Rock mechanical parameter logging intelligent evaluation method considering continental facies shale fabric characteristics

Abstract

The invention relates to the technical field of oil exploration, and discloses a rock mechanical parameter logging intelligent evaluation method considering continental facies shale fabric characteristics. The method comprises the following steps: 1, dividing shale lithofacies, identifying and determining the bedding development thickness of a sample through an optical microscope, determining a granularity experiment of the sample through granularity analysis, and forming mineral composition and bedding factor lithofacies division; 2, constructing a typical logging facies based on a rock electricity response relationship, and dividing the imaging logging facies of the research area according to natural gamma curves of conventional logging and imaging logging, core deposition structure characteristics and imaging logging image characteristics; 3, testing the longitudinal and transverse wave velocities and the porosity of different shale lithofacies, and calculating the dynamic elastic modulus and the Poisson's ratio. Through fine division and description, the method for accurately evaluating the bedding development condition, the particle size characteristics and the mineral components of the shale sample is provided, the testing time is short, the cost is low, and accurate evaluation of shale can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration, and particularly to an intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale. Background Art

[0002] As an important unconventional oil and gas resource, shale oil and gas has been increasingly highly regarded by countries around the world in recent years. Shale oil and gas mainly exist in the pores or fractures of shale reservoirs in adsorbed or free states, and due to factors such as tectonic background, sedimentary environment, provenance conditions, and diagenesis, the properties of shale reservoirs vary greatly, showing strong heterogeneity.

[0003] The heterogeneity of shale not only includes heterogeneity in lithology, thickness, organic matter abundance, structure and texture, mineral composition, reservoir physical properties, and reservoir space structure, but also strong heterogeneity exists in terms of microscopic components, biomarker compounds, laminae and their combinations. And as the most characteristic sedimentary structure of shale, laminae are widely developed in shale formations in the form of bedding structures, which directly lead to obvious changes in shale heterogeneity and anisotropy, and further have a great impact on shale reservoir properties, brittleness, and rock mechanical properties. And compared with marine shale laminae, the laminae of continental shale often do not appear alone, but often appear together with other laminae, and the symbiotic combination of multiple laminae leads to diverse changes in shale laminae, bringing great challenges to the evaluation of the rock mechanical properties of continental shale reservoirs.

[0004] Currently, for the evaluation of the rock mechanics of shale reservoirs, the traditional method mainly uses a triaxial high-temperature and high-pressure rock mechanics experimental testing instrument to measure the static Young's modulus and Poisson's ratio of rocks. However, the testing time for a single sample in this method is long and the cost is high, and the measuring points are relatively discrete, which is not conducive to the continuous evaluation of rock mechanical properties in the vertical direction. Therefore, it is often necessary to use an acoustic wave tester to measure the dynamic Young's modulus and Poisson's ratio of rocks, and construct a static-dynamic correction relationship model for rock mechanics, so as to achieve the continuous evaluation of single-well rock mechanics using logging curves. However, most of the currently constructed static-dynamic correction relationship models are established for marine shale reservoirs. However, the rock fabric of continental shale reservoirs is complex, with many types of lamina structures, and microfractures and other microstructures are mostly developed. Therefore, this model is not suitable for continental shale with strong lamina fabric heterogeneity. Therefore, it is necessary to construct a static-dynamic correction relationship model considering the differences in lamina fabric and pore microfractures on the basis of the division of continental shale lamina fabric, and apply it to the logging evaluation and interpretation of the rock mechanics of continental complex shale lithofacies reservoirs, which is of great significance for the fine evaluation of continental shale oil reservoirs and the selection of longitudinal sweet spots.

[0005] Therefore, the present invention provides an improved and more accurate method for evaluating shale rock mechanical parameters. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides an intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale, which solves the problems of poor accuracy, long testing time and high cost of the existing logging intelligent evaluation methods.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: an intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale, including the following steps;

[0008] Step 1: Divide the shale lithofacies. Determine the bedding development thickness of the sample through optical microscope identification and the grain size experiment of the sample through grain size analysis to form the lithofacies division of mineral composition and bedding factors;

[0009] Step 2: Construct typical logging facies based on the electro-lithological response relationship. Divide the imaging logging facies of the study area according to the natural gamma curve of conventional logging and imaging logging, the core sedimentary structure characteristics and the imaging logging image characteristics;

[0010] Step 3: Carry out the longitudinal and transverse wave velocities and porosity tests of different shale lithofacies, and calculate the dynamic elastic modulus and Poisson's ratio;

[0011] Step 4: Carry out rock mechanical tests under the formation conditions of different shale lithofacies to obtain their static elastic modulus and Poisson's ratio;

[0012] Step 5: Construct the dynamic-static correction relationship of rocks for different shale lithofacies types;

[0013] Step 6: Conduct the continuity evaluation of rock mechanical parameters based on the intelligent identification of logging facies.

[0014] Preferably, in Step 1, the samples are determined through whole-rock mineral X-ray diffraction analysis and clay mineral X-ray diffraction analysis based on the color, oiliness and bedding development degree of the shale.

[0015] Preferably, in Step 2, specific comparative analysis is carried out through the relationship between the lithological characteristics of core-taking and logging data and the corresponding logging signals.

[0016] Preferably, in Step 3, the longitudinal and transverse wave propagation velocities in the rock are obtained by processing and interpreting acoustic logging data, and combined with density logging data, continuous dynamic rock mechanical parameters are calculated.

[0017] Preferably, the formula for calculating Young's modulus by the logging method in Step 3 is:

[0018]

[0019] The formula for calculating Poisson's ratio by the logging method is:

[0020]

[0021] Among them, V p is the longitudinal wave velocity, V s is the shear wave velocity, Δt p is the longitudinal wave travel time difference, Δt s is the shear wave travel time difference, E d is the dynamic elastic modulus, P d is the dynamic Poisson's ratio.

[0022] Preferably, in the fourth step, a triaxial compression test is used to measure the static rock mechanics parameters.

[0023] Preferably, the formula for the rock mechanics parameters in the fourth step is:

[0024] B = ε 3 / ε 1

[0025] p s =(Bσ 1 -σ 3 / [σ 3 (2B - 1)-σ 1 )

[0026] E s =(σ 1 -2P s σ 3 ) / σ 3

[0027] Among them, B is the ratio of the radial strain to the transverse strain in the confining pressure unloading stage, ε 1 is the axial strain, ε 3 is the radial strain, p s is the static Poisson's ratio, E s is the static Young's modulus, σ 1 is the axial stress, σ 3 is the radial stress.

[0028] Preferably, in the fifth step, based on the crossplot analysis of the dynamic and static Young's modulus and Poisson's ratio test data, a conversion model considering pores and lithology is obtained.

[0029] Preferably, in the sixth step, the logging facies of different lithofacies and the dynamic and static conversion models are put into the system language for learning. Different lithologies are identified through the first step, and then through the lithology-electricity identification relationship. Finally, based on the obtained dynamic and static Young's modulus, templates of different lithologies are established to identify different lithologies.

[0030] The present invention provides an intelligent logging evaluation method for rock mechanics parameters considering the fabric characteristics of continental shale. It has the following beneficial effects:

[0031] 1. The present invention is finely divided and described to provide a method for accurately evaluating the bedding development, grain size characteristics, and mineral composition of shale samples. Meanwhile, logging facies division results are provided. By analyzing natural gamma curves, core sedimentary structure characteristics, and imaging logging image characteristics, the strata are divided into different types of facies, providing a basis for strata interpretation and reservoir analysis, helping to better understand the characteristics of the strata and reservoir properties, and providing an important reference basis for oil and gas exploration.

[0032] 2. The present invention fully considers the influence of different laminations and porosity inside shale on its rock mechanical parameters, and based on the unique characteristics of continental shale, a new set of intelligent logging evaluation methods is established. This method has the advantages of short testing time and low cost, and can effectively evaluate shale. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is the lithofacies division map of the present invention, where a is the first-level partition of lithofacies naming, and b is the second-level partition of lithofacies naming;

[0034] Figure 2 It is the schematic diagram of the laminated silty shale facies of the present invention;

[0035] Figure 3 It is the schematic diagram of the thick-layer shell limestone facies of the present invention;

[0036] Figure 4 It is the schematic diagram of the interbedded shale and shell limestone facies of the present invention;

[0037] Figure 5 It is the schematic diagram of the shale facies with calcareous streaks of the present invention;

[0038] Figure 6 It is the schematic diagram of the clay shale facies of the present invention;

[0039] Figure 7 It is the schematic diagram of the logging crossplot plate of five typical logging facies of the present invention;

[0040] Figure 8 It is the schematic diagram of the stress-strain curve characteristics of the Y1-2 sample of the present invention;

[0041] Figure 9 It is the schematic diagram of the stress-strain curve characteristics of the Y2-2 sample of the present invention;

[0042] Figure 10 It is the schematic diagram of the stress-strain curve characteristics of the Y3-2 sample of the present invention;

[0043] Figure 11 It is the schematic diagram of the stress-strain curve characteristics of the Y4-2 sample of the present invention;

[0044] Figure 12 Schematic diagram of the stress-strain curve characteristics of the Y5-2 sample of the present invention;

[0045] Figure 13 Schematic diagram of the stress-strain curve characteristics of the Y6-2 sample of the present invention;

[0046] Figure 14 Schematic diagram of the stress-strain curve characteristics of the Y7-2 sample of the present invention;

[0047] Figure 15 Schematic diagram of the stress-strain curve characteristics of the D1-2 sample of the present invention;

[0048] Figure 16 Schematic diagram of the stress-strain curve characteristics of the D2-2 sample of the present invention;

[0049] Figure 17 Schematic diagram of the stress-strain curve characteristics of the D3-2 sample of the present invention;

[0050] Figure 18 Layout of the dynamic and static correction diagram of the laminated silty shale of the present invention;

[0051] Figure 19 Cross-sectional diagram of logging interpretation of single-well rock mechanics parameters of the present invention. Detailed implementation manners

[0052] Next, in combination with the accompanying drawings of the present invention, the technical solutions of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment:

[0054] Please refer to the attached Figure 1 - attached Figure 19 , the embodiment of the present invention provides an intelligent logging evaluation method for rock mechanics parameters considering the fabric characteristics of continental shale, including the following steps;

[0055] Step 1, divide the shale lithofacies, determine the bedding development thickness of the sample through optical microscope identification, and determine the particle size experiment of the sample through particle size analysis to form the lithofacies division of mineral composition and bedding factors;

[0056] Step 2, construct a typical logging facies based on the rock-electric response relationship, and divide the imaging logging facies of the study area according to the natural gamma curve of conventional logging and imaging logging, the core sedimentary structure characteristics and the imaging logging image characteristics.

[0057] Step 3: Conduct the measurement of the P-wave and S-wave velocities and porosity of different shale lithofacies, and calculate the dynamic elastic modulus and Poisson's ratio.

[0058] Step 4: Conduct the rock mechanics tests under the formation conditions of different shale lithofacies to obtain their static elastic modulus and Poisson's ratio.

[0059] Step 5: Construct the dynamic-static correction relationships of rocks for different shale lithofacies types.

[0060] Step 6: Conduct the continuity evaluation of rock mechanics parameters based on the intelligent identification of logging facies.

[0061] Based on the identification by optical microscopy and grain size analysis, determine the bedding development, grain size characteristics and mineral composition of shale samples, conduct lithofacies division, use conventional logging and imaging logging data, and construct typical logging facies by analyzing natural gamma curves, core sedimentary structure characteristics and imaging logging image characteristics, divide the formation into different types of facies, conduct the measurement of the P-wave and S-wave velocities and porosity of samples of different shale lithofacies, calculate the dynamic elastic modulus and Poisson's ratio, understand the elastic characteristics and pore structure of rocks, conduct rock mechanics tests under formation conditions, including stress-strain tests and compressive strength tests, obtain the static elastic modulus and Poisson's ratio of rocks, based on the data and results obtained in the previous steps, construct the dynamic-static correction relationships of rocks for different shale lithofacies types, establish the dynamic-static conversion relationship of rock mechanics parameters, and conduct the continuity evaluation of rock mechanics parameters based on the intelligent identification results of logging facies to reveal the distribution law of underground rock mechanics characteristics.

[0062] In Step 1, based on the color, oiliness and bedding development degree of shale, determine the samples through whole-rock mineral X-ray diffraction analysis and clay mineral X-ray diffraction analysis.

[0063] Optimize different shale samples based on the color, oiliness and bedding development degree of shale, and conduct whole-rock mineral X-ray diffraction analysis and clay mineral X-ray diffraction analysis to determine the mineral composition of the samples, total organic carbon content analysis to determine the organic matter content of the samples, optical microscopy identification to determine the bedding development thickness of the samples, and grain size analysis to determine the grain size of the samples. The experimental formation of the mineral composition and bedding factor lithofacies division scheme is divided into 5 categories, the lithofacies of Jurassic shale is divided, and the logging identification method and plate of strip-triangle diagram of shale are established (attached Figure 1 ). Specifically, it includes:

[0064] Lithofacies 1: Laminated silty shale, and the silty materials appear in several laminae. From the perspective of sedimentary structure, horizontal bedding and small wedge-shaped cross-bedding are common in silty mudstone. In addition, wavy bedding can be seen in some cores.

[0065] Lithofacies 2: Thick-bedded shell limestone. The internal structure of the shell limestone is relatively disordered. The grain size of a single layer is usually gradually changing and may exhibit a series of sedimentary structures, including ripple bedding, parallel-wave bedding, etc.

[0066] Lithofacies 3: Interbedded shale and shell limestone facies: This type of lithofacies commonly shows unequal-thickness interbedding of light gray shell limestone bands and dark gray argillaceous bands in the core.

[0067] Lithofacies 4: Shale with calcareous bands facies, which is overall gray-dark gray. The light-colored part reflects a higher carbonate content, while the dark-colored part has a higher felsic or clay mineral content.

[0068] Lithofacies 5: Argillaceous shale, mainly gray-black or black. The single-layer thickness of the shale ranges from 0.5 m to 5 m. The grain size is very fine without a granular feeling, rich in organic matter, and developed horizontal laminations.

[0069] In step two, through core sampling, a specific comparative analysis is carried out on the relationship between the lithological characteristics of the logging data and the corresponding logging signals.

[0070] According to the relationship between the lithological characteristics of the core sampling and logging data and the corresponding logging signals (as shown in Table 1 below), a specific comparative analysis is carried out to establish the lithology-electricity relationship. Then, based on the natural gamma curves of conventional logging and imaging logging, the core sedimentary structure characteristics and the imaging logging image characteristics, the imaging logging facies of the study area are divided into 5 categories: laminated silty shale, thick-bedded shell limestone, interbedded shale and shell limestone facies, shale with calcareous bands facies, and argillaceous shale (attached Figure 2 -attached Figure 6 );

[0071] Table 1: Schematic table of logging response values for five typical logging facies:

[0072]

[0073] Among them, the laminated silty shale has the characteristics of high gamma and low resistivity; the thick-bedded shell limestone has the characteristics of low gamma and low resistivity; the interbedded shale and shell limestone facies has the characteristics of low gamma and extremely high resistivity, and there are abrupt changes in both gamma and resistivity in the shell layer; the shale with calcareous bands facies has the characteristics of high gamma, low resistivity, and there is an abrupt change in the resistivity curve in the calcareous band; the argillaceous shale has the characteristics of high gamma, high acoustic wave, and low resistivity, and four cross-plot plates of RT-AC, CNL-DEN, DEN-PE, and K-TH are used to identify different lithologies (attached Figure 7 ).

[0074] In step three, by processing and interpreting the acoustic logging data, the longitudinal and transverse wave propagation velocities in the rock are obtained, and combined with the density logging data, continuous dynamic rock mechanical parameters are calculated.

[0075] By processing and interpreting acoustic logging data, the longitudinal and shear wave propagation velocities in the rock can be obtained. Combining with density logging data, continuous dynamic rock mechanical parameters can be calculated, enabling a comprehensive understanding of the mechanical properties of the rock and providing important references for engineering design, reservoir evaluation, and oil and gas development.

[0076] Acoustic logging data record the propagation times of various acoustic waves in the rock. By processing and interpreting the acoustic logging data, the longitudinal and shear wave propagation velocities in the rock can be obtained. Combining with density logging data, continuous dynamic rock mechanical parameters (as shown in Table 2 below) can be calculated;

[0077] The formula for calculating Young's modulus by the logging method in Step 3 is:

[0078]

[0079] The formula for calculating Poisson's ratio by the logging method is:

[0080]

[0081] where V p is the longitudinal wave velocity, V s is the shear wave velocity, Δt p is the longitudinal wave slowness, Δt s is the shear wave slowness, E d is the dynamic elastic modulus, P d is the dynamic Poisson's ratio;

[0082] Table 2: Test data table of rock physical parameters for five typical shale lithofacies:

[0083]

[0084]

[0085] In Step 4, a triaxial compression test is used to determine the static rock mechanical parameters.

[0086] By using a triaxial compression test to determine the static rock mechanical parameters, important parameters such as the compressive strength, elastic modulus, and Poisson's ratio of the rock can be provided, providing accurate data support for the evaluation of rock mechanical properties and engineering design.

[0087] The formula for the rock mechanical parameters in Step 4 is:

[0088] B = ε 3 / ε 1

[0089] p s = (Bσ 1 -σ 3 / [σ 3(2B - 1)-σ 1 )

[0090] E s =(σ 1 -2P s σ 3 ) / σ 3

[0091] where B is the ratio of radial to transverse strain during the confining pressure unloading stage, ε 1 is the axial strain, ε 3 is the radial strain, p s is the static Poisson's ratio, E s is the static Young's modulus, σ 1 is the axial stress, σ 3 is the radial stress.

[0092] Carry out rock mechanics tests under different shale lithofacies formation conditions to obtain their static elastic modulus and Poisson's ratio. Use uniaxial or triaxial compression experiments to measure static rock mechanics parameters. Taking the triaxial rock compression test as an example, apply confining pressure with radial hydraulic oil and axial stress. Use axial and radial strain gauges to monitor the axial and radial deformations of the core plug, and output the stress-strain curve to analyze and calculate rock mechanics parameters such as cohesion, internal friction angle, Young's modulus, and Poisson's ratio;

[0093] Appendix Figure 8 - Appendix Figure 17 is the strain curve of some samples under the condition of confining pressure of 20 Mpa and temperature of 50°C.

[0094] In step five, based on the cross-analysis of the dynamic and static Young's modulus and Poisson's ratio test data, a conversion model considering pores and lithology is obtained.

[0095] Construct the dynamic and static correction relationships of rocks by shale lithofacies type. Based on the cross-analysis of the dynamic and static Young's modulus and Poisson's ratio test data, a conversion model considering pores and lithology can be obtained, specifically including:

[0096] Dynamic and static Young's modulus conversion model of laminated silty shale considering pores and lithology:

[0097]

[0098] R 2 =0.9759

[0099] Dynamic and static Poisson's ratio conversion model of laminated silty shale considering pores and lithology:

[0100]

[0101] R 2 =0.9849

[0102] Dynamic-static Young's modulus conversion model for thick-layer shell limestone considering pore and lithology:

[0103]

[0104] R 2 = 0.981

[0105] Dynamic-static Poisson's ratio conversion model for thick-layer shell limestone considering pore and lithology:

[0106]

[0107] R 2 = 0.9906

[0108] Dynamic-static Young's modulus conversion model for interbedded shale and shell limestone facies considering pore and lithology:

[0109]

[0110] R 2 = 0.9469

[0111] Dynamic-static Poisson's ratio conversion model for interbedded shale and shell limestone facies considering pore and lithology:

[0112]

[0113] R 2 = 0.8738

[0114] Dynamic-static Young's modulus conversion model for shale with calcareous bands considering pore and lithology:

[0115]

[0116] R 2 = 0.9897

[0117] Dynamic-static Poisson's ratio conversion model for shale with calcareous bands considering pore and lithology:

[0118]

[0119] R 2 = 0.9564

[0120] Dynamic-static Young's modulus conversion model for clay shale considering pore and lithology:

[0121]

[0122] R 2 = 0.8934

[0123] Dynamic-static Poisson's ratio conversion model of argillaceous shale considering pores and lithology:

[0124]

[0125] R 2 = 0.5456

[0126] where E d , E s are the dynamic and static Young's moduli respectively; P d , P s are the dynamic and static Poisson's ratios respectively, is the porosity;

[0127] Appendix Figure 18 is the dynamic-static correction chart of laminated silty shale.

[0128] In step six, the logging facies of different lithofacies and the dynamic-static conversion model are put into the system language for learning. Different lithologies are identified through step one, and then through the lithology-electricity identification relationship. Finally, based on the obtained dynamic and static Young's moduli, templates of different lithologies are established to identify different lithologies.

[0129] Based on the intelligent identification of logging facies, the continuity evaluation of rock mechanics parameters is carried out. The logging facies of different lithofacies and the dynamic-static conversion model are put into the system language for learning. Different lithologies are identified through step one, and then through the lithology-electricity identification relationship, the corresponding logging facies are intelligently corresponded. Then, based on the obtained dynamic and static Young's moduli, dynamic and static Poisson's ratios, and porosity, different lithologies are intelligently identified through the established templates of different lithologies (Appendix Figure 19 ).

[0130] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale, characterized in that, it includes the following steps; Step 1: Divide the shale lithofacies. Determine the bedding development thickness of the sample through optical microscope identification and the grain size experiment of the sample through grain size analysis to form the lithofacies division of mineral composition and bedding factors; Step 2: Construct typical logging facies based on the response relationship between rock electricity. Divide the imaging logging facies of the study area according to the natural gamma curve of conventional logging and imaging logging, the core sedimentary structure characteristics and the imaging logging image characteristics; Step 3: Carry out the longitudinal and transverse wave velocities and porosity tests of different shale lithofacies, and calculate the dynamic elastic modulus and Poisson's ratio; Step 4: Carry out rock mechanical tests under the formation conditions of different shale lithofacies to obtain their static elastic modulus and Poisson's ratio; Step 5: Construct the dynamic-static correction relationship of rocks by shale lithofacies type; Step 6: Conduct the continuity evaluation of rock mechanical parameters based on the intelligent identification of logging facies.

2. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale according to claim 1, characterized in that, in the said Step 1, the sample is determined based on the color, oiliness, and lamination development degree of the shale through whole-rock mineral X-ray diffraction analysis and clay mineral X-ray diffraction analysis.

3. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale according to claim 1, characterized in that, in the said Step 2, specific comparative analysis is carried out through the relationship between the lithological characteristics of core-taking and logging data and the corresponding logging signals.

4. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale according to claim 1, characterized in that, in the said Step 3, the longitudinal and transverse wave propagation velocities in the rock are obtained by processing and interpreting acoustic logging data, and combined with density logging data, continuous dynamic rock mechanical parameters are calculated.

5. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale according to claim 1, characterized in that, the formula for calculating Young's modulus by the logging method in the said Step 3 is: the formula for calculating Poisson's ratio by the logging method is: Among them, V p is the longitudinal wave velocity, V s is the shear wave velocity, Δt p is the longitudinal wave travel time difference, Δt s is the shear wave travel time difference, E d is the dynamic elastic modulus, P d is the dynamic Poisson's ratio.

6. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale according to claim 1, characterized in that, in the said Step 4, a triaxial compression experiment is used to measure the static rock mechanical parameters.

7. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale according to claim 1, characterized in that, the formula for the rock mechanical parameters in the said Step 4 is: B = ε 3 / ε 1 p s = (Bσ 1 - σ 3 / [σ 3 (2B - 1)-σ 1 ) E s = (σ 1 - 2P s σ 3 ) / σ 3 Among them, B is the ratio of the radial strain to the transverse strain during the confining pressure unloading stage, ε 1 is the axial strain, ε 3 is the radial strain, p s is the static Poisson's ratio, E s is the static Young's modulus, σ 1 is the axial stress, σ 3 is the radial stress.

8. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale according to claim 1, characterized in that, in the said Step 5, based on the crossplot analysis of dynamic-static Young's modulus and Poisson's ratio test data, a conversion model considering pores and lithology is obtained.

9. An intelligent logging evaluation method for rock mechanical parameters considering the fabric characteristics of continental shale according to claim 1, characterized in that, In step six, the logging facies and dynamic-static conversion models of different lithofacies are put into the system language for learning. Different lithologies are identified through step one, and then through the lithology-electricity identification relationship. Finally, based on the obtained dynamic and static Young's moduli, templates of different lithologies are established to identify different lithologies.

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