies generally suffer from the following limitations:

[0004] (1) Insufficient understanding of the mechanism: Most methods rely on empirical statistics or linear constitutive relations, failing to reveal the intrinsic energy driving mechanism and path selection criteria of rock fracture under complex stress state from a physical perspective.

[0005] (2) Difficulty in cross-scale integration: It is difficult to effectively unify the micromechanical mechanism that controls the propagation of a single crack with the development law of the regional crack system under the action of macro-stress field, resulting in inconsistencies in prediction results at different scales.

[0006] (3) Limited quantitative accuracy: The quantitative characterization of key parameters such as fracture aperture and volume density is often based on simplified assumptions or indirect conversions, lacking a solid physical theoretical foundation, resulting in low consistency between the prediction results and actual observation data, especially in reservoirs with strong heterogeneity.

[0007] Therefore, existing technologies have not yet provided a theoretical framework and methodology that, based on unified physical principles, can achieve high-precision quantitative prediction of structural fracture parameters. Developing a new method that can fundamentally reflect the physical nature of rock fracture and achieve accurate quantitative characterization across scales has become a pressing technical challenge in this field. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, this invention provides a quantitative characterization method for tectonic fracture parameters based on the coupling of the minimum energy dissipation and minimum action principles. Building upon the energy dissipation calculations of tectonic fracture in rock mechanics experiments, this method couples the minimum energy dissipation and minimum action principles to innovatively establish a rock fracture criterion and derive quantitative characterization models for fracture aperture, volumetric density, and linear density. Based on elastoplastic geostress field simulation, it achieves quantitative characterization of tectonic fracture parameters, and verifies reliability by comparing with actual data, thus improving the theoretical methods and system for quantitative fracture research.

[0009] To achieve the above objectives, the present invention adopts the following solution:

[0010] A quantitative characterization method for structural fracture parameters based on the principle of minimum energy consumption and minimum action coupling includes the following steps:

[0011] Step 1: Calculate the energy dissipation of structural fractures based on rock mechanics experiments;

[0012] Step 2: Establish a rock fracture criterion based on the coupling of the principle of minimum energy consumption and the principle of minimum action;

[0013] Step 3: Establish a crack aperture characterization model based on the coupling of the minimum energy consumption principle and the minimum action principle;

[0014] Step 4: Establish a crack volume density and linear density characterization model based on the coupling of the minimum energy consumption principle and the minimum action principle;

[0015] Step 5: Construct a heterogeneous mechanical parameter volume model and perform elastoplastic stress field simulation;

[0016] Step 6: Based on the rock fracture criterion, fracture aperture characterization model, fracture volume density and linear density characterization model, perform quantitative prediction of structural fracture parameters, and compare with actual data to complete reliability verification.

[0017] Furthermore, in the above method, step 1 includes:

[0018] Triaxial rock mechanics experiments were conducted to obtain rock mechanics parameters, including elastic modulus, Poisson's ratio, compressive strength, and cumulative energy; the energy dissipation value when tectonic cracks are generated was calculated based on stress-strain curves and the law of conservation of energy.

[0019] Furthermore, in the above method, the energy dissipation value is calculated using the following formula:

[0020] ;

[0021] in: For dissipated energy, J;

[0022] The value is the triaxial principal stress, in MPa;

[0023] These are the triaxial principal strain values, which are dimensionless.

[0024] The elastic modulus is measured experimentally in GPa.

[0025] Poisson's ratio, measured experimentally, is dimensionless.

[0026] ;

[0027] ;

[0028] 'a' represents a confining pressure-related parameter, which is obtained by nonlinear regression fitting of the initial segment of the stress-strain curve under different confining pressures.

[0029] b represents porosity. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0030] Porosity, measured experimentally;

[0031] c represents the mud content. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0032] The mud content was determined experimentally.

[0033] ;

[0034] Peak stress value, MPa;

[0035] The value represents the peak strain, which is dimensionless.

[0036] Furthermore, in the above method, step 2 includes:

[0037] A fracture energy dissipation rate characterization model is obtained based on the principle of minimum energy dissipation; a time domain function is introduced based on the principle of minimum action to establish a rock fracture criterion under the coupling of minimum energy dissipation and minimum action; wherein, when the energy dissipation rate is greater than the energy dissipation rate threshold, the rock fractures.

[0038] Furthermore, in the above method, step 3 includes:

[0039] Based on the principles of minimum energy consumption and minimum action, a crack aperture calculation model is derived, where crack aperture serves as the energy release pathway on the optimal crack propagation path.

[0040] Furthermore, in the above method, the crack aperture calculation model is as follows:

[0041] ;

[0042] in, For the energy consumed in the fracture, J;

[0043] The value is the triaxial principal stress, in MPa;

[0044] Let be the crack length, in meters.

[0045] Crack aperture, mm; The angle between the maximum principal stress and the crack surface is °;

[0046] The angle between the minimum principal stress and the crack surface is °.

[0047] Furthermore, in the above method, step 6 includes:

[0048] Based on the three-dimensional geostress field simulation results, nodal stress and strain parameters are extracted; the parameters of structural fractures are solved in batches through programming; the prediction results are verified using core, imaging logging or production dynamic data, and are considered reliable when the consistency is greater than 90%.

[0049] Furthermore, the present invention also discloses a storage device storing a computer program, which, when executed by a processor, implements the above-described method.

[0050] Furthermore, the present invention also discloses a computing device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to execute the computer program to implement the above-described method.

[0051] Furthermore, the aforementioned computing device further includes an input / output interface for receiving rock mechanics experimental data and outputting predicted results of structural fracture parameters.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. Strong theoretical innovation: For the first time, the universal principle of minimum energy consumption and the principle of minimum action in physics are coupled and systematically introduced into the field of tectonic fracture prediction. From the deeper physical level of "energy distribution" and "path selection", a unified, mechanism-driven theoretical framework for the irreversible process of rock fracture is established, which promotes the fundamental leap of tectonic fracture research from empirical qualitative description to mechanistic quantitative prediction.

[0054] 2. High prediction accuracy and significant practical value: This method unifies cross-scale information such as microscopic single fracture propagation and fracture systems in the macroscopic geostress field within the same physical framework, thereby achieving accurate quantitative characterization of key parameters such as fracture aperture and density. Practical application in the Hangjinqi block of Sinopec North China Bureau shows that the predicted fracture density has an average agreement rate with measured results exceeding 90%, and the correlation coefficient between the predicted fracture aperture and actual production dynamics is as high as 0.94. The accuracy is significantly better than traditional methods, providing more reliable technical support for the accurate prediction of "sweet spots" in unconventional oil and gas reservoirs and the optimization of drilling target areas, effectively reducing exploration and development risks. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the process for quantitative characterization of structural fracture parameters based on the principle of minimum energy consumption and minimum action.

[0056] Figure 2 This is a schematic diagram of energy evolution during rock deformation.

[0057] Figure 3 This is a schematic diagram of a crack aperture characterization model;

[0058] Figure 4 This is a schematic diagram of a crack density characterization model;

[0059] Figure 5 This is a schematic diagram comparing and verifying the quantitative prediction results of fracture density with imaging logging data.

[0060] Figure 6 This is a schematic diagram comparing and verifying the quantitative prediction results of crack aperture with actual production dynamic data. Detailed Implementation

[0061] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention. To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0062] Example 1:

[0063] A quantitative characterization method for structural fracture parameters based on the principle of minimum energy consumption and minimum action coupling, such as... Figure 1 As shown, the steps are as follows:

[0064] Step 1: Calculation of energy dissipation during structural fracture based on rock mechanics experiments. The specific method is as follows:

[0065] (1) Screen the core sections of the well with similar depths to the target layer of the core well, make a rough observation, select the cores with no cracks on the surface and perform radial orientation consistency calibration to ensure that the radial relative geographical orientation of each core is the same at 0°. Drill standard plunger samples with a diameter of 25mm and a length of 50mm respectively, and cut and grind both ends of the drilled samples. The perpendicularity of the end face to the rock sample axis is less than 0.25°.

[0066] (2) Triaxial rock mechanics tests were conducted on the obtained plunger specimens. During the experiment, the confining pressure was kept constant, and the axial load was gradually increased until the rock failed. Rock mechanics parameters (elastic modulus, Poisson's ratio, compressive strength, and accumulated energy, etc.) were obtained. At the same time, stress-strain curves were obtained, and the energy dissipation value at the time of tectonic crack formation was calculated using the law of conservation of energy. Figure 2 ):

[0067] ;

[0068] in: For dissipated energy, J;

[0069] The value is the triaxial principal stress, in MPa;

[0070] These are the triaxial principal strain values, which are dimensionless.

[0071] The elastic modulus is measured experimentally in GPa.

[0072] Poisson's ratio, measured experimentally, is dimensionless.

[0073] ;

[0074] ;

[0075] a represents confining pressure-related parameters, obtained through nonlinear regression fitting of the initial segment of the stress-strain curves under different confining pressures; b represents porosity. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0076] Porosity, measured experimentally;

[0077] c represents the mud content. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0078] The mud content was determined experimentally. ;

[0079] Peak stress value, MPa;

[0080] The value represents the peak strain, which is dimensionless.

[0081] Step 2: Establish rock fracture criteria by coupling the principles of minimum energy consumption and minimum action. The specific method is as follows:

[0082] (1) The principle of minimum energy consumption holds that structural cracks always follow the core idea of ​​"minimum energy consumption" during their generation, propagation, and termination. By introducing the generalized principle of minimum energy consumption, a fracture energy consumption rate characterization model under the constraint of the principle of minimum energy consumption is obtained:

[0083] ;

[0084] in: The energy dissipation rate during rupture is expressed in J / s.

[0085] The value is the triaxial principal stress, in MPa;

[0086] These are the triaxial principal strain values, which are dimensionless.

[0087] The elastic modulus is measured experimentally in GPa.

[0088] Poisson's ratio, measured experimentally, is dimensionless.

[0089] ;

[0090] ;

[0091] 'a' represents a confining pressure-related parameter, which is obtained by nonlinear regression fitting of the initial segment of the stress-strain curve under different confining pressures.

[0092] b represents porosity. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0093] Porosity, measured experimentally;

[0094] c represents the mud content. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0095] The mud content was determined experimentally. ;

[0096] Peak stress value, MPa;

[0097] The value represents the peak strain, which is dimensionless.

[0098] (2) Assume and The moments corresponding to the start and end of the structural crack propagation are respectively the moments when the crack begins to form and extends. Therefore, there exists an "optimal solution" for the action at these two moments. Based on the principle of minimum action, the Lagrangian function is introduced. To describe the characteristics of tectonic fracture development, a rock fracture criterion under the coupling of minimum energy consumption and minimum action is obtained:

[0099] ;

[0100] in: Let be the energy consumption rate function in the time domain, in J / s;

[0101] Let MPa be the triaxial principal stress function in the time domain.

[0102] The derivative of the triaxial principal stress function in the time domain;

[0103] The elastic modulus is measured experimentally in GPa.

[0104] Poisson's ratio, measured experimentally, is dimensionless.

[0105] ;

[0106] ;

[0107] 'a' represents a confining pressure-related parameter, which is obtained by nonlinear regression fitting of the initial segment of the stress-strain curve under different confining pressures.

[0108] b represents porosity. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0109] Porosity, measured experimentally;

[0110] c represents the mud content. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0111] The mud content was determined experimentally.

[0112] ;

[0113] Peak stress value, MPa;

[0114] The value represents the peak strain, which is dimensionless.

[0115] The rupture threshold is:

[0116] ;

[0117] in: ;

[0118] The energy consumption rate threshold is expressed in J / s.

[0119] Peak stress value, MPa;

[0120] The elastic modulus is measured experimentally in GPa.

[0121] Poisson's ratio, measured experimentally, is dimensionless.

[0122] ;

[0123] ;

[0124] 'a' represents a confining pressure-related parameter, which is obtained by nonlinear regression fitting of the initial segment of the stress-strain curve under different confining pressures.

[0125] b represents porosity. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0126] Porosity, measured experimentally;

[0127] c represents the mud content. Relevant parameters, and Those skilled in the art can verify and fine-tune this coefficient through conventional experiments based on specific lithology;

[0128] The mud content was determined experimentally.

[0129] ;

[0130] The value represents the peak strain, which is dimensionless.

[0131] when > At that time, the rock loses its stability and fractures.

[0132] Step 3: Establishment of a crack aperture characterization model under the coupling of the minimum energy consumption and minimum action principle. The specific method is as follows:

[0133] Based on the principle of minimum energy consumption, it is concluded that the development of tectonic fractures satisfies the premise of "minimum energy consumption." Combined with the principle of minimum action, it can be seen that there exists an "optimal path" for fracture propagation, such as... Figure 3 As shown, assuming a single crack is a cubic sheet, and the crack aperture is the only energy release path on the optimal crack propagation path, the sum of squares inequality yields:

[0134] ;

[0135] in, For the energy consumed in the fracture, J; The value is the triaxial principal stress, in MPa; Let the crack length be in meters (m). Crack aperture, mm; The angle between the maximum principal stress and the crack surface is °; The angle between the minimum principal stress and the crack surface is °.

[0136] The energy consumption of the structural crack is minimized on the optimal path if and only if the above equation holds true. Therefore, the structural crack aperture calculation model under the constraint of the minimum energy consumption - minimum action principle is as follows:

[0137] ;

[0138] in, For the energy consumed in the fracture, J;

[0139] The value is the triaxial principal stress, in MPa;

[0140] Let the crack length be in meters (m).

[0141] Crack aperture, mm;

[0142] The angle between the maximum principal stress and the crack surface is °;

[0143] The angle between the minimum principal stress and the crack surface is °.

[0144] Step 4: Establishment of a crack volume and linear density characterization model under the coupling of the minimum energy consumption and minimum action principle. The specific method is as follows:

[0145] According to the law of conservation of energy, the energy consumed by the fracture caused by the structural crack is conserved from the energy required for the crack to propagate into a solid mass (assuming the crack is a thin cubic sheet). Figure 4 As shown, the expression for the fracture volume density under the minimum energy consumption-minimum action theory coupling is obtained as follows:

[0146] ;

[0147] in: The density of the cracks within a unit cell, per unit ;

[0148] The crack strain energy density is expressed in units of... ;

[0149] The volume of a unit cell, in units ;

[0150] The total area of ​​the cracks, in units of ;

[0151] The energy plane density can be measured experimentally, and its unit is . ;

[0152] The value is the triaxial principal stress, in MPa;

[0153] The values ​​are triaxial principal strains and are dimensionless.

[0154] Peak stress value, MPa;

[0155] The value represents the peak strain, which is dimensionless.

[0156] The residual strain value is dimensionless.

[0157] 'a' represents a confining pressure-related parameter, obtained experimentally.

[0158] b represents porosity. Relevant parameters, and ; Porosity, measured experimentally;

[0159] c represents the mud content. Relevant parameters, and ; The mud content was determined experimentally. ;

[0160] ;

[0161] ;

[0162] This leads to the expression for crack linear density under the minimum energy consumption-minimum action theory:

[0163] ;

[0164] in: The crack linear density within a unit cell, per unit stripe ;

[0165] The density of the cracks within a unit cell, per unit ;

[0166] The total number of cracks within the unit cell;

[0167] The total area of ​​the cracks, in units of ;

[0168] For the surface area of ​​a single crack, in units ;

[0169] Let the crack length be in meters (m).

[0170] Crack aperture, mm;

[0171] The angle between the maximum principal stress and the crack surface is °.

[0172] Step 5: Construction of the heterogeneous mechanical parameter volume model and simulation of the elastoplastic geostress field. The specific method is as follows:

[0173] (1) In view of the complex lithological development characteristics, based on the static rock mechanics parameter data obtained from the rock mechanics experiment in step 1, the empirical formula is optimized on the basis of the existing logging empirical formula. The dynamic and static rock mechanics parameters are statistically classified, and the test points with differences caused by human factors are removed. The least squares method is applied to fit the optimal dynamic-static mechanical parameter correction regression curve, and the high-precision single-well continuous static mechanical parameters after dynamic-static correction are obtained. The parameters are then seismically inverted into the reservoir geological model to obtain the three-dimensional heterogeneous mechanical parameter body model of the target layer in the study area.

[0174] (2) Acoustic emission experiments were conducted on the core samples from the wells in the study area. Each group of samples consisted of 4 samples, one of which was taken from the vertical direction (parallel to the wellbore axis) and the other 3 from 3 directions at 45° angles to each other in the horizontal plane. The prepared samples were subjected to repeated loading experiments in the laboratory at a loading rate of 0.1 MPa. The acoustic emission signal of the rock sample under load was measured as a function of the load. The Kessel point was determined, and the average value of the Kessel point and the corresponding load was taken as the maximum normal stress on the core sample underground.

[0175] (3) Based on the structural trace method, faults and fractures are classified and matched. The rose diagram is used to conduct statistical analysis of fracture direction, identify the dominant group of conjugate fractures, and use the bisector of their conjugate angle as the direction of the maximum principal stress of the structural stress field.

[0176] (4) Based on the test results of the direction and magnitude of the tectonic stress, a three-dimensional geostress field simulation is carried out, the mechanical boundary of the model is set, the force is applied, and the evolution process of the three-dimensional geostress field is simulated.

[0177] Step 6: Construct quantitative predictions of crack parameters and verify the reliability of the results. The specific methods are as follows:

[0178] (1) Based on the previous three-dimensional geostress field simulation results, the instantaneous maximum principal stress, minimum principal stress, intermediate principal stress and corresponding principal strain parameters are extracted node by node. The rock fracture criterion, crack aperture, volume density and linear density characterization model are written into an executable Python program to automatically read the node data exported by the finite element method and realize the batch solution of parameters such as crack aperture, volume density and linear density, thereby clarifying the spatial distribution characteristics of structural crack parameters.

[0179] (2) Based on the above three-dimensional spatial distribution results of structural fracture parameters, through core samples (Table 1) and imaging (…),… Figure 5 Actual production dynamics () Figure 6 The accuracy of the prediction results was verified using data such as [data missing]. Application in the Hangjinqi block of the Sinopec North China Oilfield showed that the average agreement between the predicted fracture density and the core and imaging logging interpretation results exceeded 90%, and the correlation coefficient between the predicted fracture aperture and actual production dynamics reached 0.94, significantly higher than traditional quantitative fracture prediction methods. Therefore, if the agreement between the simulation results and actual data is greater than 90%, the simulation results are considered reliable; otherwise, the test results of the tectonic stress direction and magnitude are re-analyzed, the boundary condition loading scheme is corrected, and the three-dimensional geostress field is re-simulated, ultimately completing the three-dimensional quantitative characterization of tectonic fracture parameters.

[0180] Table 1. Prediction results of fracture density in core samples.

[0181] hashtag Core fracture density (fractures / m) Simulated crack density (cracks / m) Compliance rate % New 3 1.3 1.52 85.52 New 4 1.4 1.48 94.59 New 5 2.4 1.67 69.58 New 6 1.2 1.25 85.71 New 602 0.8 1.12 71.42 New 7 1.8 1.63 90.56 New 701 0.87 1.49 58.39 New 8 1.2 1.16 96.67 Jin 29 1.83 1.69 58.39 Jin138 1.76 1.58 96.67 Jin 150 1.5 1.23 82.00 Jin 79 1.3 1.40 92.86 Jin 153 1.33 1.37 97.08

[0182] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A quantitative characterization method for structural fracture parameters based on the principle of minimum energy consumption and minimum action coupling, characterized in that, Includes the following steps: Step 1: Calculate the energy dissipation of structural fractures based on rock mechanics experiments; Step 1 includes: Triaxial rock mechanics experiments were conducted to obtain rock mechanics parameters, including elastic modulus, Poisson's ratio, compressive strength, and cumulative energy; the energy dissipation value when tectonic cracks are generated was calculated based on stress-strain curves and the law of conservation of energy. The energy dissipation value is calculated using the following formula: ； in: For dissipated energy, J; The value is the triaxial principal stress, in MPa; The values ​​are triaxial principal strains and are dimensionless. The elastic modulus is measured experimentally in GPa. Poisson's ratio, measured experimentally, is dimensionless. ； ； 'a' represents a confining pressure-related parameter, which is obtained by nonlinear regression fitting of the initial segment of the stress-strain curve under different confining pressures. b represents porosity. Relevant parameters, and The coefficient is verified and fine-tuned through conventional experiments based on the specific lithology. Porosity, measured experimentally; c represents the mud content. Relevant parameters, and The coefficient is verified and fine-tuned through conventional experiments based on the specific lithology. The mud content was determined experimentally. ； Peak stress value, MPa; The value represents the peak strain, which is dimensionless. The residual strain value is dimensionless. Step 2: Establish a rock fracture criterion based on the coupling of the principle of minimum energy consumption and the principle of minimum action; Step 3: Establish a crack aperture characterization model based on the coupling of the minimum energy consumption principle and the minimum action principle; Step 4: Establish a crack volume density and linear density characterization model based on the coupling of the minimum energy consumption principle and the minimum action principle; Step 5: Construct a heterogeneous mechanical parameter volume model and perform elastoplastic stress field simulation; Step 6: Based on the rock fracture criterion, fracture aperture characterization model, fracture volume density and linear density characterization model, perform quantitative prediction of structural fracture parameters, and compare with actual data to complete reliability verification.

2. The method as described in claim 1, characterized in that, Step 2 includes: A fracture energy dissipation rate characterization model is obtained based on the principle of minimum energy dissipation; a time domain function is introduced based on the principle of minimum action to establish a rock fracture criterion under the coupling of minimum energy dissipation and minimum action; wherein, when the energy dissipation rate is greater than the energy dissipation rate threshold, the rock fractures.

3. The method as described in claim 1, characterized in that, Step 3 includes: Based on the principles of minimum energy consumption and minimum action, a crack aperture calculation model is derived, where crack aperture serves as the energy release pathway on the optimal crack propagation path.

4. The method as described in claim 3, characterized in that, The crack aperture calculation model is as follows: ； in, For the energy consumed in the fracture, J; The value is the triaxial principal stress, in MPa; Let the crack length be in meters (m). Crack aperture, mm; The angle between the maximum principal stress and the crack surface is °; The angle between the minimum principal stress and the crack surface is °.

5. The method as described in claim 1, characterized in that, Step 6 includes: Based on the three-dimensional geostress field simulation results, nodal stress and strain parameters are extracted; the parameters of structural fractures are solved in batches through programming; the prediction results are verified using core, imaging logging or production dynamic data, and are considered reliable when the consistency is greater than 90%.

6. A storage device storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

7. A computing device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the computer program to implement the method as described in any one of claims 1 to 5.

8. The computing device as claimed in claim 7, characterized in that, The computing device further includes an input / output interface for receiving rock mechanics experimental data and outputting predicted results of structural fracture parameters.

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