Strike-slip fault identification method and device based on multiple seismic attribute fusion technology

Abstract

The application relates to the technical field of oil and gas exploration, and discloses a strike-slip fracture identification method and device based on a variety of seismic attribute fusion technologies. The method first acquires seismic data and geological background data of a target area, then performs seismic profile interpretation and calculates coherence, curvature, tensor thickness, symmetry illumination attribute and maximum likelihood attribute based on the geological background and the seismic data, and finally determines the preferred attributes according to the scale of the fracture and performs quantitative fusion to identify the strike-slip fracture zone. The method is suitable for fractures of different scales, different attributes are selected for quantitative fusion, the advantages of different characteristics are complemented, the identification precision and sensitivity of the fracture are improved, and the research work of researchers in the aspects of the fracture and well position is effectively assisted.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, specifically to a strike-slip fault identification method and device based on multi-seismic attribute fusion technology. Background Technology

[0002] In seismic exploration trap evaluation, faults are a crucial element. Early oilfield exploration primarily focuses on structural traps, and faults within these traps play a vital role. Faults can serve as conduits for oil and gas migration, potentially existing for short- and long-distance migration in both lateral and vertical directions. These faults are sometimes referred to as "source faults" or "oil-controlling faults." Faults can also seal off oil and gas, especially reverse faults, where the target reservoir is precisely aligned with tight mudstone, forming fault nose structures or fault block structures.

[0003] Faults are an important research object in structural geology. Based on the relative movement of the two sides of a fault, they can be classified into normal faults, reverse faults, and strike-slip faults. Faults are divided into upper and lower (left and right) blocks, with the fault plane as the boundary. When there is relative movement between the two blocks, the block with greater activity (larger slip) is usually the active block, and the other is the passive block. In the study of normal and reverse faults, the identification of the active and passive blocks based on methods such as earthquakes and field outcrops is relatively common. However, there is still no systematic analysis and research on the identification of the active and passive blocks of strike-slip faults.

[0004] However, the fracture patterns within formations are often complex. For example, the same formation may contain other types of fracture zones besides strike-slip fault zones, such as extensional fault zones. These different types of fault zones form different matrices and have different impacts on oil and gas reservoirs, but their morphological characteristics are similar, which can easily interfere with the identification of strike-slip fault zones. This often results in technical problems such as large errors and poor accuracy in identifying strike-slip fault zones.

[0005] Chinese patent application CN111796323A discloses a method and system for identifying strike-slip fault boundaries and main sections. This method determines the boundaries and main sections of strike-slip fault zones by calculating three attributes at the marker layer: gradient structure tensor, coherence, and amplitude variation rate. While the existing technology involves calculating seismic attributes such as gradient structure tensor and coherence, it does not comprehensively consider the characterization characteristics of different seismic attributes. Therefore, it suffers from significant errors in identifying strike-slip faults and cannot effectively identify faults at different scales. Summary of the Invention

[0006] To address the problems and shortcomings of the existing technologies, this invention proposes a strike-slip fracture identification method and device based on multi-seismic attribute fusion technology. This method selects different attributes for quantitative fusion of fractures at different scales to achieve complementary advantages between different features, thereby improving the accuracy and sensitivity of fracture identification and effectively assisting researchers in their research on fractures and well locations.

[0007] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows:

[0008] A strike-slip fault identification method based on multi-seismic attribute fusion technology mainly includes the following steps:

[0009] Acquire seismic data and geological background information for the target area;

[0010] Based on the geological background and seismic data, seismic profiles are interpreted and seismic properties such as coherence, curvature, tensor thickness, symmetry illumination, and maximum likelihood are calculated.

[0011] Based on the scale of the fault, the preferred seismic attributes are determined and strike-slip fault zones are identified through quantitative fusion.

[0012] The process of determining preferred attributes and quantitatively fusing them to identify strike-slip fault zones includes: normalizing the selected seismic attributes, then calculating and quantitatively fusing them according to different weighting ratios to establish a fused body, and identifying strike-slip fault zones based on the fault characteristics of the geological bodies in the fused body.

[0013] Preferably, the coherence attribute calculation method includes:

[0014] Based on seismic data, obtain a distribution map of sampling points within the target area and calculate the maximum coherence value for each sampling point;

[0015] Based on the maximum coherence value of each sampling point, the coherence attribute map of the region is obtained.

[0016] Preferably, calculating the maximum coherence value for each sampling point includes:

[0017] A sampling point coordinate system and a rhombus boundary diagram are set for the sampling points. The sampling points serve as the origin of the sampling point coordinate system and the intersection of the major and minor axes of the rhombus boundary diagram. The major axis of the rhombus boundary diagram coincides with the X-axis of the sampling point coordinate system, and the minor axis of the rhombus boundary diagram coincides with the Y-axis of the sampling point coordinate system.

[0018] Rotate the rhombus boundary map with the sampling point as the rotation center to obtain the valid points within the rhombus boundary map;

[0019] A time window is set, and the coherence value corresponding to each rotation angle is calculated based on the data within the time window of the valid points in the diamond boundary diagram, thereby determining the maximum coherence value of the sampling point.

[0020] Preferably, the curvature attributes include minimum negative curvature, maximum positive curvature, maximum curvature, minimum curvature, Gaussian curvature, and average curvature, and the calculation method for the curvature attributes includes:

[0021] Extract seismic properties along the layer;

[0022] Based on the extracted seismic attributes along the layers, calculate the second derivative of the seismic attributes;

[0023] Calculate the curvature factor based on the second derivative of the seismic properties;

[0024] Different curvature properties are calculated based on the curvature factor.

[0025] Based on the same inventive concept, this invention also discloses a strike-slip fault identification device based on multi-seismic attribute fusion technology. The device includes a data acquisition module, a data processing module, and a strike-slip fault identification module; wherein...

[0026] The data acquisition module is used to acquire seismic data and geological background information for the target area;

[0027] The data processing module is used to interpret seismic profiles and calculate coherence, curvature, tensor thickness, and symmetry illumination properties based on geological background and seismic data.

[0028] The strike-slip fracture identification module is used to determine the preferred attributes based on the fracture scale and to quantitatively fuse and identify strike-slip fracture zones.

[0029] A computer device includes a memory, a processor, and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, it implements the steps of the strike-slip fault identification method based on multiple seismic attribute fusion technology described above.

[0030] A computer-readable storage medium storing a computer program, which, when executed in a computer processor, implements the steps of the above-described strike-slip fault identification method based on multi-seismic attribute fusion technology.

[0031] The beneficial effects of this invention are:

[0032] 1. This invention identifies strike-slip faults based on a multi-seismic attribute fusion technology. Considering the different characteristics of different types of coherent bodies in characterizing different fault zones, as well as the interrelationship between karst features and strike-slip fault zones, this invention acquires multiple attributes such as coherence, curvature, tensor thickness, and symmetry illumination, and integrates these attributes to establish a fusion body. Then, by utilizing the fault characteristics of the geological bodies in the fusion body, strike-slip fault zones are identified. This solves the technical problems of large errors and poor accuracy in identifying strike-slip fault zones in existing methods, achieving the technical effect of effectively distinguishing extensional fault zones by integrating the characterization characteristics of different seismic attributes, and accurately identifying strike-slip fault zones.

[0033] 2. This invention performs seismic attribute analysis on seismic data volumes, then extracts seismic attribute volumes along the target layer from seismic horizon data, and finally uses a newly developed curvature analysis technique to perform curvature analysis on the seismic attribute volumes along the layers, obtaining seismic curvature volumes along the layers. These seismic curvature volumes allow for precise characterization of fault, fracture, and channel morphology, as well as reservoir effectiveness, thus overcoming the limitations of conventional methods in identifying faults and fractures. This assists researchers in their work on faults and fractures, effectively calculates seismic attributes, and has better fault directionality identification capabilities. Attached Figure Description

[0034] The foregoing and hereinafter detailed description of the invention becomes clearer when read in conjunction with the following drawings, in which:

[0035] Figure 1 This is a flowchart of the method of the present invention;

[0036] Figure 2 This is a structural diagram of the device of the present invention;

[0037] Figure 3 This is a schematic diagram of the hierarchical fracture identification method of the present invention;

[0038] Figure 4 This is a schematic diagram of the maximum likelihood properties of the present invention;

[0039] Figure 5 This is a schematic diagram of the coherence properties of the present invention;

[0040] Figure 6 This is a schematic diagram of the symmetrical lighting properties of the present invention;

[0041] Figure 7 This is a schematic diagram of the curvature properties of the present invention;

[0042] Figure 8 This is a schematic diagram of the fusion properties of the principal components of the present invention;

[0043] Figure 9 This is a schematic diagram of the CMY fusion attributes of the present invention. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solutions of this invention, specific embodiments will be used to further illustrate the technical solutions for achieving the objectives of this invention. It should be noted that the technical solutions claimed by this invention include, but are not limited to, the following embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort should fall within the scope of protection of this invention.

[0045] Faults are an important research object in structural geology. Based on the relative movement of the two sides of a fault, they can be classified into normal faults, reverse faults, and strike-slip faults. Faults are divided into upper and lower (left and right) blocks, with the fault plane as the boundary. When there is relative movement between the two blocks, the block with greater activity (larger slip) is usually the active block, and the other is the passive block. In the study of normal and reverse faults, the identification of the active and passive blocks based on methods such as earthquakes and field outcrops is relatively common. However, there is still no systematic analysis and research on the identification of the active and passive blocks of strike-slip faults.

[0046] However, the fracture patterns within formations are often complex. For example, the same formation may contain other types of fracture zones besides strike-slip fault zones, such as extensional fault zones. These different types of fault zones form different matrices and have different impacts on oil and gas reservoirs, but their morphological characteristics are similar, which can easily interfere with the identification of strike-slip fault zones. This often results in technical problems such as large errors and poor accuracy in identifying strike-slip fault zones.

[0047] Currently, while some existing fracture identification methods involve the calculation of seismic attributes such as tensors and coherence, these attributes are used separately and individually when identifying fractures, without considering the attribute fusion process. Therefore, existing identification methods typically suffer from problems such as large identification errors and poor accuracy.

[0048] Based on this, embodiments of the present invention propose a method and apparatus for identifying strike-slip faults based on a multi-seismic attribute fusion technology. This method acquires multiple seismic attributes such as coherence, curvature, tensor thickness, and symmetry illumination, and integrates these seismic attributes to establish a fusion body. Then, it utilizes the fault characteristics of the geological bodies in the fusion body to identify strike-slip fault zones, achieving the technical effect of effectively distinguishing extensional fault zones by integrating the characterization characteristics of different seismic attributes, so as to accurately identify strike-slip fault zones.

[0049] To facilitate understanding of the technical solution of this invention, this invention first introduces and explains the method for identifying strike-slip faults based on the fusion of multiple seismic attributes.

[0050] This embodiment discloses a method for identifying strike-slip faults based on multiple seismic attribute fusion techniques, as detailed in the appendix to the specification. Figure 1 This method mainly includes the following steps.

[0051] Step S1. Obtain seismic data and geological background information for the target area.

[0052] Step S2. Based on the geological background and seismic data, interpret the seismic profile and calculate various seismic attributes such as coherence, curvature, tensor thickness, and symmetry illumination.

[0053] In this embodiment, the method for calculating coherence attributes includes the following steps:

[0054] Step 1: Obtain a distribution map of sampling points within the template area based on seismic data;

[0055] Step 2: For each sampling point in the sampling point distribution map, repeat the following sub-steps 1-3:

[0056] Sub-step 1: Set up a sampling point coordinate system and a rhombus boundary diagram for the sampling points. The sampling points serve as the origin of the sampling point coordinate system and the intersection of the major and minor axes of the rhombus boundary diagram. The major axis of the rhombus boundary diagram coincides with the X-axis of the sampling point coordinate system, and the minor axis of the rhombus boundary diagram coincides with the Y-axis of the sampling point coordinate system.

[0057] Sub-step 2: Rotate the rhombus boundary map with the sampling point as the rotation center to obtain the valid points within the rhombus boundary map;

[0058] Sub-step 3: Set a time window. Based on the data within the time window of the valid points in the diamond boundary diagram, calculate the coherence value corresponding to each rotation angle, and then determine the maximum coherence value of the sampling point.

[0059] Step 3: Obtain the coherence attribute map of the target region based on the maximum coherence value of each sampling point.

[0060] Specifically, the formula for calculating the coherence value is as follows:

[0061]

[0062] Where C3 is the coherence value, C is the seismic data volume consisting of J traces, each with N samples, DN*J is the covariance matrix, where DN*J is the seismic subvolume consisting of J traces and N samples participating in the calculation, and is the transpose of DN*J, T r (C) is the trace of the matrix, representing the energy of the covariance matrix, and λ1 is the largest eigenvalue, representing the dominant energy. jj It is an element on the diagonal of the matrix, λ j is the non-negative eigenvalue of the matrix, J is the number of seismic traces involved in the calculation, and j is the seismic trace number involved in the calculation.

[0063] In this embodiment, the coherence attribute diagram is shown in the appendix of the specification. Figure 5 .

[0064] In this embodiment, the method for analyzing the curvature attribute includes the following steps:

[0065] Step 1: Extract seismic attributes along the layers;

[0066] Step 2: Based on the extracted seismic attributes along the layers, calculate the second derivative of the seismic attributes. The specific calculation process for the second derivative of the seismic attributes is as follows:

[0067] [Ix,Iy]=Gradient(seis_attr,resolution)

[0068] Where: Gradient represents the first derivative, seis_attr represents the seismic attribute volume, resolution represents the resolution of the derivative, and Ix and Iy represent the first derivatives in the x and y directions, respectively;

[0069] [Ixx,Ixy]=Gradient(Ix,resolution)

[0070] [Iyx,Iyy]=Gradient(Iy,resolution)

[0071] Where: Ixx represents the second derivative in the x direction, Ixy represents the second derivative in the x and y directions, Iyx represents the second derivative in the y and x directions, and Iyy represents the second derivative in the y direction;

[0072] Step 3: Calculate the curvature factor based on the second derivative of the seismic attributes. The expression for calculating the curvature factor is as follows:

[0073]

[0074] Where a, b, c, d, and e represent different curvature factors;

[0075] Step 4: Calculate different curvature attributes based on the curvature factor. These curvature attributes include: minimum negative curvature, maximum positive curvature, maximum curvature, minimum curvature, Gaussian curvature, and average curvature. When identifying strike-slip fractures, typically one or two of these curvature attributes are selected for identification.

[0076] The expression for calculating the minimum negative curvature is:

[0077] The expression for calculating the maximum positive curvature is:

[0078] The expression for calculating the maximum curvature is:

[0079] The expression for calculating the minimum curvature is:

[0080] The expression for calculating the Gaussian curvature is:

[0081] The expression for calculating the average curvature is as follows:

[0082] In this embodiment, the schematic diagrams of the maximum likelihood property, coherence property, symmetric illumination property, and curvature property are shown in the appendix to the specification. Figures 4-7 .

[0083] Step S3. Determine the preferred seismic attributes based on the scale of the fault, and then quantitatively fuse the selected seismic attributes to identify strike-slip fault zones.

[0084] In this invention, it should be noted that the scale of the fracture is classified according to the accuracy of the seismic data identification. In an earthquake, obvious displacement of the same phase axis indicates a main fault, slight displacement-deflection indicates a secondary fault, and weak amplitude change-deflection indicates a microcrack.

[0085] The geometry of the fracture can be described using the following parameters:

[0086] Based on the seismic profile interpretation results, fault growth index, fault displacement, and fault activity rate are obtained. Based on these factors, the staging differences in fault activity are determined. The expression for calculating the fault growth index is as follows:

[0087] GI = H1 / H2;

[0088] Where GI is the fault growth index, H1 is the thickness of the hanging wall, and H2 is the thickness of the footwall.

[0089] The expression for calculating the fault activity rate is as follows:

[0090] VF = D / T;

[0091] Where VF is the fault activity rate, D is the fault displacement, and T is the deposition time;

[0092] The fault elevation difference was obtained through manual measurement.

[0093] For the main fault, it is mainly characterized by the discontinuity of the seismic wave reflection phase axis. This type of fault can be clearly shown using curvature or coherence properties. For the secondary fault, a method combining coherence and curvature properties can be used for analysis. However, the final fault results still need to be verified by cross-sections. By combining plan and cross-section, seismic noise artifacts can be eliminated. For smaller faults, some seismic properties that respond strongly to micro-fractures, such as maximum likelihood, symmetric illumination, and AFE, should be used. The identification results of micro-fractures are usually verified using drilling data.

[0094] Therefore, under normal circumstances, coherence or curvature properties, or the fusion of these two properties, can only identify large-scale fractures, but it is difficult to identify small-scale and micro-cracks. Therefore, the maximum likelihood property and the symmetric illumination property need to be combined for identification.

[0095] In this invention, after selecting appropriate attributes based on the scale of the fracture, the selected seismic attributes are normalized, and then quantitatively fused according to different weighted proportions to establish a fused body. Based on the fracture characteristics of the geological bodies in the fused body, strike-slip fault zones are finally identified. The fusion method can include principal component fusion and CMY fusion, etc., as detailed in the appendix to the specification. Figure 8 and attached Figure 9 .

[0096] Principal component fusion primarily employs linear projection to project data into a new coordinate space, resulting in a new composition where the first principal component contains the most information. After transformation, the principal components are uncorrelated, and the information content of each component decreases as the principal component number increases. The three largest components are selected for fusion.

[0097] CMY fusion is similar to RGB. The three primary colors of an image can be divided into red, green and blue, and there is also a CMY expression method. It uses image color fusion method to perform attribute fusion.

[0098] During attribute fusion, the degree of participation (attribute ratio) of different attributes in the fusion process is obtained through extensive attribute analysis and testing. For example, refer to the appendix of the instruction manual. Figure 3 For cases involving main faults, a fusion body can be established using 30%-40% curvature properties and 60%-70% coherence properties. Then, based on the fault characteristics of the geological bodies within the fusion body, the strike-slip fault zone can be identified.

[0099] For secondary fractures, strike-slip fracture zones can be identified by combining 63% curvature-coherence fusion and 37% maximum likelihood attribute fusion.

[0100] For fracture development zones, strike-slip fault zones can be identified by fusing 42% maximum likelihood attributes and 58% symmetry illumination attributes.

[0101] Furthermore, based on the same inventive concept, embodiments of the present invention also provide a strike-slip fracture identification device based on multiple seismic attribute fusion technology. This device is used to implement the strike-slip fracture identification method described above, as described in the following embodiments. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated. See the appendix to the specification. Figure 2 Specifically, the device may include: a data acquisition module 201, a data processing module 202, and a strike-slip fracture identification module 203. The structure will be described in detail below.

[0102] The data acquisition module 201 is used to acquire seismic data and geological background information of the target area;

[0103] The data processing module 202 is used to interpret seismic profiles and calculate coherence, curvature, tensor thickness and symmetry illumination properties based on geological background and seismic data.

[0104] The strike-slip fracture identification module 203 is used to determine preferred attributes and quantitatively fuse and identify strike-slip fracture zones based on the fracture scale.

[0105] It should be noted that the systems, devices, models, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described in this specification as various units based on their functions. Of course, in implementing this invention, the functions of each unit can be implemented in one or more software and / or hardware.

[0106] Furthermore, in this specification, adjectives such as first and second may only be used to distinguish an element or action, without necessarily implying any actual such relationship or order.

[0107] Furthermore, embodiments of the present invention also provide a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, it implements the steps of any of the above methods.

[0108] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed in a computer processor, implements the steps of any of the above methods.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to hinder the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A strike-slip fault identification method based on multi-seismic attribute fusion technology, characterized in that, Includes the following steps: Acquire seismic data and geological background information for the target area; Based on the geological background and seismic data, seismic profiles are interpreted and coherence, curvature, tensor thickness, symmetry illumination, and maximum likelihood properties are calculated. Based on the scale of the fracture, the preferred attributes are determined and quantitative fusion is performed to identify strike-slip fracture zones; The process of determining the preferred attributes and quantitatively fusing them to identify strike-slip fault zones includes: normalizing the selected seismic attributes, then calculating and quantitatively fusing them according to different weighting ratios to establish a fusion body, and identifying strike-slip fault zones based on the fault characteristics of the geological bodies in the fusion body. The scale of the fracture includes main faults, secondary faults, and microfractures. For main faults, the preferred properties are curvature and coherence; for secondary faults, the preferred properties are coherence and maximum likelihood; and for microfractures, the preferred properties are maximum likelihood and symmetric illumination. For the main fault, a fusion body is established by adopting 30%-40% curvature attribute and 60%-70% coherence attribute. Then, based on the fault characteristics of the geological bodies in the fusion body, the strike-slip fault zone is finally identified. For secondary faults, strike-slip fault zones were identified by combining 63% curvature-coherence fusion and 37% maximum likelihood attribute fusion. For microcracks, strike-slip fracture zones were identified by fusing 42% maximum likelihood properties and 58% symmetry illumination properties.

2. The strike-slip fault identification method based on multi-seismic attribute fusion technology according to claim 1, characterized in that, The method for calculating the coherence attribute includes: Based on seismic data, obtain a distribution map of sampling points within the target area and calculate the maximum coherence value for each sampling point; Based on the maximum coherence value of each sampling point, the coherence attribute map of the region is obtained.

3. The strike-slip fault identification method based on multi-seismic attribute fusion technology according to claim 2, characterized in that, The calculation of the maximum coherence value for each sampling point includes: A sampling point coordinate system and a rhombus boundary diagram are set for the sampling points. The sampling points serve as the origin of the sampling point coordinate system and the intersection of the major and minor axes of the rhombus boundary diagram. The major axis of the rhombus boundary diagram coincides with the X-axis of the sampling point coordinate system, and the minor axis of the rhombus boundary diagram coincides with the Y-axis of the sampling point coordinate system. Rotate the rhombus boundary map with the sampling point as the rotation center to obtain the valid points within the rhombus boundary map; A time window is set, and the coherence value corresponding to each rotation angle is calculated based on the data within the time window of the valid points in the diamond boundary diagram, thereby determining the maximum coherence value of the sampling point.

4. The strike-slip fault identification method based on multi-seismic attribute fusion technology according to claim 1, characterized in that, The method for calculating the curvature property includes: Extract seismic properties along the layer; Based on the extracted seismic attributes along the layers, calculate the second derivative of the seismic attributes; Calculate the curvature factor based on the second derivative of the seismic properties; Different curvature properties are calculated based on the curvature factor.

5. The strike-slip fault identification method based on multi-seismic attribute fusion technology according to claim 1, characterized in that, The curvature properties include: minimum negative curvature, maximum positive curvature, maximum curvature, minimum curvature, Gaussian curvature, and mean curvature.

6. The strike-slip fault identification method based on multi-seismic attribute fusion technology according to claim 1, characterized in that, The preferred method for quantitative fusion of attributes is principal component fusion or CMY fusion.

7. The strike-slip fault identification method based on multi-seismic attribute fusion technology according to claim 3, characterized in that, The coherence value corresponding to each rotation angle is calculated as follows: ； in, For coherence values, For have Each path has A seismic data volume composed of sample points Let be the covariance matrix, where For those participating in the calculation The seismic subbody composed of N sample points is transpose, Let be the trace of the matrix, representing the energy of the covariance matrix. It is the largest eigenvalue, representing the dominant energy. These are the elements on the diagonal of the matrix. These are the non-negative eigenvalues ​​of the matrix. The number of seismic traces included in the calculation. The seismic trace number is used for the calculation.

8. A strike-slip fault identification device based on multi-seismic attribute fusion technology, characterized in that, The device is used to implement the strike-slip fracture identification method according to any one of claims 1-7, comprising: The data acquisition module is used to acquire seismic data and geological background information for the target area; The data processing module is used to interpret seismic profiles and calculate coherence, curvature, tensor thickness, and symmetry illumination properties based on geological background and seismic data. The strike-slip fracture identification module is used to determine the preferred attributes based on the fracture scale and to quantitatively fuse and identify strike-slip fracture zones.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, characterized in that, When the processor executes the computer program, it implements the method steps of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed in a computer processor, implements the method of any one of claims 1-7.

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