A method and system for quantitatively characterizing the direction of ground stress based on fine structure morphology

Abstract

The present application relates to a kind of based on fine configuration mode ground stress direction quantitative characterization method and system, it includes: poststack three-dimensional seismic data is handled optimally, according to the seismic data of optimization processing Hou, based on well seismic calibration result, obtain fine structure surface of purpose layer;According to the fine structure surface of purpose layer, obtain the structure strain of purpose layer, and strain classification;According to the structure strain of classified purpose layer, extract and optimize the azimuth of structure strain of purpose layer, to carry out quantitative characterization to the stress direction of purpose layer stratum.This application can make full use of three-dimensional seismic data to carry out stratum stress direction characterization, effectively reduce the uncertainty of structure strain stress orientation, while improving the prediction efficiency of structure strain stress orientation.The present application can be widely applied in offshore oilfield development geology research field.

Description

Technical Field

[0001] This invention relates to the field of geological research technology for offshore oilfield development, and in particular to a method and system for quantitative characterization of geostress direction based on fine structural morphology. Background Technology

[0002] As oil and gas field exploration and development deepens, low-permeability oil and gas reservoirs have become an important area in China's oil and gas exploration and development. Research on low-permeability oil and gas reservoirs mainly focuses on two aspects: geological "sweet spots" and engineering "sweet spots." Among these, the study of geostress is a key aspect of the engineering "sweet spot," including the magnitude and direction of stress. This directly relates to the deployment of horizontal fracturing wells and is crucial for reservoir stimulation to improve single-well productivity and achieve economical and effective development of low-permeability oil and gas fields.

[0003] Current research on geostress primarily employs pre-stack inversion to obtain parameters such as subsurface rock density, P-wave velocity, and S-wave velocity. Then, based on kinematic and mechanical theories, elastic parameters of the rock layers, such as Young's modulus, Poisson's ratio, shear modulus, and bulk modulus, are calculated. Furthermore, dynamic and static modulus conversion efficiently couples indoor mechanical test data, well logging data, and seismic data to ultimately achieve a three-dimensional characterization of various mechanical parameters of the target reservoir. However, these methods are often labor-intensive and time-consuming. In addition, existing technologies mainly rely on inversion data and actual well logging data for related analyses, and the richness of the basic data also affects the stress analysis results to some extent. However, for offshore low-permeability oil and gas fields, which face sparse well networks, large burial depths, and a lack or absence of actual well logging data, existing geostress analysis techniques are insufficient to meet the accuracy requirements for geostress prediction in offshore low-permeability fracturing development. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to provide a method and system for quantitative characterization of geostress direction based on fine structural morphology, which can fully utilize three-dimensional seismic data to characterize the stress direction of strata.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for quantitative characterization of geostress direction based on fine structural morphology, comprising: optimizing post-stack three-dimensional seismic data; obtaining the fine structural surface of the target layer based on the well-seismic calibration results according to the optimized seismic data; obtaining the structural strain of the target layer based on the fine structural surface of the target layer and classifying the strain; extracting and optimizing the azimuth angle of the structural strain of the target layer based on the classified structural strain of the target layer, so as to quantitatively characterize the stress direction of the target layer.

[0006] Furthermore, the post-stack 3D seismic data is optimized, including:

[0007] Seismic data is decomposed into seismic data of different frequency bands to identify the effective low frequency, dominant frequency, effective high frequency and high frequency noise bands of seismic data.

[0008] The three-dimensional seismic data, which are decomposed into seismic data of different frequency bands, are subjected to noise reduction processing to obtain the initial optimized seismic data.

[0009] The initial optimized seismic data is subjected to frequency topology optimization to obtain the final optimized seismic data.

[0010] Furthermore, the seismic data decomposition adopts the spectral decomposition method, the noise reduction process adopts the median filtering method, and the frequency topology optimization adopts the spectral equalization method.

[0011] Furthermore, based on the well-seismic calibration results, the fine structural surface of the target layer is obtained, including:

[0012] Based on the acquired optimized seismic data, fine well-seismic calibration was performed using well logging and 3D seismic data to clarify the seismic reflection characteristics of the target layer;

[0013] Based on well-seismic calibration results and profile interaction, high-density fine structural interpretation of the target layer is carried out using a pre-set size grid to obtain three-dimensional grid data of the structural surface.

[0014] The structural surfaces of the target layer are obtained by interpreting the high-density, fine-grained structure of the target layer using three-dimensional mesh data.

[0015] Furthermore, the structural strain of the target layer is determined based on the fine structural surface of the target layer, and the strain is classified, including:

[0016] Based on the obtained high-precision structural surface, the maximum curvature attribute of the structural surface is extracted to obtain the structural strain data of the target layer;

[0017] Thresholds were set based on the maximum curvature attribute to extract the positive strain and negative strain attribute data of the construction, respectively.

[0018] Furthermore, the strain azimuth angle of the target layer is extracted and optimized, including:

[0019] Based on the obtained positive and negative tectonic strain attribute data, the azimuth attributes of positive and negative tectonic strain are extracted respectively, and their respective strain azimuth data are obtained.

[0020] Linear processing is performed on the two types of azimuth information to obtain the initial strain azimuth data of the structural strain.

[0021] Based on the initial strain azimuth data, a threshold is set, and characteristic curves representing the strain azimuth information of the two types of structures are extracted to obtain the initial structural strain azimuth data.

[0022] Based on the initial tectonic strain azimuth data, the initial tectonic strain azimuth was optimized in combination with the regional tectonic stress characteristics to eliminate abnormal data and obtain the final two types of tectonic strain azimuth data.

[0023] Furthermore, the stress direction of the target layer is quantitatively characterized, including:

[0024] Based on the acquired structural strain orientation data, the two types of structural strain orientation data are overlaid and displayed.

[0025] Based on the obtained superimposed data of the two types of tectonic strain orientations, the tectonic strain orientation data of different parts of the target layer were statistically analyzed;

[0026] Based on the azimuth statistics, a structural strain azimuth rose diagram of the target layer is created to obtain the final geostress azimuth data of the target layer.

[0027] A quantitative characterization system for geostress direction based on fine structural morphology includes: an optimization module for optimizing post-stack 3D seismic data and obtaining the fine structural surface of the target layer based on well-seismic calibration results; a structural classification module for obtaining the structural strain of the target layer based on the fine structural surface and classifying the strain; and a quantitative characterization module for extracting and optimizing the azimuth angle of the structural strain of the target layer based on the classified structural strain, so as to quantitatively characterize the stress direction of the target layer.

[0028] A computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0029] A computing device includes: 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, and the one or more programs include instructions for performing any of the methods described above.

[0030] The present invention has the following advantages due to the adoption of the above technical solutions:

[0031] 1. This invention utilizes high-precision 3D seismic data, combining well and seismic analysis, to trace and interpret the top surface of the target layer structure. By determining the maximum curvature attribute of the structural surface, it obtains the curvature characteristics of different types of structural surfaces. Based on this, through curvature surface azimuth calculation, linear characterization and optimization of azimuth information, and structural stress azimuth rose diagram, a comprehensive analysis is conducted to quantitatively characterize the structural strain azimuth of the target layer. This guides the deployment of horizontal fracturing wells and provides important technical support for the efficient development and scheme adjustment of low-permeability underground oil and gas fields.

[0032] 2. This invention adopts a well-seismic combined analysis method, which makes full use of rich three-dimensional seismic information to constrain the quantitative characterization of tectonic strain and stress orientation. It makes up for the shortcomings of the method of analyzing tectonic strain and stress orientation based mainly on well and pre-stack inversion information under the condition of sparse well network at sea, effectively reducing the uncertainty of tectonic strain and stress orientation, and improving the prediction efficiency of tectonic strain and stress orientation. Attached Figure Description

[0033] Figure 1 This is a flowchart of the quantitative characterization method of geostress direction based on fine structural morphology in an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of the seismic spectrum characteristics before and after optimization of three-dimensional seismic data in an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of the positive strain and negative strain planes of the target layer in an embodiment of the present invention;

[0036] Figure 4 This is a schematic diagram of the orientation plane of the target layer under positive strain and negative strain in an embodiment of the present invention;

[0037] Figure 5 This is a schematic diagram of the strain azimuth rose of the target layer structure in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0039] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0040] To address the limitation of existing geostress analysis techniques in meeting the accuracy requirements for geostress prediction in offshore low-permeability fracturing development, this invention establishes a quantitative geostress direction characterization method and system based on fine structural morphology, fully utilizing 3D seismic data and drilled well data. The method includes the following steps: post-stack 3D seismic data optimization processing; obtaining the fine structural surface of the target layer based on well-seismic calibration results using the optimized seismic data; determining the structural strain of the target layer based on the fine structural surface and classifying the strain; extracting and optimizing the azimuth angle of the structural strain of the target layer based on the structural strain; and quantitatively characterizing the stress direction of the target layer based on the azimuth angle of the structural strain. This invention employs a well-seismic combined analysis method, fully utilizing abundant 3D seismic information to constrain the quantitative characterization of structural strain and stress azimuth. It overcomes the shortcomings of methods relying primarily on well and pre-stack inversion information for structural strain and stress azimuth analysis under sparse well networks at sea, effectively reducing the uncertainty of structural strain and stress azimuth, while improving the prediction efficiency. This guides the deployment and optimization of horizontal development well networks, providing crucial technical support for the efficient development and scheme adjustment of underground low-permeability oil and gas reservoirs.

[0041] In one embodiment of the present invention, a method for quantitatively characterizing geostress direction based on fine structural morphology is provided. In this embodiment, as... Figure 1 As shown, the method includes the following steps:

[0042] 1) Optimize the post-stack 3D seismic data, and obtain the fine structural surface of the target layer based on the well-seismic calibration results of the optimized seismic data;

[0043] 2) Calculate the structural strain of the target layer based on the fine structural surface of the target layer, and classify the strain;

[0044] 3) Based on the structural strain of the target layer after classification, extract and optimize the azimuth angle of the structural strain of the target layer in order to quantitatively characterize the stress direction of the target layer.

[0045] In step 1) above, the post-stack 3D seismic data is optimized, including the following steps:

[0046] 1.1.1) Based on 3D seismic data, the seismic data is decomposed into seismic data of different frequency bands, and the effective low frequency, dominant frequency, effective high frequency and high frequency noise frequency bands of the seismic data are identified;

[0047] In this embodiment, the seismic data decomposition adopts the spectral decomposition method;

[0048] 1.1.2) The 3D seismic data, which is decomposed into seismic data of different frequency bands, is subjected to noise reduction processing to obtain the initial optimized seismic data;

[0049] In this embodiment, the noise reduction process uses the median filtering method;

[0050] 1.1.3) Perform frequency extension optimization on the initial optimized seismic data to obtain the final optimized seismic data;

[0051] In this embodiment, the frequency extension optimization adopts the spectral equalization method.

[0052] Specifically, such as Figure 2 As shown, before 3D seismic processing, the effective low-frequency band was 5-10Hz, the effective high-frequency band was 60-80Hz, the dominant frequency was 25Hz, and frequencies above 80Hz were dominated by high-frequency noise. After optimization, the seismic bandwidth remained basically unchanged from before processing, and the dominant seismic frequency was improved to 35Hz.

[0053] In step 1) above, the fine structural surface of the target layer is obtained based on the well-seismic calibration results, including the following steps:

[0054] 1.2.1) Based on the acquired optimized seismic data, fine well-seismic calibration is performed using well logging and 3D seismic data to clarify the seismic reflection characteristics of the target layer;

[0055] 1.2.2) Based on the well-seismic calibration results and profile interaction, a high-density fine structural interpretation of the target layer with a preset size grid is performed to obtain three-dimensional grid data of the structural surface;

[0056] In this embodiment, the preferred preset grid size is: 5*5 grid;

[0057] 1.2.3) Obtain the structural surface of the target layer from the high-density fine structure interpretation three-dimensional mesh data.

[0058] In step 2) above, the structural strain of the target layer is determined based on the fine structural surface of the target layer, and the strain is classified, including the following steps:

[0059] 2.1) Extract the maximum curvature attribute of the structural surface based on the obtained high-precision structural surface, and obtain the structural strain data of the target layer;

[0060] 2.2) Set thresholds based on the maximum curvature attribute to extract the constructive positive strain and constructive negative strain attribute data respectively.

[0061] Specifically, such as Figure 3 As shown, positive and negative strains alternate, with tectonic strain being stronger in the central and western regions.

[0062] In step 3) above, extracting and optimizing the structural strain azimuth angle of the target layer includes the following steps:

[0063] 3.1.1) Based on the obtained positive and negative tectonic strain attribute data, extract the positive and negative tectonic strain azimuth attributes respectively, and obtain their respective strain azimuth data;

[0064] 3.1.2) Linear processing is performed on the two types of azimuth information to obtain the initial strain azimuth data of the constructed strain;

[0065] 3.1.3) Based on the initial strain azimuth data, a threshold is set, and characteristic curves representing the strain azimuth information of the two types of structures are extracted to obtain the initial structural strain azimuth data.

[0066] 3.1.4) Based on the initial tectonic strain azimuth data, the initial tectonic strain azimuth is optimized in combination with the regional tectonic stress characteristics to eliminate abnormal data and obtain the final two types of tectonic strain azimuth data.

[0067] Specifically, such as Figure 4 As shown, the directional trends of the positive and negative structural strains in the target layer are generally consistent, ranging from northwest to southeast to near east-west.

[0068] In step 3) above, the stress direction of the target layer is quantitatively characterized, including the following steps:

[0069] 3.2.1) Based on the obtained structural strain orientation data, the two types of structural strain orientation data are overlaid and displayed;

[0070] 3.2.2) Based on the obtained superimposed data of the two types of structural strain orientations, the structural strain orientation data of different parts of the target layer are statistically analyzed;

[0071] 3.2.3) Based on the azimuth statistics, a structural strain azimuth rose diagram of the target layer is prepared to obtain the final geostress azimuth data of the target layer.

[0072] Specifically, such as Figure 5 As shown, the predicted azimuth angle range of the structural strain of the target layer is N120°~135°E, which is consistent with the trend of the maximum stress direction (N120°E) measured in the well core.

[0073] In one embodiment of the present invention, a quantitative characterization system for geostress direction based on fine structural morphology is provided, comprising:

[0074] The optimization module optimizes the post-stack 3D seismic data and, based on the optimized seismic data, obtains the fine structural surface of the target layer according to the well-seismic calibration results.

[0075] The construction classification module calculates the structural strain of the target layer based on the fine structural surface of the target layer and classifies the strain.

[0076] The quantitative characterization module extracts and optimizes the azimuth angle of the structural strain of the target layer based on the classified structural strain, so as to quantitatively characterize the stress direction of the target layer.

[0077] In the above embodiments, the post-stack 3D seismic data is optimized, including:

[0078] Seismic data is decomposed into seismic data of different frequency bands to identify the effective low frequency, dominant frequency, effective high frequency and high frequency noise bands of seismic data.

[0079] The three-dimensional seismic data, which are decomposed into seismic data of different frequency bands, are subjected to noise reduction processing to obtain the initial optimized seismic data.

[0080] The initial optimized seismic data is subjected to frequency topology optimization to obtain the final optimized seismic data.

[0081] In the above embodiments, the seismic data decomposition adopts the spectral decomposition method, the noise reduction processing adopts the median filtering method, and the frequency extension optimization adopts the spectral equalization method.

[0082] In the above embodiments, obtaining the fine structural surface of the target layer based on the well-seismic calibration results includes:

[0083] Based on the acquired optimized seismic data, fine well-seismic calibration was performed using well logging and 3D seismic data to clarify the seismic reflection characteristics of the target layer;

[0084] Based on well-seismic calibration results and profile interaction, high-density fine structural interpretation of the target layer is carried out using a pre-set size grid to obtain three-dimensional grid data of the structural surface.

[0085] The structural surfaces of the target layer are obtained by interpreting the high-density, fine-grained structure of the target layer using three-dimensional mesh data.

[0086] In the above embodiments, the structural strain of the target layer is obtained based on the fine structural surface of the target layer, and the strain is classified, including:

[0087] Based on the obtained high-precision structural surface, the maximum curvature attribute of the structural surface is extracted to obtain the structural strain data of the target layer;

[0088] Thresholds were set based on the maximum curvature attribute to extract the positive strain and negative strain attribute data of the construction, respectively.

[0089] In the above embodiments, extracting and optimizing the strain azimuth angle of the target layer includes:

[0090] Based on the obtained positive and negative tectonic strain attribute data, the azimuth attributes of positive and negative tectonic strain are extracted respectively, and their respective strain azimuth data are obtained.

[0091] Linear processing is performed on the two types of azimuth information to obtain the initial strain azimuth data of the structural strain.

[0092] Based on the initial strain azimuth data, a threshold is set, and characteristic curves representing the strain azimuth information of the two types of structures are extracted to obtain the initial structural strain azimuth data.

[0093] Based on the initial tectonic strain azimuth data, the initial tectonic strain azimuth was optimized in combination with the regional tectonic stress characteristics to eliminate abnormal data and obtain the final two types of tectonic strain azimuth data.

[0094] In the above embodiments, the quantitative characterization of the stress direction of the target formation includes:

[0095] Based on the acquired structural strain orientation data, the two types of structural strain orientation data are overlaid and displayed.

[0096] Based on the obtained superimposed data of the two types of tectonic strain orientations, the tectonic strain orientation data of different parts of the target layer were statistically analyzed;

[0097] Based on the azimuth statistics, a structural strain azimuth rose diagram of the target layer is created to obtain the final geostress azimuth data of the target layer.

[0098] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0099] A schematic diagram of a computing device structure is provided in one embodiment of the present invention. This computing device can be a terminal, and may include: a processor, a communication interface, memory, a display screen, and an input device. The processor, communication interface, and memory communicate with each other via a communication bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. When executed by the processor, the computer program implements a method for quantitative characterization of geostress direction based on fine structural morphology. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, a management network, NFC (Near Field Communication), or other technologies. The display screen can be a liquid crystal display or an e-ink display. The input device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad mounted on the casing of the computing device, or an external keyboard, touchpad, or mouse. The processor can call logical instructions stored in the memory.

[0100] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0102] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0103] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] 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.

[0106] 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.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantitative characterization of geostress direction based on fine structural morphology, characterized in that, include: The post-stack 3D seismic data is optimized, and the fine structural surface of the target layer is obtained based on the well-seismic calibration results according to the optimized seismic data. The structural strain of the target layer is obtained based on the fine structural surface of the target layer, and the strain is classified, including: extracting the maximum curvature attribute of the structural surface based on the obtained high-precision structural surface to obtain the structural strain data of the target layer; setting thresholds based on the maximum curvature attribute to extract the structural positive strain and structural negative strain attribute data respectively. Based on the structural strain of the target layer after classification, the azimuth angle of the structural strain of the target layer is extracted and optimized in order to quantitatively characterize the stress direction of the target layer. Extracting and optimizing the structural strain azimuth angle of the target layer, including: Based on the obtained positive and negative tectonic strain attribute data, the azimuth attributes of positive and negative tectonic strain are extracted respectively, and their respective strain azimuth data are obtained. Linear processing is performed on the two types of azimuth information to obtain the initial strain azimuth data of the structural strain. Based on the initial strain azimuth data, a threshold is set, and characteristic curves representing the strain azimuth information of the two types of structures are extracted to obtain the initial structural strain azimuth data. Based on the initial tectonic strain azimuth data, the initial tectonic strain azimuth was optimized in combination with the regional tectonic stress characteristics to eliminate abnormal data and obtain the final two types of tectonic strain azimuth data.

2. The quantitative characterization method for geostress direction based on fine structural morphology as described in claim 1, characterized in that, The post-stack 3D seismic data is optimized, including: Seismic data is decomposed into seismic data of different frequency bands to identify the effective low frequency, dominant frequency, effective high frequency and high frequency noise bands of seismic data. The three-dimensional seismic data, which are decomposed into seismic data of different frequency bands, are subjected to noise reduction processing to obtain the initial optimized seismic data. The initial optimized seismic data is subjected to frequency topology optimization to obtain the final optimized seismic data.

3. The quantitative characterization method for geostress direction based on fine structural morphology as described in claim 2, characterized in that, Seismic data decomposition employs spectral decomposition, noise reduction uses median filtering, and frequency topology optimization uses spectral equalization.

4. The quantitative characterization method for geostress direction based on fine structural morphology as described in claim 1, characterized in that, The fine structural surface of the target layer is obtained based on the well seismic calibration results, including: Based on the acquired optimized seismic data, fine well-seismic calibration was performed using well logging and 3D seismic data to clarify the seismic reflection characteristics of the target layer; Based on well-seismic calibration results and profile interaction, high-density fine structural interpretation of the target layer is carried out using a pre-set size grid to obtain three-dimensional grid data of the structural surface. The structural surfaces of the target layer are obtained by interpreting the high-density, fine-grained structure of the target layer using three-dimensional mesh data.

5. The method for quantitative characterization of geostress direction based on fine structural morphology as described in claim 1, characterized in that, Quantitative characterization of the stress direction in the target formation, including: Based on the acquired structural strain orientation data, the two types of structural strain orientation data are overlaid and displayed. Based on the obtained superimposed data of the two types of tectonic strain orientations, the tectonic strain orientation data of different parts of the target layer were statistically analyzed; Based on the azimuth statistics, a structural strain azimuth rose diagram of the target layer is created to obtain the final geostress azimuth data of the target layer.

6. A quantitative characterization system for geostress direction based on fine tectonic morphology, used to implement the quantitative characterization method for geostress direction based on fine tectonic morphology as described in any one of claims 1 to 5, characterized in that, include: The optimization module optimizes the post-stack 3D seismic data and, based on the optimized seismic data, obtains the fine structural surface of the target layer according to the well-seismic calibration results. The construction classification module calculates the structural strain of the target layer based on the fine structural surface of the target layer and classifies the strain. The quantitative characterization module extracts and optimizes the azimuth angle of the structural strain of the target layer based on the classified structural strain, so as to quantitatively characterize the stress direction of the target layer.

7. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 5.

8. 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 described in claims 1 to 5.

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