A strike-slip fault three-dimensional detection method and system

Abstract

The application provides a strike-slip fault three-dimensional detection method and system, including the following steps: step 1, obtaining strike-slip fault structure feature body data; step 2, obtaining a strike-slip fault three-dimensional grid model; step 3, obtaining a strike-slip fault three-dimensional porosity model; step 4, constructing a strike-slip fault elastic parameter model; step 5, iteratively optimizing the obtained strike-slip fault elastic parameter model to obtain an optimized strike-slip fault elastic parameter model; and step 6, detecting a to-be-detected strike-slip fault according to the optimized strike-slip fault elastic parameter model. The application breaks through the conventional two-dimensional profile and depth slice model, provides a practical and effective method for establishing an Ordovician carbonate rock strike-slip fault elastic parameter model, and further improves the detection precision.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration technology, specifically relating to a three-dimensional detection method and system for strike-slip fractures. Background Technology

[0002] Strike-slip fault detection is an important part of the exploration and development of deep Ordovician oil and gas reservoirs. Existing methods for strike-slip fault detection and identification mainly rely on seismic attribute methods and manual interpretation by geologists based on the seismic response characteristics of strike-slip faults.

[0003] In terms of seismic attribute methods, the current conventional approach mainly involves extracting seismic post-stack attributes such as seismic curvature volume attributes, coherence volume attributes, maximum likelihood attributes, and ant volume attributes, and then performing thresholding to obtain the target detection range of strike-slip faults. These methods can achieve large-scale representation of strike-slip fault zones to a certain extent. However, due to the development characteristics of steep dip angles of strike-slip faults, the seismic response is mainly caused by diffraction waves. The structural size shown by the seismic response on the post-stack profile is much larger than the actual geological conditions, which is the so-called "magnification effect". Due to this factor, the detection results of these post-stack attribute methods for strike-slip faults cannot reflect the actual width of the strike-slip faults.

[0004] In terms of manual interpretation methods, the identification of strike-slip fractures is greatly influenced by human factors, and the accuracy of the results requires forward modeling verification or actual development verification. The limitations of existing methods restrict the detection accuracy of strike-slip fractures. Summary of the Invention

[0005] The purpose of this invention is to provide a three-dimensional detection method and system for strike-slip fracture, which solves the problem of inaccurate detection accuracy in the existing technology for strike-slip fracture detection.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] The present invention provides a three-dimensional detection method for strike-slip fracture, comprising the following steps:

[0008] Step 1: Obtain the feature data of the strike-slip fracture structure;

[0009] Step 2: Obtain the three-dimensional mesh model of the strike-slip fracture based on the strike-slip fracture structural feature data obtained in Step 1;

[0010] Step 3: Based on the obtained strike-slip fracture structural feature data and strike-slip fracture three-dimensional mesh model, obtain the strike-slip fracture three-dimensional porosity model;

[0011] Step 4: Construct a strike-slip fracture elastic parameter model based on the obtained three-dimensional porosity model of strike-slip fracture;

[0012] Step 5: Iteratively optimize the obtained strike-slip fracture elastic parameter model to obtain the optimized strike-slip fracture elastic parameter model.

[0013] Step 6: Detect the strike-slip fracture under test according to the optimized strike-slip fracture elastic parameter model.

[0014] Preferably, in step 1, the data of the characteristic body of the strike-slip fracture structure is obtained by the following method:

[0015] First, the second eigenvalue of the gradient structure tensor is calculated based on post-stack seismic data;

[0016] Secondly, the obtained second eigenvalue of the gradient structure tensor is compared with a preset threshold to obtain the processed second eigenvalue of the gradient structure tensor.

[0017] Finally, the second eigenvalue of each processed gradient structure tensor is subjected to tensor voting processing using a preset voting domain to obtain the strike-slip fracture structure feature data.

[0018] Preferably, in step 2, the three-dimensional mesh model of the strike-slip fracture is obtained, specifically by:

[0019] Mark the strike-slip fracture vector lines on the cross-sections and slices of the strike-slip fracture structural feature data obtained in step 1, respectively.

[0020] All the obtained strike-slip fault vector lines are discretized using a seismic grid to obtain the discretized strike-slip fault vector lines.

[0021] Integral interpolation is performed between every two fracture lines in the obtained discretized strike-slip fracture vector lines to obtain the initial three-dimensional strike-slip fracture mesh.

[0022] The initial strike-slip fracture 3D mesh was Gaussian smoothed to obtain the strike-slip fracture 3D mesh model.

[0023] Preferably, in step 3, the three-dimensional porosity model of the strike-slip fracture is obtained, specifically by:

[0024] First, obtain the actual porosity logging curves;

[0025] Secondly, the obtained actual porosity logging curves are coarsened to obtain coarsened porosity logging curves. The obtained coarsened porosity logging curves correspond to the strike-slip fracture structure feature data.

[0026] Next, the coarsened porosity logging curves and strike-slip fracture structure feature data are linearly fitted to obtain the spatial variation function of the model porosity.

[0027] Finally, the strike-slip fracture feature data corresponding to all grid points in the three-dimensional mesh model of the strike-slip fracture are substituted into the spatial variation function of the porosity of the model to obtain the three-dimensional porosity model of the strike-slip fracture.

[0028] Preferably, in step 4, the elastic parameter model for strike-slip fracture is constructed, specifically by:

[0029] Based on the three-dimensional porosity model of the strike-slip fracture obtained in step 3, an elastic parameter model of the strike-slip fracture is constructed by combining it with the rock physics model.

[0030] Preferably, in step 5, the obtained strike-slip fracture elastic parameter model is iteratively optimized. Specifically, the method is as follows:

[0031] The elastic parameter model of the strike-slip fracture obtained in step 4 is used to perform wave equation forward modeling with an actual seismic exploration and observation system, and the seismic response after model simulation is calculated.

[0032] Obtain the structural feature body corresponding to the processed seismic response;

[0033] Obtain the lateral relative distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture in the post-stack seismic data in step 1; and iteratively optimize the strike-slip fracture elastic parameter model based on this lateral relative distance.

[0034] Preferably, the elastic parameter model of the strike-slip fracture is iteratively optimized based on the lateral relative distance. Specifically, the method is as follows:

[0035] If the morphology and location of the structural feature fracture corresponding to the processed seismic response correspond one-to-one with the actual seismic structural feature fracture, then the strike-slip fracture elastic parameter model is output; otherwise, the lateral distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture is used as the fine-tuning offset of the strike-slip fracture elastic parameter model for model correction.

[0036] The modified strike-slip fracture elastic parameter model is subjected to forward modeling of the wave equation, and the process is iterated until the lateral distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture meets the requirements.

[0037] A three-dimensional detection system for strike-slip fractures, the system being capable of operating the aforementioned detection method, comprising:

[0038] The data acquisition unit is used to acquire data on the characteristic features of the strike-slip fracture structure.

[0039] Mesh model building unit, used to obtain a three-dimensional mesh model of strike-slip fracture;

[0040] Porosity module construction unit, used to obtain three-dimensional porosity model of strike-slip fracture;

[0041] Elastic parameter model building unit, used to construct elastic parameter model of strike-slip fracture;

[0042] The model optimization unit is used to iteratively optimize the obtained strike-slip fracture elastic parameter model to obtain the optimized strike-slip fracture elastic parameter model.

[0043] The detection unit is used to detect the strike-slip fracture under test according to the optimized strike-slip fracture elastic parameter model.

[0044] A three-dimensional detection device for strike-slip fracture includes a processor and a memory storing a computer program that can run on the processor, wherein the processor implements the method when executing the computer program.

[0045] A computing device, comprising:

[0046] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This invention provides a three-dimensional strike-slip fracture detection method. The established strike-slip fracture elastic parameter model meets the requirements of three-dimensional wave equation numerical simulation and does not require equivalent scale. It expands the strike-slip fracture modeling method, breaks through the conventional two-dimensional profile and depth slice model, and provides a practical and effective method for establishing an elastic parameter model of Ordovician carbonate strike-slip fracture. At the same time, this inversion method uses residual iterative correction of the forward modeling results to match the forward modeling results with actual earthquakes, so that the authenticity of the strike-slip fracture detection results has a clear quantitative reference, ensuring the certainty and accuracy of the detection results.

[0049] Furthermore, by establishing strike-slip fracture structural feature volumes using structural gradient tensors and tensor voting techniques, strike-slip fracture identification becomes more continuous, spatial identification of strike-slip fractures becomes more accurate, and ambiguity is reduced.

[0050] Furthermore, the method of vector line integral interpolation provides a more convenient means to construct a three-dimensional model of strike-slip fracture.

[0051] Furthermore, the strike-slip fault model is fine-tuned by comparing the forward modeled seismic response with the actual observed seismic response, forming an iteratively updated model that ultimately achieves the purpose of strike-slip fault detection. Attached Figure Description

[0052] Figure 1 This is a flowchart of a three-dimensional detection method for strike-slip fracture based on iterative modeling;

[0053] Figure 2 This invention provides structural feature data generated based on constructed gradient tensors and tensor voting in embodiments of the invention.

[0054] Figure 3 A rendering of a three-dimensional mesh model of strike-slip fractures in Ordovician carbonate rocks provided in an embodiment of the present invention;

[0055] Figure 4 A rendering of the three-dimensional porosity model of strike-slip fracture in Ordovician carbonate rocks provided in an embodiment of the present invention;

[0056] Figure 5 The image shows the forward modeling seismic response of the Ordovician carbonate strike-slip fault model provided in this embodiment of the invention.

[0057] Figure 6 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0058] The present application will now be described in detail with reference to the accompanying drawings, but this is not intended to limit the scope of the invention.

[0059] This invention provides a three-dimensional detection method for strike-slip fractures, which constructs a three-dimensional geophysical numerical model of strike-slip fractures in Ordovician carbonate rocks. This is an innovation in strike-slip fracture modeling methods, providing a three-dimensional implementation approach for strike-slip fracture modeling. It breaks through the conventional two-dimensional profile and depth slice models, reduces the difficulty of three-dimensional strike-slip fracture modeling, and improves the comprehensiveness and accuracy of subsequent work.

[0060] Specifically, the method includes the following steps:

[0061] Step 1: Obtain strike-slip fracture structure feature data based on gradient structure tensor and tensor voting;

[0062] In this embodiment of the invention, the strike-slip fault structure feature data includes multiple grid data in the same grid format as the seismic data. The specific method for obtaining the strike-slip fault structure feature data is as follows:

[0063] First, based on post-stack seismic data, the gradient structure tensor T is calculated with a set window size, and the gradient structure tensor matrix is ​​decomposed. Where v1, v2, and v3 are eigenvectors, and λ1, λ2, and λ3 are the corresponding non-negative eigenvalues, with λ1 ≥ λ2 ≥ λ3 ≥ 0. λ1 is the amplitude gradient intensity of the signal gradient direction; λ2 and λ3 are the gradient intensities in the plane perpendicular to the v1 vector. When a point has a stable response without fracture on the same phase axis during an earthquake, its gradient tensor eigenvalues ​​are λ1 >> 0 and λ2 ≒ 0. When the point is on a strike-slip fault, λ1 >> 0 and λ2 >> 0. λ2 is more sensitive to the expression of strike-slip faults. Therefore, the second eigenvalue, λ2, is chosen as the basis for describing strike-slip faults.

[0064] Secondly, the obtained second eigenvalue of the gradient structure tensor is compared with a preset threshold to obtain the processed second eigenvalue of the gradient structure tensor.

[0065] The second eigenvalue of each processed gradient structure tensor, i.e., the non-zero grid measurement point, undergoes tensor voting processing using a preset voting domain, ultimately yielding strike-slip fracture structure feature data. This data is primarily used to assist in identifying strike-slip fractures, and the structural feature volume effect is visible. Figure 2 .

[0066] Step 2: Manually mark the fault lines, and then integrate and interpolate to obtain a three-dimensional mesh model of the strike-slip fracture.

[0067] In this embodiment of the invention, the strike-slip fault structure feature data obtained in step 1 is an important reference for artificially marking fault lines. The artificially marked fault lines are marked on seismic profiles and slices, respectively, and strike-slip fault vector lines are marked. In this embodiment, a profile vector line is marked once for every 10 profiles, and a plane vector line is marked once for every 10 depth slices.

[0068] All marked vector lines are discretized using a seismic grid, meaning that each vector line segment is assigned a point by the grid it passes through.

[0069] By performing integral interpolation between each pair of discretized fracture lines, an initial 3D mesh of the strike-slip fracture can be obtained. The final 3D mesh model of the strike-slip fracture is obtained by Gaussian smoothing the initial 3D mesh to eliminate abrupt changes, and the effect is visible. Figure 3 .

[0070] Step 3: Based on well logging data and structural feature data, fit a spatial variation function to obtain a strike-slip fracture porosity model.

[0071] Using porosity from well logging data as hard data and strike-slip fracture structure features as constraint data, the well logging data and strike-slip fracture structure feature data are meshed according to the mesh required for modeling.

[0072] In this embodiment of the invention, the three-dimensional mesh model of the strike-slip fault obtained in step 2 is used to obtain the three-dimensional structure of the strike-slip fault. In order to obtain the final three-dimensional geophysical numerical model of the Ordovician carbonate strike-slip fault, we need to calculate the three-dimensional porosity model of the strike-slip fault.

[0073] To establish a porosity model, we need to use porosity logging curves from actual well logging data as a basis:

[0074] First, the porosity logging curve is coarsened according to the grid points traversed by the well trajectory, and a correspondence is formed with the strike-slip fracture structural feature data of the corresponding grid points in the strike-slip fracture three-dimensional grid model.

[0075] Secondly, the corresponding data set, namely the strike-slip fracture structure feature data and the coarsened porosity logging curve, is linearly fitted. In this embodiment, an S-shaped curve function is used for fitting. Different functions can be applied under different conditions. Nonlinear fitting and artificial intelligence methods can also be used for training. The function obtained by fitting is the spatial variation function of the model porosity.

[0076] Finally, by substituting the strike-slip fracture feature volume data corresponding to all grid points of the strike-slip fracture 3D mesh model into the spatial variation function, the 3D porosity model of the strike-slip fracture can be calculated, and the results are shown in the figure. Figure 4 .

[0077] Step 4: Calculate the elastic parameter model of the strike-slip fracture using the rock physics model and the three-dimensional porosity model of the strike-slip fracture obtained in Step 3. This elastic parameter model is a three-dimensional geophysical numerical model of the strike-slip fracture of Ordovician carbonate rocks.

[0078] Based on the obtained three-dimensional porosity model of the strike-slip fracture, and combined with the rock physical characteristics of the strike-slip fracture measured in the laboratory by drilling core samples, a corresponding rock physical model is established, and the elastic parameter model of the strike-slip fracture is calculated. The elastic parameters include longitudinal wave velocity, transverse wave velocity and density.

[0079] In this embodiment of the invention, based on the matrix composition of Ordovician carbonate rocks in the actual work area, the elastic parameters of the non-porous bedrock are calculated using the Wyllie average formula. The pore structure of strike-slip fractures is generally considered to be similar to cracks. In space, each pore can be regarded as "coin-shaped" and distributed in a near-vertical state at a high angle within the strike-slip fracture medium. Therefore, the Hudson model and the three-dimensional porosity model of strike-slip fractures are used as inputs to calculate the equivalent elastic parameters of the dry rock skeleton. Finally, the elastic parameter model of carbonate rock strike-slip fractures is calculated using the BK anisotropic fluid substitution formula.

[0080] Step 5. Based on the actual seismic exploration and observation system of the work area, obtain the seismic forward model response of the strike-slip fault model, and fine-tune and iterate the elastic parameter model of the strike-slip fault obtained in Step 4 according to the spatial offset of the seismic response space of the strike-slip fault.

[0081] Specifically:

[0082] First, the strike-slip fracture elastic parameter model obtained in step 4 is subjected to wave equation forward modeling using an actual seismic exploration and observation system. Then, the seismic response after simulation processing of the strike-slip fracture elastic parameter model is obtained using the reverse time migration imaging method. The results are shown in […]. Figure 5 ;

[0083] Secondly, obtain the structural feature body corresponding to the processed seismic response;

[0084] Next, the lateral relative distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture in the post-stack seismic data in step 1 is obtained. If the morphology and position of the structural feature fracture corresponding to the processed seismic response correspond one-to-one with those of the actual seismic structural feature fracture, the strike-slip fracture elastic parameter model is output; otherwise, proceed to the next step.

[0085] Next, the lateral distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture is used as the fine-tuning offset of the strike-slip fracture elastic parameter model for model correction. Forward modeling is performed again to form an iteration until the lateral relative distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture meets the requirements. At this point, the strike-slip fracture elastic parameter model is the optimal three-dimensional strike-slip fracture detection model.

[0086] like Figure 6 As shown, a three-dimensional detection system for strike-slip fractures is provided. This system is capable of operating the aforementioned detection method, including:

[0087] The data acquisition unit is used to acquire data on the characteristic features of the strike-slip fracture structure.

[0088] Mesh model building unit, used to obtain a three-dimensional mesh model of strike-slip fracture;

[0089] Porosity module construction unit, used to obtain three-dimensional porosity model of strike-slip fracture;

[0090] Elastic parameter model building unit, used to construct elastic parameter model of strike-slip fracture;

[0091] The model optimization unit is used to iteratively optimize the obtained strike-slip fracture elastic parameter model to obtain the optimized strike-slip fracture elastic parameter model.

[0092] The detection unit is used to detect the strike-slip fracture under test according to the optimized strike-slip fracture elastic parameter model.

[0093] A three-dimensional detection device for strike-slip fracture includes a processor and a memory storing a computer program that can run on the processor, wherein the processor implements the method when executing the computer program.

[0094] A computing device, comprising:

[0095] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described.

[0096] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used to execute the aforementioned three-dimensional detection method for strike-slip fracture.

[0097] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0098] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the verification method for the long-term maintenance plan of the power grid in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by a processor to implement the above-described three-dimensional detection method for strike-slip fracture.

[0099] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0100] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0101] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0102] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

Claims

1. A three-dimensional detection method for strike-slip fracture, characterized in that, Includes the following steps: Step 1: Obtain the feature data of the strike-slip fracture structure; Step 2: Obtain the three-dimensional mesh model of the strike-slip fracture based on the strike-slip fracture structural feature data obtained in Step 1; Step 3: Based on the obtained strike-slip fracture structural feature data and strike-slip fracture three-dimensional mesh model, obtain the strike-slip fracture three-dimensional porosity model; Step 4: Construct a strike-slip fracture elastic parameter model based on the obtained three-dimensional porosity model of strike-slip fracture; Step 5: Iteratively optimize the obtained strike-slip fracture elastic parameter model to obtain the optimized strike-slip fracture elastic parameter model. Step 6: Detect the strike-slip fracture under test according to the optimized strike-slip fracture elastic parameter model; In step 1, the characteristic body data of the strike-slip fracture structure is obtained. The specific method is as follows: First, the second eigenvalue of the gradient structure tensor is calculated based on post-stack seismic data; Secondly, the obtained second eigenvalue of the gradient structure tensor is compared with a preset threshold to obtain the processed second eigenvalue of the gradient structure tensor. Finally, the second eigenvalue of each processed gradient structure tensor is subjected to tensor voting processing using a preset voting domain to obtain the strike-slip fracture structure feature data. In step 2, the three-dimensional mesh model of the strike-slip fracture is obtained. The specific method is as follows: Mark the strike-slip fracture vector lines on the cross-sections and slices of the strike-slip fracture structural feature data obtained in step 1, respectively. All the obtained strike-slip fault vector lines are discretized using a seismic grid to obtain the discretized strike-slip fault vector lines. Integral interpolation is performed between every two fracture lines in the obtained discretized strike-slip fracture vector lines to obtain the initial three-dimensional strike-slip fracture mesh. The initial strike-slip fracture 3D mesh is Gaussian smoothed to obtain the strike-slip fracture 3D mesh model. In step 3, the three-dimensional porosity model of the strike-slip fracture is obtained. The specific method is as follows: First, obtain the actual porosity logging curves; Secondly, the obtained actual porosity logging curves are coarsened to obtain coarsened porosity logging curves. The obtained coarsened porosity logging curves correspond to the strike-slip fracture structure feature data. Next, the coarsened porosity logging curves and strike-slip fracture structure feature data are linearly fitted to obtain the spatial variation function of the model porosity. Finally, the strike-slip fracture feature data corresponding to all grid points in the three-dimensional mesh model of the strike-slip fracture are substituted into the spatial variation function of the porosity of the model to obtain the three-dimensional porosity model of the strike-slip fracture.

2. The three-dimensional detection method for strike-slip fracture according to claim 1, characterized in that, In step 4, the elastic parameter model for strike-slip fracture is constructed. The specific method is as follows: Based on the three-dimensional porosity model of the strike-slip fracture obtained in step 3, an elastic parameter model of the strike-slip fracture is constructed by combining it with the rock physics model.

3. The three-dimensional detection method for strike-slip fracture according to claim 1, characterized in that, In step 5, the obtained strike-slip fracture elastic parameter model is iteratively optimized. The specific method is as follows: The elastic parameter model of the strike-slip fracture obtained in step 4 is used to perform wave equation forward modeling with an actual seismic exploration and observation system, and the seismic response after model simulation is calculated. Obtain the structural feature body corresponding to the processed seismic response; Obtain the lateral relative distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture in the post-stack seismic data in step 1; and iteratively optimize the strike-slip fracture elastic parameter model based on this lateral relative distance.

4. The three-dimensional detection method for strike-slip fracture according to claim 3, characterized in that, The elastic parameter model of strike-slip fracture is iteratively optimized based on this lateral relative distance. The specific method is as follows: If the morphology and location of the structural feature fracture corresponding to the processed seismic response correspond one-to-one with the actual seismic structural feature fracture, then the strike-slip fracture elastic parameter model is output; otherwise, the lateral distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture is used as the fine-tuning offset of the strike-slip fracture elastic parameter model for model correction. The modified strike-slip fracture elastic parameter model is subjected to forward modeling of the wave equation, and the process is iterated until the lateral distance between the structural feature fracture corresponding to the processed seismic response and the actual seismic structural feature fracture meets the requirements.

5. A three-dimensional detection system for strike-slip fracture, characterized in that, Based on the detection method according to claim 1, the detection system includes: The data acquisition unit is used to acquire data on the characteristic features of the strike-slip fracture structure. Mesh model building unit, used to obtain a three-dimensional mesh model of strike-slip fracture; Porosity module construction unit, used to obtain three-dimensional porosity model of strike-slip fracture; Elastic parameter model building unit, used to construct elastic parameter model of strike-slip fracture; The model optimization unit is used to iteratively optimize the obtained strike-slip fracture elastic parameter model to obtain the optimized strike-slip fracture elastic parameter model. The detection unit is used to detect the strike-slip fracture under test according to the optimized strike-slip fracture elastic parameter model.

6. A three-dimensional detection device for strike-slip fracture, characterized in that, It includes a processor and a memory storing a computer program that can run on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1-4.

7. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods according to claims 1 to 4.

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