A crack zone plane feature detection method, device, equipment and medium

Abstract

The present application provides a fracture zone plane feature detection method, device, equipment and medium, including: obtaining three-dimensional seismic data corresponding to a plurality of spatial positions in the fracture zone; determining the dip angle and azimuth angle of the geological texture in the target spatial position according to the three-dimensional seismic data, and the coordinates of the spatial position corresponding to the geological texture, determining the analysis time window corresponding to the geological texture; calculating the surface coefficient of the geological texture according to the analysis time window; the surface coefficient is introduced into the fracture enhanced curvature function to obtain the curvature value of the geological texture; according to the curvature value of the geological texture, the projection of the spatial position corresponding to the geological texture and the ground parallel direction is calculated; all spatial positions are projected and reorganized to obtain a fracture zone image. Through the above method, the description effect of the geological texture characteristics is enhanced, the fracture enhanced curvature function can comprehensively represent the appearance of the geological texture in each analysis time window, and a fracture zone image which can comprehensively represent the fracture zone plane feature is obtained.

Description

Technical Field

[0001] This invention relates to the field of petroleum exploration technology and can be applied to the field of geological curvature of oil and gas reservoirs, particularly a method, device, equipment, and medium for detecting planar features of fracture zones. Background Technology

[0002] According to statistics, more than half of the world's oil and gas production is distributed in fractured reservoirs. However, due to the low porosity, heterogeneity and anisotropy of carbonate reservoirs, the difficulty of oil and gas exploration is greatly increased. Fractures are the main channels for oil and gas accumulation and migration in such reservoirs. Therefore, accurately predicting fractures in such reservoirs is equivalent to finding oil and gas directly in such reservoirs.

[0003] Currently, the method of detecting cracks in fracture zones through images requires the manual design of the analysis window size and the vertical and horizontal scanning of geological textures in 3D seismic data based on the analysis window. Due to the limitations of the analysis window size and scanning direction, it is often only possible to select the surface coefficients of the geological texture at the center of the analysis window to characterize the features of all geological textures included in the analysis window. This results in incomplete data scanning, and the actual geological appearance does not match the features reflected by the image. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, this paper aims to provide a method, apparatus, device, and medium for detecting planar features of fracture zones, thereby resolving the issues of incomplete data scanning and discrepancies between the actual geological features and the features reflected in the images in existing technologies.

[0005] To solve the above-mentioned technical problems, the specific technical solution presented in this paper is as follows:

[0006] On the one hand, this paper provides a method for detecting planar features of crack zones, including:

[0007] Acquire three-dimensional seismic data corresponding to multiple spatial locations within the fracture zone;

[0008] The dip angle and azimuth angle of the geological texture at the target spatial location are determined based on the three-dimensional seismic data.

[0009] The analysis window for the geological texture is determined based on the dip angle and azimuth angle of the geological texture, as well as the coordinates of the target spatial location corresponding to the geological texture.

[0010] The surface coefficients of the geological texture are calculated based on the analysis window.

[0011] The surface coefficients of the geological texture are substituted into the fracture enhancement curvature function to obtain the curvature value of the geological texture;

[0012] Based on the curvature value of the geological texture, calculate the projection of the geological texture and the ground parallel to the target spatial location.

[0013] By using the above method, the projections of geological textures corresponding to all spatial locations in the fracture zone are obtained, and the projections are reconstructed to obtain the fracture zone image.

[0014] As one embodiment of this document, the crack enhancement curvature function is obtained based on a first curvature function and a second curvature function, and further includes:

[0015] The root mean square error of the first curvature function and the second curvature function is calculated to obtain the crack enhancement curvature function;

[0016] The first curvature function characterizes the anticline features of the geological texture;

[0017] The second curvature function characterizes the synclinal features of the geological texture.

[0018] As an embodiment of this article, determining the analysis window of the geological texture based on its dip angle and azimuth angle, and the coordinates of the target spatial location corresponding to the geological texture, further includes:

[0019] Obtain the dip angle and azimuth angle of the geological texture at the target spatial location;

[0020] The analysis time window for the geological texture is constructed according to the formula g(t,p,q)=g(t-px-qy);

[0021] Wherein, p and q on the right side of the equation are the dip angle and azimuth angle of the geological texture at the target spatial location, respectively; x and y on the right side of the equation are the coordinates of the target spatial location, respectively; and g(t,p,q) represents the analysis window function, wherein the analysis window function is used to construct the analysis window.

[0022] As an embodiment of this article, the calculation of the surface coefficients of the geological texture based on the analysis time window further includes:

[0023] According to the formula d = p(x,y,t) and e = q(x,y,t), calculate several surface coefficients a, b, c, d and e of the geological texture;

[0024] Wherein, a is the tilt direction difference coefficient, b is the azimuth direction difference coefficient, c is the difference mean coefficient, d is the tilt angle of the data point, and e is the azimuth angle of the data point;

[0025] P is the tilt angle of the analysis window, and q is the orientation angle of the analysis window.

[0026] As an embodiment of this article, the step of calculating the root mean square error of the first curvature function and the second curvature function to obtain the crack enhancement curvature function further includes:

[0027] The first curvature function is K1 = [a(1+e 2 )+b(1+d 2 )-cde] / (1+d 2 +e 2 ) 12 ;

[0028] The second curvature function is K2 = (4ab - c) 2 ) / (1+d 2 +e 2 ) 2 ;

[0029] The mean square error of the first curvature function and the second curvature function is calculated to obtain... Wherein, C is the crack enhancement curvature function, a is the dip angle difference coefficient, b is the azimuth angle difference coefficient, c is the difference mean coefficient, d is the dip angle of the geological texture, and e is the azimuth angle of the geological texture.

[0030] As an embodiment of this article, determining the dip angle and azimuth angle of the geological texture at the target spatial location based on the three-dimensional seismic data includes:

[0031] From the three-dimensional seismic data, obtain three gradient vector volumes that correspond one-to-one with the three directions of the geological texture at the target spatial location;

[0032] Perform a dextral operation on the three gradient vector volumes to obtain the gradient structure tensor matrix of the geological texture;

[0033] The gradient structure tensor matrix is ​​subjected to spectral decomposition to obtain three feature vectors corresponding to the geological texture, and three feature values ​​corresponding to the three feature vectors.

[0034] Select the eigenvector corresponding to the largest eigenvalue among the three eigenvalues;

[0035] Based on the selected feature vectors, the dip angle and azimuth angle of the geological texture are determined.

[0036] As an embodiment of this paper, before obtaining the three gradient vector volumes that correspond one-to-one with the three directions of the geological texture at the target spatial location from the three-dimensional seismic data, the process includes:

[0037] The three-dimensional seismic data is filtered using a three-dimensional Gaussian smoothing filter.

[0038] On the other hand, this paper also provides a crack zone planar feature detection device, including:

[0039] The data acquisition unit is used to acquire three-dimensional seismic data corresponding to several spatial locations within the fracture zone.

[0040] The parameter acquisition unit is used to determine the dip angle and azimuth angle of the geological texture at the target spatial location based on the three-dimensional seismic data.

[0041] The time window creation unit is used to determine the analysis time window of the geological texture based on the dip angle and azimuth angle of the geological texture and the coordinates of the target spatial position corresponding to the geological texture.

[0042] A surface coefficient calculation unit is used to calculate the surface coefficient of the geological texture based on the analysis time window;

[0043] The curvature value calculation unit is used to input the surface coefficient of the geological texture into the fracture enhancement curvature function to obtain the curvature value of the geological texture, wherein the fracture enhancement curvature function is obtained based on the first curvature function and the second curvature function;

[0044] The projection calculation unit is used to calculate the projection of the geological texture in the direction parallel to the ground corresponding to the target spatial location, based on the curvature value of the geological texture.

[0045] The image reconstruction unit is used to obtain the projection of the geological texture corresponding to all spatial locations in the fracture zone through the above unit, and to perform projection reconstruction to obtain the fracture zone image.

[0046] On the other hand, this document also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the crack zone planar feature detection methods.

[0047] On the other hand, this document also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any one of the crack zone planar feature detection methods.

[0048] By employing the above technical solution, a variable analysis window is established using the dip angle and azimuth angle of the geological texture corresponding to each spatial location in the fracture zone. Compared with the existing technology that uses a single vector for representation, this analysis window adds dip angle and azimuth angle, for a total of three vectors, which greatly enhances the descriptive effect of geological texture characteristics. Furthermore, the surface coefficient of the geological texture is calculated using the relevant parameters of the analysis window, and the appearance of the geological texture within each analysis window can be more comprehensively represented by the fracture enhancement curvature function. By performing the same processing and projection on all spatial locations in the fracture zone, a fracture zone image that represents the planar features of the fracture zone can be obtained more comprehensively than that of the existing technology.

[0049] To make the above and other objects, features and advantages of this document more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments or prior art described herein, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this article. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This paper presents an overall system diagram of a crack zone planar feature detection method according to an embodiment of the invention.

[0052] Figure 2 This paper illustrates a step-by-step diagram of a crack zone planar feature detection method according to an embodiment of the invention.

[0053] Figure 3 This diagram illustrates the steps for determining the dip angle and azimuth angle in the crack zone planar feature detection method of this embodiment.

[0054] Figure 4 A schematic diagram of the analysis time window for a crack zone planar feature detection method according to an embodiment of this paper is shown;

[0055] Figure 5 A schematic diagram of a crack zone planar feature detection device according to an embodiment of this article is shown;

[0056] Figure 6 This paper shows a schematic diagram of the parameter acquisition unit of a crack zone planar feature detection device according to an embodiment of the present paper;

[0057] Figure 7 This paper shows a schematic diagram of the curvature value calculation unit of a crack zone planar feature detection device according to an embodiment of the present paper;

[0058] Figure 8This paper illustrates a time window creation unit of a crack zone planar feature detection device according to an embodiment of the present invention.

[0059] Figure 9 This paper presents a data flow diagram of an overall system for detecting planar features of crack zones according to an embodiment of the present invention.

[0060] Figure 10 A schematic diagram of a computer device as described in this article is shown.

[0061] Explanation of symbols in the attached drawings:

[0062] 101. Seismic wave generator;

[0063] 102. Detector;

[0064] 103. Database;

[0065] 104. Computing terminal;

[0066] 501. Data Acquisition Unit;

[0067] 502. Parameter Acquisition Unit;

[0068] 5021, Gradient Acquisition Module;

[0069] 5022, Duplex Operation Module;

[0070] 5023, Spectral Decomposition Module;

[0071] 5024. Feature value selection module;

[0072] 5025, Tilt and Azimuth Acquisition Module;

[0073] 503. Time Window Creation Unit;

[0074] 5031. Tilt and Azimuth Acquisition Module;

[0075] 5032. Analyze the module for determining the time window function;

[0076] 504. Surface coefficient calculation unit;

[0077] 505. Curvature value calculation unit;

[0078] 5051, First curvature function module;

[0079] 5052, Second Curvature Function Module;

[0080] 5053, Root Mean Square Error Calculation Module;

[0081] 506. Projection Calculation Unit;

[0082] 507. Image reconstruction unit;

[0083] 1002. Computer equipment;

[0084] 1004, Processor;

[0085] 1006. Memory;

[0086] 1008. Drive mechanism;

[0087] 1010. Input / Output Module;

[0088] 1012. Input devices;

[0089] 1014. Output devices;

[0090] 1016. Presentation device;

[0091] 1018. Graphical User Interface;

[0092] 1020. Network interface;

[0093] 1022. Communication link;

[0094] 1024. Communication bus. Detailed Implementation

[0095] The technical solutions in the embodiments described below will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments described herein, and not all of the embodiments. Based on the embodiments described herein, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this document.

[0096] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings herein are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0097] Curvature properties are a new method for structural interpretation and reservoir analysis using the degree of curvature of strata. In recent years, they have received widespread attention because they are better than coherent properties at characterizing discontinuities such as various complex faults, fractures, channels and structural bends.

[0098] Before calculating curvature properties, three-dimensional seismic data must be acquired, and an analysis window is slid across the data. Whenever the analysis window is positioned at a certain location within the three-dimensional seismic data, the features of the center location of the geological texture corresponding to all spatial locations contained within the current analysis window need to be solved. For example, if a spatial location containing 3×3×3 units is identified within the analysis window, in existing technologies, the central (2,2,2) location within the analysis window needs to be solved, and the features of this location represent the features of all spatial locations within the analysis window. Furthermore, since three-dimensional seismic data is relatively large compared to the analysis window, the analysis window is scanned sequentially along the x, y, or z directions, with time as the unit. After scanning all corresponding spatial locations in the three-dimensional seismic data, the images are projected to obtain the fracture zone image corresponding to the three-dimensional seismic data.

[0099] In current industrial production, when construction workers obtain an image of a fracture zone that can characterize the planar distribution of fracture zones, they can drill wells based on this image, placing the wellbore at the fracture site, and thus have a high probability of obtaining the desired oil and gas reservoir.

[0100] like Figure 1 The diagram shows an overall system diagram for crack zone planar feature detection, which includes a seismic wave generator 101, a detector 102, a database 103, and a computing terminal 104.

[0101] The seismic wave generator 101 is used to transmit seismic waves. During seismic exploration of the target stratum, multiple intersecting survey lines are laid out on the surface corresponding to the target stratum. Multiple geophones 102 are arranged at equal intervals along each survey line. Each geophone 102 receives the reflected wave signal from the seismic wave generator 101. For any geophone 102, the seismic wave artificially generated by the seismic wave generator 101 is received. Since the seismic wave is reflected when it encounters different rock strata interfaces as it propagates underground, the geophone 102 receives the reflected wave signal and determines multiple seismic data points corresponding to the location of that geophone 102 based on the received signal. That is, multiple seismic data points can be determined at each geophone 102, and the multiple seismic data points corresponding to each geophone 102 form a three-dimensional seismic data volume corresponding to the target stratum. Furthermore, the waveform displayed in the geophone 102 for the multiple seismic data points detected by each geophone 102 is called a seismic trace.

[0102] The method for determining multiple seismic data corresponding to the location of the geophone 102 based on the received signal can be as follows: Assuming the coordinates of the location of the geophone 102 are (x, y), the geophone 102 determines the amplitude of the received signal at preset intervals. After determining an amplitude each time, the transmission time t of the seismic wave in the target stratum is determined based on the time of excitation of the seismic wave and the current time. The transmission distance t×v of the seismic wave can be determined based on the transmission time and the propagation speed of the seismic wave, where v represents the propagation speed of the seismic wave. At this time, the reflected wave corresponding to the currently determined amplitude can be considered as the reflected wave of the seismic wave at the spatial location (x, y, t×v / 2). Therefore, in this embodiment of the invention, the seismic data corresponding to each spatial location can be recorded as u(x, y, t).

[0103] Database 103 is used to store seismic data for all spatial locations within each target stratum. It also stores all target strata in a 3D seismic data structure. It should be noted that there may be multiple target strata. For example, when geological exploration personnel need to explore multiple strata simultaneously, they can send the seismic data of the target strata to the database wirelessly or via wired connection. Each seismic data file will have an identifier corresponding to the target stratum, such as 0x01, 0x02, etc. Database 103 uses these file identifiers to perform cluster analysis on the seismic data, thereby obtaining all seismic data for the target stratum.

[0104] The computing terminal 104 can be a tablet computer, a desktop computer, or a laptop computer, depending on its usage method. It can also be a Unix terminal, a Windows terminal, a Linux terminal, a Web terminal, or a Java terminal, depending on its operating system. This article does not limit the type of computing terminal. When the computing terminal 104 receives a user's computing instruction, it performs the calculation according to the target stratum corresponding to the computing instruction, and can obtain the fracture zone image of the fracture zone corresponding to the target stratum.

[0105] It should be noted that in the crack zone image, cracks are represented by gray lines. The darker the gray color, the more obvious the crack and the wider the line; while the lighter the gray color, the less obvious the crack, or even the non-existent crack.

[0106] In existing technologies, because the size of the analysis window is fixed when scanning 3D seismic data through the analysis window, and the characteristics of all data within the analysis window are represented by the data characteristics of the spatial position of the center of the analysis window, the data scanning is inaccurate and incomplete, resulting in a problem where the actual geological features do not match the features reflected by the image.

[0107] To address the aforementioned issues, this paper provides a fracture zone planar feature detection method, which can improve the planar features displayed in the fracture zone image of the target formation corresponding to the fracture zone. Figure 2 This is a schematic diagram illustrating the steps of a crack zone planar feature detection method provided in this embodiment. This specification provides the operational steps of the method described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel. Specifically, as shown in the attached drawings... Figure 2 As shown, the method may include:

[0108] Step 201: Obtain three-dimensional seismic data corresponding to several spatial locations within the fracture zone.

[0109] Step 202: Determine the dip angle and azimuth angle of the geological texture at the target spatial location based on the three-dimensional seismic data.

[0110] Step 203: Determine the analysis window of the geological texture based on the dip angle and azimuth angle of the geological texture and the coordinates of the target spatial position corresponding to the geological texture.

[0111] Step 204: Calculate the surface coefficients of the geological texture based on the analysis time window.

[0112] Step 205: Substitute the surface coefficients of the geological texture into the fracture enhancement curvature function to obtain the curvature value of the geological texture.

[0113] Step 206: Based on the curvature value of the geological texture, calculate the projection of the geological texture corresponding to the target spatial location in the direction parallel to the ground.

[0114] Step 207: Using the above method, obtain the projection of the geological texture corresponding to all spatial locations in the fracture zone, and perform projection reconstruction to obtain the fracture zone image.

[0115] It should be noted that the methods and apparatus disclosed herein can be used in the field of shale oil and gas exploration, as well as in any oil and gas exploration field other than shale oil and gas exploration. The application field of the fracture zone planar feature detection method and apparatus disclosed herein is not limited.

[0116] It should be noted that the target spatial location is a point in the 3D seismic data, which can be a randomly specified target or a specific endpoint in the 3D seismic data.

[0117] In this embodiment, steps 201-206 illustrate the geological texture at a certain spatial location. Step 207 iterates through steps 201-206 at all spatial locations in the fracture zone to obtain the desired fracture zone image. However, it should be noted that the projection acquisition can be performed in parallel or serially at all spatial locations. This article does not limit this.

[0118] By employing the above technical solution, a variable analysis window is established using the dip angle and azimuth angle of the geological texture corresponding to each spatial location in the fracture zone. Compared with the existing technology that uses a single vector for representation, this analysis window adds dip angle and azimuth angle, for a total of three vectors, which greatly enhances the descriptive effect of geological texture characteristics. Furthermore, the surface coefficient of the geological texture is calculated using the relevant parameters of the analysis window, and the appearance of the geological texture within each analysis window can be more comprehensively represented by the fracture enhancement curvature function. By performing the same processing and projection on all spatial locations in the fracture zone, a more comprehensive and accurate fracture zone image depicting the geological appearance can be obtained compared with the existing technology.

[0119] like Figure 3 The diagram illustrates the steps for determining the dip and azimuth angles in the fracture zone planar feature detection method. As an embodiment of this paper, step 202, determining the dip and azimuth angles of the geological texture in the spatial location based on the three-dimensional seismic data, specifically includes:

[0120] Step 301: Obtain three gradient vector volumes from the three-dimensional seismic data that correspond one-to-one with the three directions of the geological texture at the target spatial location.

[0121] It should be noted that in 3D seismic data, there are multiple spatial locations, each represented by a unified coordinate system, such as u(x,y,t).

[0122] Before step 301, the seismic data can also be filtered using a three-dimensional Gaussian smoothing filter.

[0123] Specifically, for example, assuming the seismic data at a certain spatial location is u(x,y,t), a three-dimensional Gaussian smoothing filter G(x,y,t,σ) is used. g The geological texture data corresponding to a certain spatial location in the 3D seismic data is preprocessed by smoothing and filtering to obtain u′(x,y,t). The specific processing procedure is as follows:

[0124] u′(x,y,t)=u(x,y,t)*G(x,y,t,σ g )

[0125] in,

[0126] In the above formula, u′(x,y,t) represents the seismic data at the spatial location after processing with a three-dimensional Gaussian smoothing filter, and G(x,y,t,σ) represents the seismic data at the spatial location. g ) represents a three-dimensional Gaussian smoothing filter, where σ g The noise scale parameter can characterize the magnitude of the waveform of seismic data in a three-dimensional Gaussian filter, and can generally be taken as 2 or 3.

[0127] In this step, the three gradient vectors are the first gradient vector, the second gradient vector, and the third gradient vector. The first gradient vector is used to describe the rate of change of the geological texture at the corresponding spatial location along the first coordinate direction. The second gradient vector is used to describe the rate of change of the geological texture at the corresponding spatial location along the second coordinate direction. The third gradient vector is used to describe the rate of change of the geological texture at the corresponding spatial location along the third coordinate direction. The first and second coordinate directions are two directions parallel to the Earth's surface and are perpendicular to each other. The third coordinate direction is a direction perpendicular to the Earth's surface.

[0128] In this paper, the first coordinate direction can be the x-direction, the second coordinate direction can be the y-direction, and the third coordinate direction can be the z-direction.

[0129] There are two ways to determine the three gradient vectors:

[0130] The first method determines the first, second, and third gradient vectors at a given spatial location using the central difference method. The equation is as follows:

[0131]

[0132]

[0133]

[0134] In the above formula, g x (x,y,t) is the gradient vector in the x-direction, i.e., the first gradient vector, representing the rate of change of the geological texture along the x-direction at the corresponding spatial location, g. y (x,y,t) is the gradient vector in the y-direction, i.e., the second gradient vector, representing the rate of change of the geological texture along the y-direction at the corresponding spatial location, g t (x, y, t) is the gradient vector in the z-direction, also known as the third gradient vector, representing the rate of change of geological texture along the z-direction at a spatial location. Here, Δx, Δy, and Δt are the sampling intervals in the x, y, and z directions, respectively. The sampling interval refers to the spatial distance between any two spatial locations.

[0135] The second method involves determining the first, second, and third gradient vectors at a given spatial location using the finite difference method. This is illustrated in the following equation:

[0136]

[0137]

[0138]

[0139] The Δx, Δy, and Δt in the above formula are the same as those in the first method, and will not be explained further here.

[0140] Since the first gradient vector, the second gradient vector, and the third gradient vector at a spatial location are used to characterize the rate of change of the geological texture at a spatial location in the x, y, and z directions, respectively, the first gradient vector, the second gradient vector, and the third gradient vector at a spatial location can be collectively referred to as the three gradient vector volumes at a certain spatial location.

[0141] Step 302: Perform a dextral operation on the three gradient vector volumes to obtain the gradient structure tensor matrix of the geological texture.

[0142] Performing a dextral operation on the first, second, and third gradient vectors at a certain spatial location yields the gradient structure tensor matrix corresponding to that spatial location. Specifically, assuming the seismic data for the spatial location is u′(x,y,t), the first gradient vector g of the spatial location can be obtained according to step 301. x (x,y,t), the second gradient vector g y (x,y,t) and the third gradient vector g t (x, y, t). Performing a conjugate operation on the three gradient vectors of the spatial location yields a matrix, which can then be used as the gradient structure tensor matrix corresponding to the spatial location, as shown in the following equation:

[0143]

[0144] In the above formula, Let g represent the gradient structure tensor matrix corresponding to a certain spatial location. x The first gradient vector g representing the spatial location x (x,y,t),g y The second gradient vector g represents a certain spatial location. y (x,y,t),g t The third gradient vector g representing spatial location t (x,y,t).

[0145] As a more preferred approach, when performing a dextral operation on the three gradient vectors of the seismic data at a spatial location, a three-dimensional Gaussian smoothing filter can be applied to the gradient structure tensor matrix constructed from the three gradient vectors, as shown in the following equation:

[0146]

[0147] In the above formula, Let g represent the gradient structure tensor matrix corresponding to a certain spatial location. x The first gradient vector g representing the spatial location x (x,y,t),g y The second gradient vector g represents a certain spatial location. y (x,y,t),g t The third gradient vector g representing spatial location t (x,y,t).

[0148] In the above formula, σ ρ The structural scale parameter, σ, characterizes the features of stratigraphic boundaries as shown by seismic data in a 3D Gaussian filter. ρ The value σ typically ranges from 0.1 to 3. g With construction scale parameter σ ρ The corresponding relationship is 3σ g ≤σ ρ ≤10σ g .

[0149] After smoothing the gradient structure tensor matrix corresponding to a certain spatial location using a three-dimensional Gaussian smoothing filter, it has the effect of structure-guided filtering.

[0150] Step 303: Perform spectral decomposition on the gradient structure tensor matrix to obtain three eigenvectors corresponding to the geological texture and three eigenvalues ​​corresponding to the three eigenvectors.

[0151] The following method can be used to determine the three eigenvectors of the gradient structure tensor matrix corresponding to a certain spatial location and the three eigenvalues ​​that correspond one-to-one with the three eigenvectors:

[0152]

[0153] Where λ represents the eigenvalue of the gradient structure tensor matrix corresponding to a certain spatial location, and I is the third-order identity matrix, i.e. Through this calculation, the three eigenvalues ​​λ of the gradient structure tensor matrix corresponding to a certain spatial location can be obtained. i Then, by utilizing the one-to-one correspondence between eigenvalues ​​and eigenvectors, we obtain the relationship between the three eigenvalues ​​λ. iThe three eigenvectors v correspond one-to-one i .

[0154] Of course, there are other ways to determine the eigenvectors and eigenvalues ​​of the gradient structure tensor matrix corresponding to a certain spatial location, which are not limited here.

[0155] Since the gradient structure tensor matrix corresponding to a certain spatial location represents the discontinuity structure of the strata at that spatial location, and a certain eigenvalue of any matrix is ​​used to characterize the projection of the matrix onto the direction represented by the eigenvector corresponding to that eigenvalue, in this embodiment of the invention, the dip angle and azimuth angle of the geological texture at a certain spatial location can be determined by the largest eigenvector of the gradient structure tensor matrix. Optionally, the dip angle and azimuth angle of the geological texture at a certain spatial location can also be determined by other eigenvectors of the gradient structure tensor matrix besides the largest eigenvector. This invention does not limit this.

[0156] Step 304: Select the eigenvector corresponding to the largest eigenvalue among the three eigenvalues.

[0157] Step 305: Determine the dip angle and azimuth angle of the geological texture based on the selected feature vector.

[0158] Specifically, assuming the largest eigenvalue of the gradient structure tensor matrix corresponding to a certain spatial location is λ1, and the eigenvector corresponding to λ1 is v1(x,y,t), decomposing v1(x,y,t) in the x, y, and z directions respectively, we can obtain three elements v 1x (x,y,t),v 1y (x,y,t) and v 1t (x, y, t). Using these three elements, the tilt and azimuth of a given spatial location can be obtained. Specifically, it can be represented by the following formula:

[0159]

[0160]

[0161] In the above formula, p(x,y,t) is the dip angle of the geological texture at a certain spatial location, and q(x,y,t) is the azimuth angle of the geological texture at a certain spatial location.

[0162] Alternatively, the dip angle and azimuth angle of geological texture at a certain spatial location can be determined in other ways. For example, assuming the largest eigenvalue of the gradient structure tensor matrix corresponding to a certain spatial location is λ1, and the eigenvector corresponding to λ1 is v1(x,y,t), decomposing v1(x,y,t) in the x, y, and z directions respectively, we can obtain three elements v 1x (x,y,t),v1y (x,y,t) and v 1t (x, y, t). Using these three elements, the tilt and azimuth of a given spatial location can be obtained. Specifically, it can be represented by the following formula:

[0163]

[0164]

[0165] Of course, there are other ways to determine the dip angle and azimuth angle of geological texture at a certain spatial location, which are not limited here.

[0166] As an embodiment of this paper, step 203, based on the dip angle and azimuth angle of the geological texture and the coordinates of the spatial location corresponding to the geological texture, determines an analysis window that corresponds one-to-one with the geological texture, further including:

[0167] Obtain the dip angle and azimuth angle of the geological texture.

[0168] Based on the formula g(t,p,q)=g(t-px-qy), an analysis window for this geological texture is constructed.

[0169] In this equation, p and q on the right-hand side are the dip angle and azimuth angle of the geological texture in the spatial location, respectively. t, x, and y on the right-hand side are the coordinates of the spatial location corresponding to the geological texture, respectively. g(t,p,q) on the right-hand side represents the analysis window function, which is used to construct the analysis window.

[0170] In this embodiment, the analysis window of the geological texture corresponding to each location can be understood as a hexahedron, the shape of which is determined according to the dip angle and azimuth angle of the geological texture.

[0171] In the formula g(t,p,q)=g(t-px-qy), the right side of the equation represents the dip angle p of the current geological texture, the azimuth angle q of the current geological texture, and the coordinates t, x, and y of the spatial position enclosing the current geological texture. These coordinates should be understood as the gradient vector g along the gradient direction of the selected largest eigenvalue. x (x,y,t), g y (x,y,t) or g t (x,y,t) Multiply the vectors on the right side of the equation and assign the result to the left side of the equation to obtain the representation function g(t,p,q) of the analysis window for the shape of the hexahedron. In subsequent calculations, this representation function g(t,p,q) is used as the calculation variable.

[0172] Through this representation function, it can be clearly seen that the analysis window is variable. That is, the spatial location coordinates, the dip angle and azimuth angle of the geological texture jointly determine the size and shape of the analysis window. Compared with the fixed-size and fixed-shape analysis windows in the prior art, the variable-size analysis window has a faster recognition efficiency. It can be imagined that when the geological texture changes are relatively gentle, the size of the analysis window will increase, while when the geological texture changes are more complex, the size of the analysis window can be reduced to improve the resolution and obtain more detailed recognition parameters.

[0173] To make the variable process of the analysis window clearer, this article also provides, for example... Figure 4 The diagram shows an analysis window for a method of detecting planar features of crack zones. Figure 4 In the diagram, abcd represents the plane formed by the x and y directions. Figure 4 It can be clearly seen that the shape of each analysis window is different. Because geological textures are three-dimensional and difficult to describe, the degree of tortuosity of the geological texture is used to simplify the description of its relationship with the analysis windows. Figure 4 The leftmost geological texture is the most tortuous, while the rightmost geological texture is almost straight. Therefore, the analysis window corresponding to the leftmost geological texture is the smallest, and the gradient of each surface is the largest. The dip angle and azimuth angle of the geological texture are also the largest. Using a small time window can more finely represent the characteristics of the geological texture. The rightmost geological texture is almost straight, so an analysis time window that is almost rectangular is used to identify the characteristics of the geological texture.

[0174] It should be noted that, in order to better represent the variable characteristics of the analysis window, this... Figure 4 The analysis windows are separated, but in reality, each analysis window is interconnected and together they constitute a complete three-dimensional seismic data.

[0175] exist Figure 4 In this system, each geological texture passes through a data point, which represents the coordinates of that geological texture.

[0176] In this embodiment, step 204, which involves calculating the surface coefficients of the geological texture based on the analysis window, further includes:

[0177] According to the formula d = p(x,y,t) and e = q(x,y,t) are used to calculate several surface coefficients a, b, c, d and e of the geological texture.

[0178] Wherein, a is the dip angle direction difference coefficient, b is the azimuth direction difference coefficient, c is the difference mean coefficient, d is the dip angle of the geological texture, and e is the azimuth angle of the geological texture.

[0179] P is the tilt angle of the analysis window, and q is the orientation angle of the analysis window.

[0180] In this step, since the analysis time window and all features characterizing the geological texture have been determined, it is necessary to quantitatively characterize the features of the geological texture using parameters. In this paper, surface coefficients are used to represent these features. After obtaining the analysis time window, the surface coefficients are calculated based on p and q in the function g(t,p,q) of the analysis time window.

[0181] In this embodiment, the step of substituting the surface coefficients of the geological texture into the fracture enhancement curvature function to obtain the curvature value of the geological texture, wherein the fracture enhancement curvature function is obtained based on the first curvature function and the second curvature function, and further includes:

[0182] By substituting the surface coefficient into the crack enhancement curvature function, the curvature value corresponding to the geological texture can be obtained. The curvature function displays the shape or curvature of the slope, and by viewing the curvature value, it can be determined whether a certain part of the surface is convex or concave. The spatial morphology of the crack can be obtained through the curvature value.

[0183] In this paper, the root mean square error of the first curvature function and the second curvature function is calculated to obtain the crack enhancement curvature function;

[0184] The first curvature function characterizes the anticline features of the geological texture.

[0185] The second curvature function characterizes the synclinal features of the geological texture.

[0186] An anticline refers to a landform where the rock strata bend and bulge upwards. A syncline is the opposite of an anticline, referring to a fissure where the two wings point upwards and the center bends downwards.

[0187] In existing technologies, anticline features are typically used to represent cracks, which makes the crack representation insufficient. This paper takes into account both anticlines and synclines, making the crack description more comprehensive and enabling the acquisition of crack details that were previously unavailable in crack detection processes.

[0188] Wherein, the first curvature function is K1=[a(1+e 2 )+b(1+d 2 )-cde] / (1+d 2 +e 2 ) 12 .

[0189] The second curvature function is K2 = (4ab - c) 2 ) / (1+d 2 +e 2 ) 2 .

[0190] The mean square error of the first curvature function and the second curvature function is calculated to obtain... Wherein, C is the crack enhancement curvature function, a is the dip angle difference coefficient, b is the azimuth angle difference coefficient, c is the difference mean coefficient, d is the dip angle of the data point, and e is the azimuth angle of the data point.

[0191] It should be noted that when the parameter ad of the constructed variable analysis time window is substituted into the first curvature function, the parameter K1(x,y,t) can be obtained. When the parameter ad of the constructed variable analysis time window is substituted into the second curvature function, the parameter K2(x,y,t) can be obtained. After taking the mean square error of the coordinates corresponding to K1(x,y,t) and K2(x,y,t), the curvature value of the geological texture corresponding to the analysis time window can be obtained.

[0192] The same processing is applied to all spatial locations in the 3D seismic data. The spatial distribution characteristics of all geological textures in the 3D seismic data can be obtained by using the curvature values ​​of the geological textures corresponding to all spatial locations.

[0193] The spatial distribution features are projected in a direction parallel to the ground to obtain a planar image, which is the crack zone image. This crack zone image represents all the cracks in the space.

[0194] The above method enables the acquisition of noise-free 3D seismic data. It allows the extraction of feature values ​​and feature vectors of geological textures corresponding to all spatial locations. Furthermore, a variable analysis window is used to obtain the surface coefficients of the geological textures. These surface coefficients are then imported into a fracture enhancement curvature function to obtain syncline features in addition to anticline features. This method enhances the representation of fractures, resulting in a more comprehensive and contrasting fracture zone image compared to existing technologies. Based on this fracture zone image, construction personnel can identify more drilling locations, thus better facilitating the storage of oil and gas in fracture zones.

[0195] like Figure 5 A schematic diagram of a crack zone planar feature detection device is shown, comprising:

[0196] The data acquisition unit 501 is used to acquire three-dimensional seismic data corresponding to several spatial locations in the fracture zone.

[0197] The parameter acquisition unit 502 is used to determine the dip angle and azimuth angle of the geological texture at the target spatial location based on the three-dimensional seismic data.

[0198] The time window creation unit 503 is used to determine the analysis time window of the geological texture based on the tilt angle and azimuth angle of the geological texture and the coordinates of the target spatial position corresponding to the geological texture.

[0199] The surface coefficient calculation unit 504 is used to calculate the surface coefficient of the geological texture within the analysis window.

[0200] The curvature value calculation unit 505 is used to input the surface coefficient of the geological texture into the crack enhancement curvature function to obtain the curvature value of the geological texture.

[0201] The projection calculation unit 506 is used to calculate the projection of the geological texture in the direction parallel to the ground corresponding to the target spatial location based on the curvature value of the geological texture.

[0202] Image reconstruction unit 507 is used to obtain the projection of geological textures corresponding to all spatial locations in the fracture zone through the above unit, and to perform projection reconstruction to obtain a fracture zone image.

[0203] This device can quickly acquire the dip and azimuth angles of geological textures at various spatial locations within a fracture zone. It can also establish variable analysis windows based on these dip and azimuth angles, significantly enhancing the description of geological texture characteristics. Furthermore, it can calculate the surface coefficients of the geological texture using the relevant parameters of the analysis window, and comprehensively characterize the appearance of the geological texture within each analysis window through the fracture enhancement curvature function. By applying the same processing and projection to all spatial locations within the fracture zone, a fracture zone image with a significantly enhanced geological appearance can be obtained compared to existing technologies.

[0204] like Figure 6 The diagram shows a parameter acquisition unit of a crack zone planar feature detection device. As an embodiment of this paper, the parameter acquisition unit 502 includes:

[0205] The gradient acquisition module 5021 is used to acquire three gradient vector volumes from the three-dimensional seismic data that correspond one-to-one with the three directions of the geological texture at the target spatial location.

[0206] The dyadic operation module 5022 is used to perform dyadic operations on the three gradient vector volumes to obtain the gradient structure tensor matrix of the geological texture.

[0207] The spectral decomposition module 5023 is used to perform spectral decomposition on the gradient structure tensor matrix to obtain three feature vectors corresponding to the geological texture and three feature values ​​corresponding to the three feature vectors.

[0208] The eigenvalue selection module 5024 is used to select the eigenvector corresponding to the largest eigenvalue among the three eigenvalues.

[0209] The tilt and azimuth acquisition module 5025 is used to determine the tilt and azimuth of the geological texture based on the selected feature vector.

[0210] like Figure 7 The diagram shows a curvature value calculation unit of a crack zone planar feature detection device. As an embodiment of this paper, the curvature value calculation unit 505 includes:

[0211] The first curvature function module 5051 is used to store the first curvature function to characterize the anticline features of the geological texture.

[0212] The second curvature function module 5052 is used to store the second curvature function to characterize the synclinal features of the geological texture.

[0213] The mean square error calculation module 5053 is used to calculate the mean square error of the first curvature function and the second curvature function to obtain the crack enhancement curvature function, so as to comprehensively characterize the synclinal and anticline features of the geological texture.

[0214] like Figure 8 The diagram shows a time window creation unit for a crack zone planar feature detection device. As an embodiment of this paper, the time window creation unit 503 includes:

[0215] The tilt and azimuth acquisition module 5031 is used to acquire the tilt and azimuth of the geological texture.

[0216] The analysis window function determination module 5032 is used to construct the analysis window of the geological texture according to the formula g(t,p,q)=g(t-px-qy); where p and q on the right side of the equation are the dip angle and azimuth angle of the geological texture in the spatial location, respectively, x and y on the right side of the equation are the coordinates of the spatial location corresponding to the geological texture, and g(t,p,q) represents the analysis window function, wherein the analysis window function is used to construct the analysis window.

[0217] Using the aforementioned device, analysis windows for each spatial location in 3D seismic data can be quickly established. Each analysis window is related to the dip angle and azimuth angle of the geological texture at the current spatial location. After constructing the analysis window, surface coefficients can be calculated based on the newly assigned coordinates of the current analysis window. The surface coefficients can be imported into the fracture enhancement curvature function to obtain the curvature value of the geological texture. The projection of each spatial location can be calculated based on the geological texture. By using the aforementioned device to calculate the 3D seismic data, fracture zone images can be obtained, which can reveal the planar distribution characteristics of the fracture zones.

[0218] like Figure 9 The diagram shown is a data flow chart of an overall system for detecting planar features of crack zones, including:

[0219] Step 901: Seismic wave generator 101 sends seismic waves to the target stratum.

[0220] Step 902: Detector 102 receives the reflected seismic waves and acquires multiple seismic data.

[0221] Step 903: Obtain seismic data from database 103 and construct three-dimensional seismic data.

[0222] Step 904: The computing terminal 104 calculates the dip angle and azimuth angle of the geological texture at each spatial location based on the three-dimensional seismic data.

[0223] Step 905: The computing terminal 104 designs the analysis window for geological texture at each spatial location based on the tilt angle and azimuth angle.

[0224] Step 906: The computing terminal 104 uses the analysis window to calculate the surface coefficients of the geological texture at all spatial locations.

[0225] Step 907: The computing terminal 104 calls the crack enhancement curvature function in the database 103 to calculate the curvature value of each geological texture.

[0226] Step 908: The computing terminal 104 uses the curvature value to project onto a plane to obtain an image of the crack zone.

[0227] like Figure 10 As shown in this embodiment, a computer device 1002 may include one or more processors 1004, such as one or more central processing units (CPUs), each of which can implement one or more hardware threads. The computer device 1002 may also include any memory 1006 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, the memory 1006 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 1002. In one case, when the processor 1004 executes associated instructions stored in any memory or combination of memories, the computer device 1002 can perform any operation of the associated instructions. The computer device 1002 also includes one or more drive mechanisms 1008 for interacting with any memory, such as hard disk drive mechanisms, optical disk drive mechanisms, etc.

[0228] Computer device 1002 may also include an input / output module 1010 (I / O) for receiving various inputs (via input device 1012) and providing various outputs (via output device 1014). A specific output mechanism may include a presentation device 1016 and an associated graphical user interface (GUI) 1018. In other embodiments, the input / output module 1010 (I / O), input device 1012, and output device 1014 may be omitted, and the device may function solely as a computer device within a network. Computer device 1002 may also include one or more network interfaces 1020 for exchanging data with other devices via one or more communication links 1022. One or more communication buses 1024 couple the components described above together.

[0229] The communication link 1022 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. The communication link 1022 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0230] Corresponding to Figures 2-4 In addition to the methods described above, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the above-described methods.

[0231] This embodiment also provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to perform the following: Figures 2-4 The method shown.

[0232] It should be understood that in the various embodiments of this document, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0233] It should also be understood that, in the embodiments herein, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following associated objects have an "or" relationship.

[0234] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0235] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0236] In the embodiments provided herein, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0237] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described herein, depending on actual needs.

[0238] Furthermore, the functional units in the various embodiments of this document can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0239] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this paper, in essence, or the part that contributes to the prior art, or all or 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 this paper. 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.

[0240] This document uses specific embodiments to illustrate the principles and implementation methods of this document. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this document. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this document. Therefore, the content of this specification should not be construed as a limitation of this document.

Claims

1. A method for detecting planar features of crack zones, characterized in that, include: Acquire three-dimensional seismic data corresponding to multiple spatial locations within the fracture zone; The dip angle and azimuth angle of the geological texture at the target spatial location are determined based on the three-dimensional seismic data. The analysis window for the geological texture is determined based on the dip angle and azimuth angle of the geological texture, as well as the coordinates of the target spatial location corresponding to the geological texture. The surface coefficients of the geological texture are calculated based on the analysis window. The surface coefficients of the geological texture are substituted into the fracture enhancement curvature function to obtain the curvature value of the geological texture; wherein, the fracture enhancement curvature function is obtained by applying a first curvature function characterizing the anticline features of the geological texture. and the second curvature function characterizing the synclinal features of geological texture The result is obtained by calculating the mean square error; its form is: ; Based on the curvature value of the geological texture, calculate the projection of the geological texture and the ground parallel to the target spatial location. By using the above method, the projections of geological textures corresponding to all spatial locations in the fracture zone are obtained, and the projections are reconstructed to obtain the fracture zone image; The step of determining the analysis window for the geological texture based on its dip angle and azimuth angle, and the coordinates of the target spatial location corresponding to the geological texture, further includes: Obtain the dip angle and azimuth angle of the geological texture at the target spatial location; According to the formula Construct the analysis window for the geological texture; Among them, the right side of the equation and Let be the dip angle and azimuth angle of the geological texture at the target spatial location, respectively. The right side of the equation... , and The coordinates of the target spatial location are shown on the left side of the equation. This is represented as an analysis window function, wherein the analysis window function is used to construct the analysis window; The step of calculating the surface coefficients of the geological texture based on the analysis time window further includes: According to the formula , , , and Calculate several surface coefficients a, b, c, d, and e of the geological texture; Wherein, a is the dip angle direction difference coefficient, b is the azimuth angle direction difference coefficient, c is the difference mean coefficient, d is the dip angle of the geological texture, and e is the azimuth angle of the geological texture; P is the analysis window function. The tilt angle, q is the analysis window function. The direction angle.

2. The method for detecting planar features of crack zones according to claim 1, characterized in that, The step of determining the dip angle and azimuth angle of the geological texture at the target spatial location based on the three-dimensional seismic data includes: From the three-dimensional seismic data, obtain three gradient vector volumes that correspond one-to-one with the three directions of the geological texture at the target spatial location; Perform a dextral operation on the three gradient vector volumes to obtain the gradient structure tensor matrix of the geological texture; The gradient structure tensor matrix is ​​subjected to spectral decomposition to obtain three feature vectors corresponding to the geological texture, and three feature values ​​corresponding to the three feature vectors. Select the eigenvector corresponding to the largest eigenvalue among the three eigenvalues; Based on the selected feature vectors, the dip angle and azimuth angle of the geological texture are determined.

3. The method for detecting planar features of crack zones according to claim 2, characterized in that, Before obtaining the three gradient vector volumes from the three-dimensional seismic data, which correspond one-to-one with the three directions of the geological texture at the target spatial location, the process includes: The three-dimensional seismic data is filtered using a three-dimensional Gaussian smoothing filter.

4. A device for detecting planar features of crack zones, characterized in that, include: The data acquisition unit is used to acquire three-dimensional seismic data corresponding to several spatial locations within the fracture zone. The parameter acquisition unit is used to determine the dip angle and azimuth angle of the geological texture at the target spatial location based on the three-dimensional seismic data. The time window creation unit is used to determine the analysis time window of the geological texture based on the dip angle and azimuth angle of the geological texture and the coordinates of the target spatial position corresponding to the geological texture. A surface coefficient calculation unit is used to calculate the surface coefficient of the geological texture based on the analysis time window; The curvature value calculation unit is used to input the surface coefficients of the geological texture into the fracture enhancement curvature function to obtain the curvature value of the geological texture; wherein, the fracture enhancement curvature function is obtained by applying a first curvature function characterizing the anticline features of the geological texture. and the second curvature function characterizing the synclinal features of geological texture The mean square error is calculated; the expression is as follows: ; The projection calculation unit is used to calculate the projection of the geological texture in the direction parallel to the ground corresponding to the target spatial location, based on the curvature value of the geological texture. The image reconstruction unit is used to obtain the projection of the geological texture corresponding to all spatial locations in the fracture zone through the above unit, and to perform projection reconstruction to obtain the fracture zone image; The time window creation unit is specifically used for: Obtain the dip angle and azimuth angle of the geological texture at the target spatial location; According to the formula Construct the analysis window for the geological texture; Among them, the right side of the equation and Let be the dip angle and azimuth angle of the geological texture at the target spatial location, respectively. The right side of the equation... , and The coordinates of the target spatial location are shown on the left side of the equation. This is represented as an analysis window function, wherein the analysis window function is used to construct the analysis window; The surface coefficient calculation unit is specifically used for: According to the formula , , , and Calculate several surface coefficients a, b, c, d, and e of the geological texture; Wherein, a is the dip angle direction difference coefficient, b is the azimuth angle direction difference coefficient, c is the difference mean coefficient, d is the dip angle of the geological texture, and e is the azimuth angle of the geological texture; P is the analysis window function. The tilt angle, q is the analysis window function. The direction angle.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the crack zone planar feature detection method as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the crack zone planar feature detection method as described in any one of claims 1-3.

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