A high-precision fracture identification method based on well logging images under lithology constraints

Abstract

The present invention relates to the field of well logging image fracture identification technology, and specifically to a high-precision well logging image fracture identification method under lithology constraints, comprising the following steps: S1 establishing a dual-parameter lithology constraint index based on rock mechanics parameters and natural gamma; S2 extracting fracture information from well wall electrical imaging based on human-computer interaction; S3 extracting fracture information from array acoustic wave images; S4 establishing a comprehensive fracture discrimination index under multi-parameter constraints; S5 testing the fine fracture identification results under high-precision image information. The present invention first establishes a comprehensive lithology constraint condition index based on different lithology and rock mechanics related parameters, quantitatively calculates fracture related parameters and finely evaluates fracture geometric characteristics, evaluates fracture mechanical properties and fracture development types in combination with fracture geometric characteristics extracted by imaging, and finally establishes a comprehensive fracture identification index with multi-parameter constraints based on the results of imaging and array acoustic wave analysis to perform comprehensive fracture discrimination, thereby achieving accurate fracture identification.

Description

Technical Field

[0001] The invention relates to the technical field of well logging image fracture recognition, and in particular to a high-precision well logging image fracture recognition method under lithology constraints. Background Art

[0002] At present, the oil and gas production of fractured reservoirs accounts for a large proportion of the world's total oil and gas production. Therefore, it is of great significance to study fractured reservoirs. Fractures are good oil and gas migration channels and good oil and gas storage spaces. Therefore, efficient and accurate identification of fractures is of great significance.

[0003] At present, the main methods for fracture identification include geological, seismic and logging methods. Although the geological method can clearly see the specific characteristics of natural fractures, the amount of data required is large, and it is impossible to take cores at every depth in actual situations; for seismic methods to identify fractures, the recognition accuracy is high for large-scale fractures, but the recognition accuracy for small-scale fractures is low, and the cost of using seismic methods for fracture identification is high, so it has not been widely used; the most widely used fracture identification method is logging method, which mainly includes single conventional logging series identification, imaging logging identification, and multi-parameter conventional logging fusion identification of fractures. In a single logging series, fractures will lead to increased acoustic time difference, increased neutron porosity, reduced density, and reduced resistivity. However, due to the lithology, filling and fluid properties, the logging response is affected, causing fracture identification errors. Since imaging logging generates wellbore images based on the formation resistivity within the wellbore range, it is currently considered to be the most accurate logging method compared to conventional logging, and array acoustic logging has a longer detection range, but at this stage, how to identify fractures by image information fusion under the constraints of lithology still has certain difficulties.

[0004] In the prior art, a patent document with document number CN114609682A records a method, system and computer-readable storage medium for extracting electrical imaging fracture and hole parameters. The method comprises the following steps: obtaining a full-borehole coverage image of electrical imaging logging; performing an initial segmentation on the full-borehole coverage image of electrical imaging logging to obtain a fracture and hole sub-image of the initial segmentation of the full-bore electrical conductivity image; extracting target shape parameters of the fracture and hole sub-image of the initial segmentation of the full-bore electrical conductivity image; performing a secondary segmentation on the fracture and hole sub-image of the initial segmentation of the full-bore electrical conductivity image based on the target shape parameters of the fracture and hole sub-image of the initial segmentation of the full-bore electrical conductivity image to obtain fracture and hole sub-images; and extracting electrical imaging fracture and hole parameters based on the fracture and hole sub-images.

[0005] The above technical solution has complicated experimental methods and complex operations. It does not take into account the changes in the imaging image caused by the changes in lithology, resulting in errors in the extracted information. The resulting parameter errors will lead to inaccurate final extraction results. In addition, the results of the imaging images are not verified, and the accuracy of the established related models is not high.

[0006] Patent document with document number CN112780253A records a method for predicting and evaluating fractured reservoirs. The method determines the basic parameters of fracture development of fractured reservoirs based on geological data; conducts intersection analysis on the logging characteristics of fractured reservoirs to determine the geophysical identification basis of fractured reservoirs; conducts single-well fractured reservoir interpretation to determine the longitudinal development characteristics of fractured reservoirs and the development law of fractured reservoirs; combines the logging characteristics of fractured reservoirs with DST tests to conduct single-well research on the effectiveness of fractured reservoirs; uses the logging characteristics of fractured reservoirs to determine the predicted offset distance and azimuth of pre-stack five-dimensional fractures and calculate the development intensity and direction of fractured reservoirs; combines the results of fracture effectiveness evaluation to determine the effective fracture development zone; combines the results of structural reservoir research to complete comprehensive reservoir evaluation and well location recommendations.

[0007] The above technical solution requires complex data and is difficult to identify fractures. It also does not take into account the fracture identification illusion caused by lithology changes, resulting in errors in the accuracy of fracture identification. Summary of the invention

[0008] To solve the above problems, the present invention provides a high-precision logging image fracture identification method under lithology constraints, which is used to reduce the logging image fracture identification error, so as to accurately predict oil and gas and conduct precise oil and gas exploration and development.

[0009] In order to achieve the above object, the technical solution of the present invention is as follows: a high-precision logging image fracture identification method under lithology constraints, comprising the following steps:

[0010] S1 Establish a dual-parameter lithology constraint index based on rock mechanics parameters and natural gamma: Process the natural gamma logging curve to obtain the radioactive lithology constraint coefficient LITH gr The rock brittleness index is calculated by the rock mineral composition, and the brittle rock constraint coefficient LITH is obtained by processing bi According to the correlation between radioactive rock constraint coefficient and brittle rock constraint coefficient and lithology, the weight coefficient is determined to establish the comprehensive lithology constraint coefficient LITH con ;

[0011] S2 Extraction of fracture information from electrical imaging of the wellbore based on human-computer interaction: Under the established lithology constraint indicators, fracture information is extracted based on electrical imaging images to clarify fracture effectiveness and geometric feature information;

[0012] S3 array acoustic wave image crack information extraction: Based on the crack geometric features extracted from electrical imaging, the crack mechanical feature information is further extracted based on the array acoustic wave image;

[0013] S4 establishes a comprehensive crack identification index under multi-parameter constraints: calculates different weight coefficients according to different crack information, establishes a comprehensive crack identification index, and performs comprehensive crack identification based on the comprehensive crack identification index;

[0014] S5 Fracture fine identification result verification under high-precision image information: Based on the fracture parameters extracted from each logging series, the comprehensive index of the logging fracturing section is calculated and compared with the actual development of the logging fractures for verification.

[0015] Furthermore, S1 established a dual-parameter lithology constraint index based on rock mechanical parameters and natural gamma, and the radioactive lithology constraint coefficient LITH gr The calculation formula is as follows:

[0016]

[0017] Brittle rock constraint factor LITH bi The calculation formula is as follows:

[0018]

[0019] Comprehensive lithology constraint factor LITH con The calculation formula is as follows:

[0020]

[0021] According to LITH con The value is compared with the lithology of the standard well to determine its threshold, thereby determining the lithology constraint range.

[0022] Furthermore, S2 extracts crack information from wellbore electrical imaging based on human-computer interaction. The specific steps are as follows:

[0023] According to the crack information displayed by the electrical imaging image, numerical simulation is performed to obtain quantitative calculation of the crack geometric characteristics:

[0024]

[0025] Where W is the crack width, R m is the mud resistivity, R xo is the resistivity of the intrusion zone, k and n are constants related to the instrument, and A is the conductivity anomaly area caused by the crack, which is calculated by calibrating the imaging image and the resistivity curve;

[0026] The average hydrodynamic width of the crack is calculated by the following formula:

[0027]

[0028] Where, L is the length of the i-th crack, cm;

[0029] The apparent porosity of the fracture is calculated by the following formula:

[0030]

[0031] In the formula, S m is the rock surface area;

[0032] The apparent density of cracks is calculated by the following formula:

[0033]

[0034] Where H is the statistical window length; C is the borehole coverage, %; θ is the apparent dip angle of the i-th fracture.

[0035] Furthermore, the borehole electrical imaging logging uses 16 levels of grayscale to represent the relative changes in formation resistivity; the fractures are picked up based on human-computer interaction, and the fracture inclination and strike characteristics are extracted based on the sinusoidal curve morphology, and the fracture type is preliminarily identified based on the image color band brightness characteristics and the fracture image morphology.

[0036] Further, the crack information extraction of S3 array acoustic wave image is as follows:

[0037] Based on the fracture geometric feature information extracted by electrical imaging, the energy amplitude and attenuation value of the formation P-wave, S-wave and Stoneley wave slowness are extracted from the array waveform data, and the fast and slow S-waves of the formation are effectively separated to calculate the percentage size and direction of the formation velocity anisotropy;

[0038] Decode the array waveform data, integrate the receiver's monopolar P-wave, S-wave and Stoneley wave energies, calculate the P-wave, S-wave and Stoneley wave energies, and determine whether cracks are developed and their mechanical properties;

[0039] Based on the difference in wave velocity of shear waves with different polarization directions during propagation, the geometric characteristics of the cracks are judged and calibrated with the electrical imaging results.

[0040] Predict the crack direction based on the orientation of the fast shear wave surface;

[0041] Based on the size of anisotropy, combined with the geometric characteristics of electrical imaging, the development intensity of cracks is evaluated and the mechanical types of cracks are classified;

[0042] Compare the fracture direction with the direction of the maximum horizontal principal stress of the formation to predict the fracture effectiveness.

[0043] Furthermore, the effectiveness of predicting fractures includes that when the fracture direction differs from the angle of the maximum horizontal principal stress of the formation by 35°, the effectiveness of predicting the fracture system is poor; when the fracture direction is consistent with the angle of the maximum horizontal principal stress of the formation or the difference is less than 35°, the effectiveness of predicting the fracture system is good.

[0044] Furthermore, S4 establishes a comprehensive crack identification index under multi-parameter constraints. The specific steps are as follows:

[0045] Extract the time difference between P-wave and S-wave and Stoneley wave, and calculate the P-wave and S-wave time difference ratio coefficient DTi to reflect the crack inclination;

[0046]

[0047] Where DTC is the longitudinal wave time difference, μs / ft; DTS is the transverse wave time difference, μs / ft

[0048] Establishment of comprehensive index of crack type based on shear wave splitting data and DTi f ;

[0049]

[0050] Where a and b are the relevant weights of each parameter and the crack type; S1 is the fast shear wave time difference, μs / m; S2 is the slow shear wave time difference, μs / m;

[0051] Establish comprehensive crack identification indicators:

[0052]

[0053] Further, fracture inclination: DTi greater than 0.7 represents a low-angle fracture, and DTi less than 0.7 represents a high-angle fracture;

[0054] Crack type: Establish a comprehensive index of crack type. Between 0.1-0.25 is a translaminar shear crack, between 0.25-0.3 is a bedding sliding shear crack, and between 0.3-0.4 is a tension crack, thereby classifying different types of cracks.

[0055] The above scheme has the following beneficial effects:

[0056] 1. The present invention, under established lithological constraints, extracts characteristic information of cracks based on electrical imaging and array acoustic wave image features, establishes corresponding identification indicators, and finally calculates different weight coefficients based on different crack characteristic indicators to establish comprehensive crack indicators for comprehensive crack identification. Compared with the prior art, the method according to the present invention can efficiently identify cracks with an identification accuracy rate of more than 85%. Combined with actual production, the greater the comprehensive crack indicator, the higher the output.

[0057] 2. The present invention has a simple and convenient operation method. Under the lithology constraint condition, a comprehensive fracture identification index is constructed based on the well wall electrical imaging logging and array acoustic wave logging image information to efficiently identify fractures. By establishing different lithology constraints, a comprehensive fracture identification index is established in combination with imaging and array acoustic wave image information, thereby identifying fractures with high precision.

[0058] 3. The present invention, based on the extraction of logging responses of various types of fractures, imposes lithological conditions for constraints, and extracts geometric feature information of each fracture based on the image information of wellbore electrical imaging logging and array acoustic logging, so that each fracture sample is more precise and the final identification result is more accurate.

[0059] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of an embodiment of a method for high-precision well logging image fracture identification under lithology constraints of the present invention;

[0061] Figure 2 A fracture interpretation flow chart of an embodiment of a high-precision well logging image fracture identification method under lithology constraints of the present invention;

[0062] Figure 3 A schematic diagram of a variable density image of an embodiment of a method for identifying fractures using high-precision well logging images under lithology constraints of the present invention;

[0063] Figure 4 A schematic diagram of shear wave splitting of an embodiment of a high-precision well logging image fracture identification method under lithology constraints of the present invention;

[0064] Figure 5 It is a flow chart of array acoustic wave fracture identification of an embodiment of a high-precision well logging image fracture identification method under lithology constraints of the present invention;

[0065] Figure 6 A schematic diagram of comprehensive evaluation results of fractures in Well X according to an embodiment of a high-precision well logging image fracture identification method under lithology constraints of the present invention;

[0066] Figure 7 This is a schematic diagram of the relationship between comprehensive fracture indicators and production in an embodiment of a high-precision well logging image fracture identification method under lithology constraints of the present invention. DETAILED DESCRIPTION

[0067] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] The following is further described in detail through specific implementation methods:

[0069] Implementation example Figure 1-Figure 7 As shown: A high-precision logging image fracture identification method under lithology constraints includes the following steps:

[0070] S1 Establish a dual-parameter lithology constraint index based on rock mechanical parameters and natural gamma: Process the natural gamma log curve (GR) to obtain the radioactive lithology constraint coefficient LITH gr The rock brittleness index is calculated by the rock mineral composition, and the brittle rock constraint coefficient LITH is obtained by processing bi According to the correlation between radioactive rock constraint coefficient and brittle rock constraint coefficient and lithology, the weight coefficient is determined to establish the comprehensive lithology constraint coefficient LITH con .

[0071] In this embodiment, the calculation formula of the radioactive lithology constraint coefficient LITHgr is as follows:

[0072]

[0073] Brittle rock constraint factor LITH bi The calculation formula is as follows:

[0074]

[0075] Comprehensive lithology constraint factor LITH con The calculation formula is as follows:

[0076]

[0077] According to LITH con The value is compared with the lithology of the standard well to determine its threshold, thereby determining the lithology constraint range.

[0078] S2 Extraction of fracture information from wellbore electrical imaging based on human-computer interaction: Under the established lithology constraint indicators, fracture information is extracted based on electrical imaging images to clarify fracture effectiveness and geometric feature information.

[0079] Based on the established lithology constraints, combined with electrical imaging, the identification is based on the electrical imaging image. Due to its high resolution and full borehole scanning characteristics, imaging logging reflects macroscopic geological phenomena. Specifically, the steps are as follows:

[0080] According to the crack information displayed by the electrical imaging image, numerical simulation is performed to obtain quantitative calculation of the crack geometric characteristics:

[0081]

[0082] Where W is the crack width, R m is the mud resistivity, R xo is the resistivity of the intrusion zone, k and n are constants related to the instrument, and A is the conductivity anomaly area caused by the crack, which is calculated by calibrating the imaging image with the resistivity curve.

[0083] Based on the artificially picked cracks, the fracture average hydrodynamic width (FVAH) can also be calculated by the following formula, which mainly represents the influence of the crack scale on the fluid flow, reflects the permeability of the crack, and can more accurately reflect the effective crack information.

[0084]

[0085] Where L is the length of the i-th crack, cm.

[0086] The fracture apparent porosity (FVPA), i.e., the apparent opening area of ​​the fracture per unit area of ​​the wellbore wall, is calculated by the following formula.

[0087]

[0088] In the formula, S m is the surface area of ​​the rock.

[0089] The fracture apparent density (FVDC), i.e. the total number of fractures per meter of well section, is calculated by the following formula.

[0090]

[0091] Where H is the statistical window length; C is the borehole coverage, %; θ is the apparent dip angle of the i-th fracture.

[0092] In addition, the borehole electrical imaging logging in this embodiment uses 16 levels of grayscale to represent the relative change of formation resistivity; the higher the resistivity, the higher the image color band brightness, and the lower the resistivity, the darker the image color band brightness. Fractures are picked up based on human-computer interaction, and the fracture inclination and strike characteristics are extracted based on the sine curve morphology. The fracture type is determined based on the image color band brightness characteristics and the fracture image morphology, as shown in the attached figure. Figure 2 shown.

[0093] S3 array acoustic wave image crack information extraction: Based on the crack geometric features extracted by electrical imaging, crack information is further extracted based on array acoustic wave images.

[0094] Specifically, under different lithology constraints, due to the filling of mud in the open fracture, the acoustic wave velocity is attenuated, and obvious interference fringes appear on the variable density map, as shown in the attached figure. Figure 3 As shown in the figure, in order to quantitatively extract fracture information, the energy amplitude and attenuation value of the formation P-wave, S-wave and Stoneley wave slowness are extracted from the array waveform data, and the fast and slow S-waves of the formation are effectively separated to calculate the percentage size and direction of the formation velocity anisotropy, as shown in the figure. Figure 4 shown.

[0095] In this embodiment, the array waveform data is decoded, and the energies of the monopolar longitudinal waves, transverse waves and Stoneley waves of the eight pairs of receivers are integrated and calculated to calculate the energies of the longitudinal waves, transverse waves and Stoneley waves. The energy attenuation can reflect the degree of fracture development and the effectiveness of the fractures. In the fracture development section, the energies of the longitudinal waves, transverse waves and Stoneley waves all attenuate to varying degrees, especially the Stoneley waves, which have a greater attenuation degree. Based on this, it can be judged whether the fractures are developed and the information on the mechanical properties of the rock can be obtained in combination with the longitudinal and transverse waves and the rock density.

[0096] In vertical fractured formations (HTI media), when the shear wave propagates in the direction parallel to the fracture plane, the corresponding formation elastic modulus is higher and the propagation speed is faster, while when the shear wave propagates in the normal direction of the fracture plane, the corresponding formation elastic modulus is lower and the propagation speed is slower, which leads to the difference in the wave speed of shear waves with different polarization directions during the propagation process, and then the fracture information can be judged. In addition, the magnitude of the shear wave time difference anisotropy is proportional to the degree of development of the fracture. Therefore, the magnitude of the shear wave time difference anisotropy can be used to evaluate the development intensity of the fracture. The orientation of the fast shear wave surface can predict the direction of the fracture. By comparing the direction of the fracture and the direction of the maximum horizontal principal stress of the formation, the effectiveness of the fracture can be further predicted. Combined with the analysis of the example, it is found that when the fracture direction differs from the direction of the maximum horizontal principal stress of the formation by 35°, the effectiveness of the fracture system is poor; when the angles of the two are consistent or the difference is less than 35°, the effectiveness of the fracture system is good.

[0097] S4 establishes a comprehensive crack identification index under multi-parameter constraints: different weight coefficients are calculated according to different crack information, a comprehensive crack identification index is established, and comprehensive crack identification is performed based on the comprehensive crack identification index.

[0098] Specifically, based on the above array acoustic wave recognition principle, single well array acoustic wave interpretation is performed (see attached Figure 5 As shown), extract the time difference between the longitudinal and transverse waves and the Stoneley wave, extract the time difference between the longitudinal and transverse waves and the Stoneley wave, and calculate the longitudinal and transverse wave time difference ratio coefficient DTi to reflect the crack inclination;

[0099]

[0100] Where DTC is the longitudinal wave time difference, μs / ft; DTS is the transverse wave time difference, μs / ft.

[0101] Establishment of comprehensive index of crack type based on shear wave splitting data and DTi f ;

[0102]

[0103] Where a and b are the relevant weights of each parameter and crack type; S1 is the fast shear wave time difference, μs / m; S2 is the slow shear wave time difference, μs / m.

[0104] Where a and b are the relevant weights of each parameter and crack type; S1 is the fast shear wave time difference, μs / m; S2 is the slow shear wave time difference, μs / m.

[0105] Establish comprehensive crack identification indicators:

[0106]

[0107] In this embodiment, for the crack inclination: DTi greater than 0.7 represents a low-angle crack, and DTi less than 0.7 represents a high-angle crack; for the crack type: a comprehensive index of the crack type is established: between 0.1-0.25 is a translaminar shear crack, between 0.25-0.3 is a bedding sliding shear crack, and between 0.3-0.4 is a tension crack, thereby classifying different types of cracks.

[0108] S5 Fracture fine identification result verification under high-precision image information: Based on the fracture parameters extracted from each logging series, the comprehensive index of the logging fracturing section is calculated and compared with the actual development of the logging fractures for verification.

[0109] In this example, based on lithological constraints, electrical imaging and array acoustic wave images are combined to extract fracture-related information, quantitatively calculate fracture geometric features, and match the calculated results with actual fractures. Different geometric feature information is assigned corresponding weights, and a comprehensive fracture identification index is established to conduct a comprehensive fracture evaluation system. The calculated comprehensive index of the X1 well fracturing section is 0.83, indicating that its fractures are relatively developed, and the comprehensive index of the X2 well fracturing section is 0.35, indicating that its fractures are not developed. This example method can efficiently identify fractures with an identification accuracy of more than 85%, and combined with actual production, the larger the comprehensive fracture index, the higher the production. As shown in the attached figure Figure 6 , 7 shown.

[0110] Obviously, the above examples are only examples for clear explanation, and are not intended to limit the implementation methods. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from them are still within the protection scope of the invention.

Claims

1. A high-precision logging image fracture identification method under lithology constraints, characterized in that: The steps include: S1 Establish a dual-parameter lithology constraint index based on rock mechanics parameters and natural gamma: Process the natural gamma logging curve to obtain the radioactive lithology constraint coefficient LITH gr The rock brittleness index is calculated by the rock mineral composition, and the brittle rock constraint coefficient LITH is obtained by processing bi According to the correlation between radioactive rock constraint coefficient and brittle rock constraint coefficient and lithology, the weight coefficient is determined to establish the comprehensive lithology constraint coefficient LITH con ; S2 Extraction of fracture information from electrical imaging of wellbore wall based on human-computer interaction: Extract fracture information based on electrical imaging images under the established lithology constraint indicators; S3 array acoustic wave image fracture information extraction: Extract fracture information based on array acoustic wave images under the established lithology constraint indicators; S4 establishes a comprehensive crack identification index under multi-parameter constraints: calculates different weight coefficients according to different crack information, establishes a comprehensive crack identification index, and performs comprehensive crack identification based on the comprehensive crack identification index; S5 Fracture fine identification result verification under high-precision image information: Based on the fracture parameters extracted from each logging series, the comprehensive index of the logging fracturing section is calculated and compared with the actual development of the logging fractures; S1 In the establishment of lithology constraint index, the radioactive lithology constraint coefficient LITH gr The calculation formula is as follows: Brittle rock constraint factor LITH bi The calculation formula is as follows: Comprehensive lithology constraint factor LITH con The calculation formula is as follows: According to LITH con The value of is compared with the lithology of the standard well to determine its threshold, thereby determining the lithology constraint range; S2 is based on human-computer interaction to extract crack information from wellbore electrical imaging. The specific steps are as follows: According to the crack information displayed by the electrical imaging image, numerical simulation is performed to obtain quantitative calculation of the crack geometric characteristics: Where W is the crack width, R m is the mud resistivity, R xo is the resistivity of the intrusion zone, k and n are constants related to the instrument, and A is the conductivity anomaly area caused by the crack, which is calculated by calibrating the imaging image and the resistivity curve; The average hydrodynamic width of the crack is calculated by the following formula: Where, L i is the length of the i-th crack, W i is the width of the i-th crack; The apparent porosity of the fracture is calculated by the following formula: In the formula, S m is the rock surface area; The apparent density of cracks is calculated by the following formula: Where H is the statistical window length; C is the borehole coverage rate; θ i is the apparent inclination angle of the i-th crack; S3 array acoustic wave image crack information extraction, the specific steps are as follows: Based on the fracture geometric feature information extracted by electrical imaging, the energy amplitude and attenuation value of the formation P-wave, S-wave and Stoneley wave slowness are extracted from the array waveform data, and the fast and slow S-waves of the formation are effectively separated to calculate the percentage size and direction of the formation velocity anisotropy; Decode the array waveform data, integrate the receiver's monopolar P-wave, S-wave and Stoneley wave energies, calculate the P-wave, S-wave and Stoneley wave energies, and determine whether cracks are developed and their mechanical properties; Based on the difference in wave velocity of shear waves with different polarization directions during propagation, the geometric characteristics of the cracks are judged and calibrated with the electrical imaging results. Predict the crack direction based on the orientation of the fast shear wave surface; Based on the size of anisotropy, combined with the geometric characteristics of electrical imaging, the development intensity of cracks is evaluated and the mechanical types of cracks are classified; Compare the fracture direction with the direction of the maximum horizontal principal stress of the formation to predict the fracture effectiveness; S4 establishes comprehensive crack identification indicators. The specific steps are as follows: Extract the time difference between P-wave and S-wave and Stoneley wave, and calculate the P-wave and S-wave time difference ratio coefficient DTi to reflect the crack inclination; Where, DTC is the longitudinal wave time difference; DTS is the transverse wave time difference; Establishment of comprehensive index of crack type based on shear wave splitting data and DTi f ; Where a', b' are the relevant weights of each parameter and the crack type; S1 is the fast shear wave time difference; S2 is the slow shear wave time difference; Establish comprehensive crack identification indicators:

2. The high-precision well logging image fracture identification method under lithology constraints according to claim 1 is characterized in that: Well wall electrical imaging logging uses 16 levels of grayscale to represent the relative change of formation resistivity; fractures are picked up based on human-computer interaction, and the fracture dip and strike characteristics are extracted based on the sine curve morphology. The fracture type is preliminarily identified based on the image color band brightness characteristics and the fracture image morphology.

3. The high-precision well logging image fracture identification method under lithology constraints according to claim 1 is characterized in that: The effectiveness of predicting fractures includes: when the fracture direction differs from the maximum horizontal principal stress angle of the formation by 35°, the effectiveness of predicting the fracture system is poor; when the fracture direction is consistent with the maximum horizontal principal stress angle of the formation or the difference is less than 35°, the effectiveness of predicting the fracture system is good.

4. The high-precision well logging image fracture identification method under lithology constraints according to claim 1 is characterized in that: Fracture inclination: DTi greater than 0.7 represents a low-angle fracture, and DTi less than 0.7 represents a high-angle fracture; Crack type: Establish a comprehensive index of crack type. Between 0.1-0.25 is a translaminar shear crack, between 0.25-0.3 is a bedding sliding shear crack, and between 0.3-0.4 is a tension crack, thereby classifying different types of cracks.

Images