Stratigraphic-structure-oriented pre-stack texture attribute calculation method and system

Abstract

A stratigraphic-structure-oriented pre-stack texture attribute calculation method, comprising: collecting four-dimensional seismic data, and calculating a preliminary dip volume corresponding to the four-dimensional seismic data (S110); constructing a stratigraphic-structure-oriented gradient structure tensor matrix GST on the basis of the preliminary dip volume, and calculating a first dip volume on the basis of the stratigraphic-structure-oriented gradient structure tensor matrix GST (S120); and constructing a stratigraphic-structure-oriented gray-level co-occurrence matrix G on the basis of the first dip volume, and calculating a texture feature attribute on the basis of the stratigraphic-structure-oriented gray-level co-occurrence matrix G, so as to obtain a texture feature attribute calculation result (S130). Further provided is a stratigraphic-structure-oriented pre-stack texture attribute calculation system. The method and system visually extract texture attributes according to stratigraphic structure features to describe structural features of pre-stack seismic data, thereby improving the spatial continuity of the texture feature attribute calculation result.

Description

Stratigraphic structure-guided prestack texture attribute calculation method and system Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a stratum structure-guided prestack texture attribute calculation method, a stratum structure-guided prestack texture attribute calculation system, a machine-readable storage medium and an electronic device. Background Art

[0002] Texture is an inherent property of almost all surfaces, such as the grain of wood, the weave of fabric, and the pattern of crops in a field. It contains important information about the surface structure and its relationship to the surrounding environment. Because the texture properties of an image appear to carry useful information for identification purposes, texture properties are one of the most important attributes. In 1973, Haralick used the gray-level co-occurrence matrix to extract texture properties and conducted analytical research. In 1984, Love used texture features to convert seismic profiles into texture images and applied them to common structures. Texture properties can be used to distinguish fill channels, stratigraphic heterogeneity, etc., can reflect the visual characteristics of homogeneous phenomena in images, and embody the slowly changing or periodic changes in the stratigraphic and sedimentary structural organization and arrangement properties of underground strata and sediments.

[0003] Methods for extracting texture attributes include statistical analysis, structural analysis, signal processing, and modeling. The most commonly used is the gray-level co-occurrence matrix method, which is based on statistical analysis. Existing gray-level co-occurrence matrix methods are ineffective in extracting texture attributes in areas with significant stratigraphic variations. Furthermore, texture attribute calculations often focus on post-stack seismic data, neglecting the azimuthal and subtle geological information carried by pre-stack seismic data. Therefore, how to accurately and intuitively extract texture attributes from pre-stack seismic data and describe its structural characteristics is an urgent challenge.

[0004] Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a stratigraphic structure-guided prestack texture attribute calculation method and system to at least solve the above-mentioned problem of failing to accurately and intuitively extract texture attributes to describe the structural characteristics of prestack seismic data.

[0006] In order to achieve the above-mentioned purpose, the first aspect of the present invention provides a stratigraphic structure-guided pre-stack texture attribute calculation method, including: collecting four-dimensional seismic data u(x, y, t, d), and calculating the preliminary dip data volume corresponding to the four-dimensional seismic data u(x, y, t, d); constructing a stratigraphic structure-guided gradient structure tensor matrix GST according to the preliminary dip data volume, and performing a first dip data volume calculation based on the stratigraphic structure-guided gradient structure tensor matrix GST; constructing a stratigraphic structure-guided grayscale co-occurrence matrix G according to the first dip data volume, and calculating texture feature attributes based on the stratigraphic structure-guided grayscale co-occurrence matrix G to obtain texture feature attribute calculation results.

[0007] Optionally, the above-mentioned acquisition of four-dimensional seismic data u(x, y, t, d) and calculation of the preliminary dip data volume corresponding to the four-dimensional seismic data u(x, y, t, d) include: acquiring four-dimensional seismic data u(x, y, t, d) to be analyzed from pre-stack seismic data; and obtaining the preliminary dip data volume based on the four-dimensional seismic data u(x, y, t, d) using a dip scanning method.

[0008] Optionally, the above-mentioned method of obtaining a preliminary dip data volume based on the four-dimensional seismic data u(x, y, t, d) by using a dip scanning method includes: obtaining the instantaneous frequency ω(t) of the four-dimensional seismic data u(x, y, t, d) based on an instantaneous frequency calculation formula; wherein the instantaneous frequency calculation formula is:

[0009] represents the instantaneous phase, t represents the acquisition time corresponding to the four-dimensional seismic data, u(t) represents the four-dimensional seismic data, u H (t) represents the Hilbert transform data corresponding to the four-dimensional seismic data;

[0010] Based on the instantaneous wave number calculation formula, the instantaneous wave number k of the four-dimensional seismic data u(x,y,t,d) in the x direction of the survey line is obtained. x (t,x,y) and the instantaneous wave number k in the y direction of the survey line y (t,x,y); the instantaneous wave number calculation formula is:

[0011] φ(t,x,y) represents the instantaneous phase of four-dimensional seismic data;

[0012] Based on the instantaneous frequency ω(t) of the four-dimensional seismic data u(x,y,t,d), the instantaneous wave number k in the x direction of the survey line x (t,x,y) and the instantaneous wave number k in the y direction of the survey line y (t,x,y), calculate the preliminary inclination data volume; where p(x,y,t,d)=k x (t,x,y) / ω(t),q(x,y,t,d)=ky (t,x,y) / ω(t), p(x,y,t,d) and q(x,y,t,d) are the preliminary inclination data volumes.

[0013] Optionally, the above-mentioned construction of the stratigraphic structure-oriented gradient structure tensor matrix GST based on the preliminary dip data volume includes: collecting new four-dimensional seismic data u′(x, y, t, d) along the stratigraphic structure direction from the pre-stack seismic data; and constructing the stratigraphic structure-oriented gradient structure tensor matrix GST based on the new four-dimensional seismic data u′(x, y, t, d).

[0014] Optionally, the above-mentioned acquisition of new four-dimensional seismic data u′(x, y, t, d) from pre-stack seismic data along the stratigraphic structure direction includes: constructing a first rotation matrix R(p) and a second rotation matrix R(q) based on the preliminary dip data body; determining a first computational grid W′ based on the first rotation matrix R(p) and the second rotation matrix R(q); and acquiring new four-dimensional seismic data u′(x, y, t, d) in the pre-stack seismic data based on the first computational grid W′.

[0015] Optionally, the above-mentioned determination of the first computational grid W′ based on the first rotation matrix R(p) and the second rotation matrix R(q) includes: calculating the first grid based on the first rotation matrix R(p); wherein W1=R(p)*W, W1 is the first grid, and W represents the initial computational grid used for acquiring four-dimensional seismic data; calculating the second grid based on the second rotation matrix R(q); wherein W2=R(q)*W, W2 is the second grid; and determining the first computational grid W′ based on the first grid and the second grid.

[0016] Optionally, the calculation of the first dip data volume based on the stratum structure-guided gradient structure tensor matrix GST includes: performing matrix decomposition on the gradient structure tensor matrix GST based on a matrix eigendecomposition rule; wherein the matrix decomposition formula is:

[0017] υ1, υ2 and υ3 are three eigenvectors of the gradient structure tensor matrix GST, and λ1, λ2 and λ3 are three eigenvalues ​​of the gradient structure tensor matrix GST; the three eigenvalues ​​of the gradient structure tensor matrix GST are sorted to obtain the maximum eigenvalue; based on the vector in the x direction, the vector in the y direction, and the vector in the t direction of the eigenvector corresponding to the maximum eigenvalue, the first inclination data volume is calculated.

[0018] Optionally, the above-mentioned construction of the stratigraphic structure-oriented grayscale co-occurrence matrix G based on the first dip data volume includes: collecting new four-dimensional seismic data u″(x, y, t, d) guided by the stratigraphic structure from the pre-stack seismic data along the direction of the first dip data volume; performing grayscale conversion on the new four-dimensional seismic data u″(x, y, t, d) guided by the stratigraphic structure; and constructing the stratigraphic structure-oriented grayscale co-occurrence matrix G based on the grayscale converted four-dimensional seismic data u″(x, y, t, d); wherein the grayscale co-occurrence matrix G is:

[0019] Indicates the calculation direction of the gray-level co-occurrence matrix, G(i, j) represents the gray-level co-occurrence matrix, and i and j represent the positions in the gray-level co-occurrence matrix.

[0020] Optionally, the above-mentioned acquisition of new four-dimensional seismic data u″(x, y, t, d) guided by the stratum structure from the pre-stack seismic data along the direction of the first dip data body includes: constructing a third rotation matrix R(p′) and a fourth rotation matrix R(q′) according to the first dip data body; determining a second calculation grid W″ based on the third rotation matrix R(p′) and the fourth rotation matrix R(q′); and acquiring new four-dimensional seismic data u″(x, y, t, d) guided by the stratum structure in the pre-stack seismic data based on the second calculation grid W″.

[0021] Optionally, the gray-level co-occurrence matrix G based on the stratum structure orientation calculates the texture feature attributes, including:

[0022] Based on the stratum structure-oriented gray-level co-occurrence matrix G, the statistical probability p of the element G(i,j) in the gray-level co-occurrence matrix G is obtained. ij ;in, N represents the selected gray level;

[0023] Based on the statistical probability p of the element G(i,j) in the gray-level co-occurrence matrix G ij , calculate texture feature attributes.

[0024] Optionally, the texture feature attribute includes an energy value;

[0025] The energy value is calculated as follows:

[0026] Here, Energy represents the energy value.

[0027] Optionally, the texture feature attributes include contrast;

[0028] The formula for calculating contrast is:

[0029] Among them, Contrast represents contrast.

[0030] Optionally, the texture feature attributes include homogeneity;

[0031] The formula for calculating homogeneity is:

[0032] Among them, Homogeneity means homogeneity.

[0033] Optionally, the texture feature attribute includes an entropy value;

[0034] The formula for calculating entropy is:

[0035] Among them, Entropy represents the entropy value.

[0036] Optionally, the above-mentioned texture feature attributes include correlation;

[0037] The formula for calculating correlation is:

[0038] in, Correlation indicates correlation.

[0039] The second aspect of the present invention provides a stratigraphic structure-guided pre-stack texture attribute calculation system, comprising: a preliminary dip data volume calculation module, used to collect four-dimensional seismic data u(x, y, t, d), and calculate the preliminary dip data volume corresponding to the four-dimensional seismic data u(x, y, t, d); a first dip data volume calculation module, used to construct a stratigraphic structure-guided gradient structure tensor matrix GST based on the preliminary dip data volume, and perform first dip data volume calculation based on the stratigraphic structure-guided gradient structure tensor matrix GST; a texture feature attribute calculation module, used to construct a stratigraphic structure-guided grayscale co-occurrence matrix G based on the first dip data volume, and calculate texture feature attributes based on the stratigraphic structure-guided grayscale co-occurrence matrix G to obtain texture feature attribute calculation results.

[0040] In a third aspect of the present invention, a machine-readable storage medium is provided. The machine-readable storage medium stores instructions. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned stratigraphic structure-guided pre-stack texture attribute calculation method.

[0041] In a fourth aspect of the present invention, an electronic device is provided. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned stratigraphic structure-guided prestack texture attribute calculation method is implemented.

[0042] The above-mentioned technical solution provides a stratigraphic structure-guided prestack texture attribute calculation method and system. Prestack seismic data is used to collect four-dimensional seismic data u(x, y, t, d) from the prestack seismic data, and a preliminary dip data volume corresponding to the four-dimensional seismic data u(x, y, t, d) is calculated. Compared to post-stack seismic data, prestack seismic data contains richer stratigraphic and sedimentary information and exhibits subtle differences between different azimuths or offsets. A stratigraphic structure-guided gradient structure tensor matrix (GST) is constructed based on the preliminary dip data volume, and a first dip data volume calculation is performed based on this stratigraphic structure-guided gradient structure tensor matrix (GST). This improves the accuracy of dip calculations in areas of dramatic stratigraphic variation, thereby improving the accuracy of texture attribute calculations. A stratigraphic structure-guided gray-level co-occurrence matrix (G) is constructed based on the first dip data volume, and texture feature attributes are calculated based on this stratigraphic structure-guided gray-level co-occurrence matrix (G). The resulting texture feature attribute calculations improve signal resolution and continuity along the stratigraphic direction. This method and system intuitively extract texture attributes based on the structural characteristics of the stratum, with clearer physical properties, and thus better describe the structural characteristics of pre-stack seismic data, thereby more finely describing the details of stratum changes and improving the spatial continuity of the calculation results of texture feature attributes.

[0043] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings:

[0045] FIG1 is a flow chart of a stratigraphic structure-guided prestack texture attribute calculation method provided by one embodiment of the present invention;

[0046] FIG2 is a flow chart of another stratigraphic structure-guided prestack texture attribute calculation method provided by one embodiment of the present invention;

[0047] FIG3 is a schematic diagram of layer-by-layer amplitude slices of four-dimensional pre-stack seismic data provided by one embodiment of the present invention;

[0048] FIG4 is a schematic diagram of a calculated layer-by-layer texture attribute slice provided by an embodiment of the present invention;

[0049] FIG5 is a block diagram of a stratum structure-guided pre-stack texture attribute calculation system provided by one embodiment of the present invention;

[0050] FIG6 is a schematic structural diagram of an electronic device provided in a preferred embodiment of the present invention.

[0051] DESCRIPTION OF REFERENCE NUMERALS 10 - electronic device, 100 - processor, 101 - memory, 102 - computer program DETAILED DESCRIPTION

[0052] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0053] FIG1 is a flow chart of a stratigraphic structure-guided prestack texture attribute calculation method provided by one embodiment of the present invention, and FIG2 is a flow chart of another stratigraphic structure-guided prestack texture attribute calculation method provided by one embodiment of the present invention. As shown in FIG1 and FIG2, an embodiment of the present invention provides a stratigraphic structure-guided prestack texture attribute calculation method, comprising:

[0054] S110: collecting four-dimensional seismic data u(x, y, t, d), and calculating a preliminary dip data volume corresponding to the four-dimensional seismic data u(x, y, t, d);

[0055] In some implementations of this embodiment, the above-mentioned acquisition of four-dimensional seismic data u(x, y, t, d) and calculation of the preliminary dip data body corresponding to the four-dimensional seismic data u(x, y, t, d) include: acquiring the four-dimensional seismic data u(x, y, t, d) to be analyzed from pre-stack seismic data; and obtaining the preliminary dip data body based on the four-dimensional seismic data u(x, y, t, d) using a dip scanning method.

[0056] Among them, in the four-dimensional seismic data u(x, y, t, d), x represents xline, y represents inline, t represents time, and d represents direction.

[0057] In some implementations of this embodiment, the above-mentioned method of obtaining a preliminary dip data volume based on the four-dimensional seismic data u(x, y, t, d) by using the dip scanning method includes: obtaining the instantaneous frequency ω(t) of the four-dimensional seismic data u(x, y, t, d) based on the instantaneous frequency calculation formula; wherein the instantaneous frequency calculation formula is:

[0058] represents the instantaneous phase, t represents the acquisition time corresponding to the four-dimensional seismic data, u(t) represents the four-dimensional seismic data, u H (t) represents the Hilbert transform data corresponding to the four-dimensional seismic data; based on the instantaneous wave number calculation formula, the instantaneous wave number k of the four-dimensional seismic data u(x, y, t, d) in the x direction of the survey line is obtained x (t, x, y) and the instantaneous wave number k in the y direction of the survey line y (t, x, y); the instantaneous wave number calculation formula is: φ(t, x, y) represents the instantaneous phase of the four-dimensional seismic data; based on the instantaneous frequency ω(t) of the four-dimensional seismic data u(x, y, t, d), the instantaneous wave number k in the x direction of the survey line x (t, x, y) and the instantaneous wave number k in the y direction of the survey line y (t, x, y), calculate the preliminary inclination data volume; where,

[0059] p(x,y,t,d)=k x (t,x,y) / ω(t),q(x,y,t,d)=k y (t,x,y) / ω(t),

[0060] p(x,y,t,d) and q(x,y,t,d) are the preliminary inclination data volumes.

[0061] Specifically, the four-dimensional seismic data u(x, y, t, d) to be analyzed is first collected from the pre-stack seismic data. Then, the dip scanning method is used to obtain the preliminary dip data volume. The specific process of using the dip scanning method to obtain the preliminary dip data volume is as follows: First, starting from the definition of instantaneous frequency ω:

[0062] in: represents the instantaneous phase, u(t) represents the four-dimensional seismic data, u H (t) is its Hilbert transform; use the time-frequency representation method to calculate the instantaneous frequency, Rewrite as Where: p z (t,f) is the time-frequency representation of the signal spectrum. Get the instantaneous wave number of the four-dimensional seismic data u(x,y,t,d) in the x direction of the survey line

[0063] k x (t, x, y), according to Get the instantaneous wave number k of the four-dimensional seismic data u(x,y,t,d) in the y direction of the survey line y (t,x,y). Finally, the preliminary inclination data volume p(x,y,t,d) is calculated as p(x,y,t,d)=k x (t,x,y) / ω(t), the preliminary inclination data volume q(x,y,t,d) is (x,y,t,d)=k y (t,x,y) / ω(t), where p(x,y,t,d) represents the apparent dip angle of the four-dimensional seismic data u(x,y,t,d) in the x direction of the survey line, and q(x,y,t,d) represents the apparent dip angle of the four-dimensional seismic data u(x,y,t,d) in the y direction of the lateral line.

[0064] S120: constructing a stratum structure-oriented gradient structure tensor matrix GST according to the preliminary dip data volume, and performing a first dip data volume calculation based on the stratum structure-oriented gradient structure tensor matrix GST;

[0065] In some implementations of this embodiment, the above-mentioned construction of the stratigraphic structure-oriented gradient structure tensor matrix GST based on the preliminary dip data volume includes: collecting new four-dimensional seismic data u′(x, y, t, d) along the stratigraphic structure direction from the pre-stack seismic data; and constructing the stratigraphic structure-oriented gradient structure tensor matrix GST based on the new four-dimensional seismic data v′(x, y, t, d).

[0066] In some implementations of this embodiment, the above-mentioned acquisition of new four-dimensional seismic data u′(x, y, t, d) from pre-stack seismic data along the stratigraphic structure direction includes: constructing a first rotation matrix R(p) and a second rotation matrix R(q) based on the preliminary dip data body; determining a first computational grid W′ based on the first rotation matrix R(p) and the second rotation matrix R(q); and acquiring new four-dimensional seismic data u′(x, y, t, d) in the pre-stack seismic data based on the first computational grid W′.

[0067] In some implementations of this embodiment, the above-mentioned determination of the first computational grid W′ based on the first rotation matrix R(p) and the second rotation matrix R(q) includes: calculating the first grid based on the first rotation matrix R(p); wherein W1=R(p)*W, W1 is the first grid, and W represents the initial computational grid used for acquiring four-dimensional seismic data; calculating the second grid based on the second rotation matrix R(q); wherein W2=R(q)*W, W2 is the second grid; determining the first computational grid W′ based on the first grid and the second grid.

[0068] Specifically, based on pre-stack seismic data, new four-dimensional seismic data u′(x, y, t, d) are first selected along the stratigraphic structure direction. The specific process of selecting new four-dimensional seismic data u′(x, y, t, d) is as follows: First, based on the preliminary dip data volumes p(x, y, t, d) and q(x, y, t, d), the first rotation matrix R(p) and the second rotation matrix R(q) are constructed respectively. Taking the rotation around the x-axis as an example, p represents the value of the preliminary dip data body p (x, y, t, d) at the center point of the computational grid W. The first grid is obtained according to the formula W1 = R(p)*W. The second grid is obtained according to the formula W2 = R(q)*W. The stratigraphic structure-oriented gradient structure tensor matrix computational grid, i.e., the first computational grid W′, is updated according to the first grid and the second grid. The first computational grid W′ is used to reselect new four-dimensional seismic data u′(x, y, t, d) from the pre-stack seismic data. Thus, the selection of the new four-dimensional seismic data u′(x, y, t, d) is completed. Then, for the stratigraphic structure-oriented four-dimensional seismic data u′(x, y, t, d), the first computational grid W′ is updated. i Construct the gradient structure tensor matrix GST(x,y,t,d i ), achieving the purpose of constructing the formation structure-oriented gradient structure tensor matrix GST.

[0069] Among them, the specific process of using the first calculation grid W′ to reselect new four-dimensional seismic data u′(x, y, t, d) in the pre-stack seismic data is: first, it can be moved along the x direction according to the moving distance of the first grid, and then moved along the y direction according to the moving distance of the second grid, and the pre-stack seismic data circled by the first calculation grid W′ after the final movement is used as the new four-dimensional seismic data u′(x, y, t, d).

[0070] Among them, d i Direction represents the i-th direction in d, such as u(x, y, t, d i ) is the data of the i-th direction of u(x, y, t, d).

[0071] In the above implementation process, before using the first computational grid W′ to select new four-dimensional seismic data u′(x, y, t, d), the method can also use an interpolation method to make the prestack seismic data denser to obtain dense prestack seismic data.

[0072] In some implementations of this embodiment, the calculation of the first dip data volume based on the stratum structure-guided gradient structure tensor matrix GST includes: performing matrix decomposition on the gradient structure tensor matrix GST based on a matrix eigendecomposition rule; wherein the matrix decomposition formula is:

[0073] υ1, υ2 and υ3 are three eigenvectors of the gradient structure tensor matrix GST, and λ1, λ2 and λ3 are three eigenvalues ​​of the gradient structure tensor matrix GST; the three eigenvalues ​​of the gradient structure tensor matrix GST are sorted to obtain the maximum eigenvalue; based on the vector in the x direction, the vector in the y direction, and the vector in the t direction of the eigenvector corresponding to the maximum eigenvalue, the first inclination data volume is calculated.

[0074] Specifically, based on the gradient structure tensor matrix GST guided by the formation structure, a high-precision dip data volume is calculated to obtain the first dip data volume. The process of calculating the first dip data volume is as follows: first, the gradient structure tensor matrix GST is matrix decomposed to obtain the three eigenvalues ​​of the gradient structure tensor matrix GST, and the three eigenvalues ​​of the gradient structure tensor matrix GST are sorted to obtain the maximum eigenvalue. The eigenvector v1(x, y, t, d i ) vector v in the opposite direction of x 1x (x,y,t,d i ), vector v in the y direction 1y (x,y,t,d i ) and the vector v in the reverse direction of t 1t (x,y,t,d i ), calculate d i The inclination data volume p′(x,y,t,d i ) and q′(x,y,t,d i ):in,

[0075] The inclination data volume p′(x,y,t,d i ) and q′(x,y,t,d i ) is the first inclination data volume.

[0076] S130: constructing a stratum structure-oriented gray-level co-occurrence matrix G according to the first dip data volume, and calculating texture feature attributes based on the stratum structure-oriented gray-level co-occurrence matrix G to obtain a texture feature attribute calculation result.

[0077] In some implementations of this embodiment, the above-mentioned construction of the stratigraphic structure-oriented grayscale co-occurrence matrix G based on the first dip data volume includes: acquiring new four-dimensional seismic data u″(x, y, t, d) guided by the stratigraphic structure from the pre-stack seismic data along the direction of the first dip data volume; performing grayscale conversion on the new four-dimensional seismic data u″(x, y, t, d) guided by the stratigraphic structure; and constructing the stratigraphic structure-oriented grayscale co-occurrence matrix G based on the grayscale converted four-dimensional seismic data u″(x, y, t, d); wherein the grayscale co-occurrence matrix G is: Indicates the calculation direction of the gray-level co-occurrence matrix, G(i, j) represents the gray-level co-occurrence matrix, and i and j represent the positions in the gray-level co-occurrence matrix.

[0078] In some embodiments of the present embodiment, the above-mentioned acquisition of new four-dimensional seismic data u″(x, y, t, d) guided by the stratum structure from the pre-stack seismic data along the direction of the first dip data body includes: constructing a third rotation matrix R(p′) and a fourth rotation matrix R(q′) according to the first dip data body; determining a second computational grid W″ based on the third rotation matrix R(p′) and the fourth rotation matrix R(q′); and acquiring new four-dimensional seismic data u″(x, y, t, d) guided by the stratum structure in the pre-stack seismic data based on the second computational grid W″.

[0079] Specifically, the four-dimensional seismic data u′(x, y, t, d) is first updated along the direction of the first dip data body to obtain new four-dimensional seismic data u″(x, y, t, d) guided by the stratum structure. The acquisition process of the new four-dimensional seismic data u″(x, y, t, d) guided by the stratum structure is as follows: First, according to the first dip data body p′(x, y, t, di) and q′(x, y, t, di), the third rotation matrix R(p′) and the fourth rotation matrix R(q′) are constructed respectively. The third grid is obtained according to the formula W3=R(p′)*W1, and the fourth grid is obtained according to the formula W4=R(q′)*W1. The stratigraphic structure-guided gradient structure tensor matrix calculation grid is updated according to the third grid and the fourth grid to obtain the second calculation grid W". The second calculation grid W" is used to reselect new stratigraphic structure-guided four-dimensional seismic data u"(x, y, t, d) in the prestack seismic data (the prestack seismic data can be dense prestack seismic data). In this way, the selection of new stratigraphic structure-guided four-dimensional seismic data u"(x, y, t, d) is completed. Then, the new four-dimensional seismic data u"(x, y, t, d) selected according to the stratigraphic structure direction is grayscale converted, and the grayscale level is designated as N. Based on the grayscale-converted four-dimensional seismic data u"(x, y, t, d), a stratigraphic structure-guided grayscale co-occurrence matrix G is constructed. In this way, the purpose of constructing the stratigraphic structure-guided grayscale co-occurrence matrix G according to the first dip data body is achieved.

[0080] In some implementations of this embodiment, the above-mentioned gray level co-occurrence matrix G based on the stratum structure orientation calculates the texture feature attribute, including: based on the stratum structure orientation gray level co-occurrence matrix G, obtaining the statistical probability p of the element G(i,j) in the gray level co-occurrence matrix G ij ;in, N represents the selected gray level; based on the statistical probability p of the element G(i, j) in the gray level co-occurrence matrix G ij , calculate texture feature attributes.

[0081] Specifically, according to the statistical probability p of the element G(i, j) in the gray level co-occurrence matrix G guided by the stratigraphic structure ij, calculate the texture feature attributes guided by the stratum structure, so as to achieve the purpose of intuitively extracting texture attributes based on the stratum structure characteristics.

[0082] In the above implementation process, this method uses prestack seismic data and uses a stratigraphic structure-guided gradient structure tensor matrix to extract high-precision dip and azimuth information. Texture attributes are intuitively extracted based on stratigraphic structural characteristics, making physical properties clearer and better describing the structural characteristics of prestack seismic data. This allows for a more detailed description of stratigraphic variation details and improves the spatial continuity of the texture feature attribute calculation results. This method, based on the gray-level co-occurrence matrix, incorporates stratigraphic structural information and uses prestack seismic data for texture attribute calculation, offering the following advantages:

[0083] 1. This method uses pre-stack seismic data to analyze geological bodies. Compared with post-stack seismic data, pre-stack seismic data contains richer stratigraphic and sedimentary information and has subtle differences between different azimuths or offsets.

[0084] 2. Based on traditional dip scanning, this method incorporates stratigraphic structural information and uses the preliminary dip data volume obtained through preliminary calculation to constrain the gradient structure tensor matrix guided by the stratigraphic structure. This improves the dip calculation accuracy in areas with drastic stratigraphic changes, and obtains high-precision dip data guided by the stratigraphic structure, namely the first dip data volume, thereby improving the accuracy of texture attribute calculation.

[0085] 3. In the process of constructing the gray-level co-occurrence matrix, this method uses high-precision dip angle as the stratigraphic structure-guided constraint to construct a stratigraphic structure-guided gray-level co-occurrence matrix. Based on the stratigraphic structure-guided gray-level co-occurrence matrix, pre-stack seismic texture attribute calculation is realized, which can improve signal resolution and continuity along the layer direction.

[0086] Among them, please refer to Figures 3 and 4 for the implementation effect of this method. Figure 3 is a schematic diagram of an amplitude slice along the layer of four-dimensional pre-stack seismic data provided by an embodiment of the present invention, and Figure 4 is a schematic diagram of a calculated texture attribute slice along the layer provided by an embodiment of the present invention.

[0087] In some implementations of this embodiment, the texture feature attribute includes an energy value; the energy value is calculated as follows: Here, Energy represents the energy value.

[0088] Specifically, energy is defined as the sum of the squares of the gray-level co-occurrence matrix element values. The energy value reflects the uniformity of the image's grayscale distribution and the coarseness of its texture. Images with uniform grayscale distribution and regular variations have higher energy values. When all elements in the gray-level co-occurrence matrix G are equal, the energy value is low; when some values ​​in the gray-level co-occurrence matrix G are large while others are small, the energy value is high; and when the elements in the gray-level co-occurrence matrix G are concentrated, the energy value is high.

[0089] In some implementations of this embodiment, the texture feature attribute includes contrast; the calculation formula for contrast is: Among them, Contrast represents contrast.

[0090] Specifically, contrast reflects the degree of change in the stratigraphic structure of a location and its surroundings. Deeper texture grooves have greater contrast and a clearer visual effect. Conversely, lower contrast results in shallower grooves and a blurred effect. The greater the value of the element in the gray-level co-occurrence matrix G, the farther away from the diagonal, the greater the contrast.

[0091] In some implementations of this embodiment, the texture feature attribute includes homogeneity; the calculation formula for homogeneity is: Among them, Homogeneity means homogeneity.

[0092] Specifically, homogeneity reflects the uniformity of texture variation along the stratigraphic structure. A high homogeneity value indicates a lack of variation between different regions of the image texture, indicating local uniformity. Larger values ​​for diagonal elements in the gray-level co-occurrence matrix G indicate a high homogeneity value. Therefore, it can be used to quantify reflective continuity and reflect the degree of stratigraphic variation. The more consistent the variation in a geological body, the greater the homogeneity value.

[0093] In some implementations of this embodiment, the texture feature attribute includes an entropy value; the entropy value is calculated as follows: Among them, Entropy represents the entropy value.

[0094] Specifically, the entropy reflects the randomness of texture variations along the stratigraphic structure. When all elements in the gray-level co-occurrence matrix G have the greatest randomness or all values ​​are nearly equal, the entropy is high. In other words, the more complex the geological body's variations, the greater the entropy.

[0095] In some implementations of this embodiment, the texture feature attributes include correlation; the correlation is calculated as follows: in,

[0096] Correlation indicates correlation.

[0097] Specifically, correlation reflects the similarity between neighboring pixels in the horizontal and vertical directions.

[0098] FIG5 is a block diagram of a stratigraphic structure-guided pre-stack texture attribute calculation system provided by an embodiment of the present invention. As shown in FIG5 , an embodiment of the present invention provides a stratigraphic structure-guided pre-stack texture attribute calculation system, comprising:

[0099] A preliminary dip data volume calculation module is used to collect four-dimensional seismic data u (x, y, t, d) and calculate the preliminary dip data volume corresponding to the four-dimensional seismic data u (x, y, t, d);

[0100] A first dip data volume calculation module is used to construct a stratum structure-oriented gradient structure tensor matrix GST according to the preliminary dip data volume, and perform first dip data volume calculation based on the stratum structure-oriented gradient structure tensor matrix GST;

[0101] The texture feature attribute calculation module is used to construct a stratum structure-oriented gray level co-occurrence matrix G according to the first dip angle data volume, and calculate the texture feature attributes based on the stratum structure-oriented gray level co-occurrence matrix G to obtain the texture feature attribute calculation results.

[0102] Specifically, the system uses pre-stack seismic data and a stratigraphic structure-guided gradient structure tensor matrix to extract high-precision dip and azimuth information. It intuitively extracts texture attributes based on stratigraphic structural characteristics, making the physical properties clearer and thus better describing the structural characteristics of pre-stack seismic data. This allows for a more detailed description of stratigraphic change details and improves the spatial continuity of the texture feature attribute calculation results.

[0103] An embodiment of the present invention further provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by the processor 100, the processor 100 is configured to execute the above-mentioned stratigraphic structure-guided pre-stack texture attribute calculation method.

[0104] Machine-readable storage media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0105] An embodiment of the present invention also provides an electronic device 10, which includes a memory 101, a processor 100, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, the above-mentioned stratigraphic structure-guided pre-stack texture attribute calculation method is implemented.

[0106] Figure 6 is a schematic diagram of an electronic device provided in accordance with an embodiment of the present invention. As shown in Figure 6, electronic device 10 in accordance with this embodiment includes a processor 100, a memory 101, and a computer program 102 stored in memory 101 and executable on processor 100. When processor 100 executes computer program 102, the steps in the aforementioned method embodiment are implemented. Alternatively, when processor 100 executes computer program 102, the functions of the modules / units in the aforementioned device embodiment are implemented.

[0107] Exemplarily, the computer program 102 may be divided into one or more modules / units, one or more of which are stored in the memory 101 and executed by the processor 100 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 102 in the electronic device 10. For example, the computer program 102 may be divided into a preliminary inclination data volume calculation module, a first inclination data volume calculation module, and a texture feature attribute calculation module.

[0108] Electronic device 10 may be a computing device such as a desktop computer, laptop, PDA, or cloud server. Electronic device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will appreciate that FIG6 is merely an example of electronic device 10 and does not limit the scope of electronic device 10. The electronic device 10 may include more or fewer components than shown, or may combine certain components or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0109] The processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0110] The memory 101 may be an internal storage unit of the electronic device 10, such as a hard disk or memory of the electronic device 10. The memory 101 may also be an external storage device of the electronic device 10, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 10. Furthermore, the memory 101 may include both an internal storage unit of the electronic device 10 and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 may also be used to temporarily store data that has been output or is about to be output.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0112] Those skilled in the art will appreciate that embodiments of the present application may be provided as methods, systems, or computer program 102 products. Thus, the present application may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program 102 product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program 102 products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program 102 instructions. These computer program 102 instructions can be provided to a processor 100 of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor 100 of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0114] These computer program 102 instructions may also be stored in a computer-readable memory 101 that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory 101 produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0115] These computer program 102 instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0116] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0117] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A stratigraphic structure-guided prestack texture attribute calculation method, characterized in that: include: Collecting four-dimensional seismic data u(x, y, t, d), and calculating a preliminary dip data volume corresponding to the four-dimensional seismic data u(x, y, t, d); Constructing a formation structure-oriented gradient structure tensor matrix GST according to the preliminary dip data volume, and performing a first dip data volume calculation based on the formation structure-oriented gradient structure tensor matrix GST; A stratum structure-oriented gray-level co-occurrence matrix G is constructed according to the first dip angle data volume, and texture feature attributes are calculated based on the stratum structure-oriented gray-level co-occurrence matrix G to obtain a texture feature attribute calculation result.

2. The method for calculating prestack texture attributes guided by stratum structure according to claim 1, characterized in that: The collecting of four-dimensional seismic data u(x, y, t, d) and calculating the preliminary dip data volume corresponding to the four-dimensional seismic data u(x, y, t, d) include: Collecting four-dimensional seismic data u (x, y, t, d) to be analyzed from pre-stack seismic data; Based on the four-dimensional seismic data u(x, y, t, d), the dip scanning method is used to obtain the preliminary dip data volume.

3. The method for calculating prestack texture attributes guided by stratum structure according to claim 2, characterized in that: The method of obtaining a preliminary dip data volume based on the four-dimensional seismic data u (x, y, t, d) by using a dip scanning method includes: Based on the instantaneous frequency calculation formula, the instantaneous frequency ω(t) of the four-dimensional seismic data u(x, y, t, d) is obtained; wherein the instantaneous frequency calculation formula is: represents the instantaneous phase, t represents the acquisition time corresponding to the four-dimensional seismic data, u(t) represents the four-dimensional seismic data, u H (t) represents the Hilbert transform data corresponding to the four-dimensional seismic data; Based on the instantaneous wave number calculation formula, the instantaneous wave number k of the four-dimensional seismic data u (x, y, t, d) in the x direction of the survey line is obtained. x (t,x,y) and the instantaneous wave number k in the y direction of the survey line y (t, x, y); the instantaneous wave number calculation formula is: Represents the instantaneous phase of 4D seismic data; Based on the instantaneous frequency ω(t) of the four-dimensional seismic data u(x, y, t, d), the instantaneous wave number k in the x direction of the survey line x (t, x, y) and the instantaneous wave number k in the y direction of the survey line y (t, x, y) is used to calculate the initial inclination data volume; p(x, y, t, d) = k x (t,x,y) / ω(t), q(x,y,t,d)=k y (t, x, y) / ω(t), p(x, y, t, d) and q(x, y, t, d) are preliminary inclination data volumes.

4. The method for calculating prestack texture attributes guided by stratum structure according to claim 1, characterized in that: The step of constructing a formation structure-oriented gradient structure tensor matrix GST according to the preliminary dip angle data volume comprises: Acquire new four-dimensional seismic data u′(x, y, t, d) along the stratigraphic structure direction from the pre-stack seismic data; Based on the new four-dimensional seismic data u′(x, y, t, d), a stratigraphic structure-oriented gradient structure tensor matrix GST is constructed.

5. The method for calculating prestack texture attributes guided by stratum structure according to claim 4, characterized in that: The method of acquiring new four-dimensional seismic data u′(x, y, t, d) from the pre-stack seismic data along the stratum structure direction includes: According to the preliminary inclination data volume, construct the first rotation matrix R(p) and the second rotation matrix R(q); Based on the first rotation matrix R(p) and the second rotation matrix R(q), determine a first calculation grid W′; Based on the first computational grid W′, new four-dimensional seismic data u′(x, y, t, d) are acquired in the pre-stack seismic data.

6. The method for calculating prestack texture attributes guided by stratum structure according to claim 5, characterized in that: The determining of the first calculation grid W′ based on the first rotation matrix R(p) and the second rotation matrix R(q) comprises: Based on the first rotation matrix R(p), a first grid is calculated; wherein W1=R(p)*W, W1 is the first grid, and W represents the initial calculation grid used for acquiring four-dimensional seismic data; Based on the second rotation matrix R(q), calculate the second grid; wherein W2=R(q)*W, W2 is the second grid; Based on the first grid and the second grid, a first calculation grid W' is determined.

7. The method for calculating prestack texture attributes guided by stratum structure according to claim 1, characterized in that: The first dip angle data volume calculation based on the formation structure-guided gradient structure tensor matrix GST includes: Based on the matrix feature decomposition rule, the gradient structure tensor matrix GST is decomposed; the matrix decomposition formula is: υ1, υ2 and υ3 are the three eigenvectors of the gradient structure tensor matrix GST, λ1, λ2 and λ3 are the three eigenvectors of the gradient structure tensor matrix GST. Eigenvalue; Sort the three eigenvalues ​​of the gradient structure tensor matrix GST and get the largest eigenvalue; Based on the vector of the eigenvector corresponding to the maximum eigenvalue in the x direction, the vector in the y direction, and the vector in the t direction, a first inclination angle data volume is calculated.

8. The method for calculating prestack texture attributes guided by stratum structure according to claim 1, characterized in that: The step of constructing a stratum structure-oriented gray-level co-occurrence matrix G according to the first dip angle data volume comprises: Acquire new four-dimensional seismic data u″(x, y, t, d) guided by the stratigraphic structure from the pre-stack seismic data along the direction of the first dip data volume; Grayscale conversion is performed on the new four-dimensional seismic data u″(x, y, t, d) guided by the stratigraphic structure; Based on the grayscale-converted four-dimensional seismic data u″(x, y, t, d), a stratigraphic structure-oriented grayscale co-occurrence matrix G is constructed; Among them, the gray level co-occurrence matrix G is: represents the calculation direction of the gray-level co-occurrence matrix, G(i, j) represents the gray-level co-occurrence matrix, and i and j represent the positions in the gray-level co-occurrence matrix.

9. The method for calculating prestack texture attributes guided by stratum structure according to claim 8, characterized in that: The method of acquiring new four-dimensional seismic data u″(x, y, t, d) guided by the stratum structure from the pre-stack seismic data along the direction of the first dip angle data volume comprises: Constructing a third rotation matrix R(p′) and a fourth rotation matrix R(q′) according to the first tilt angle data volume; Determine a second calculation grid W″ based on the third rotation matrix R(p′) and the fourth rotation matrix R(q′); Based on the second computational grid W″, new four-dimensional seismic data u″(x, y, t, d) guided by the stratum structure are acquired in the pre-stack seismic data.

10. The method for calculating prestack texture attributes guided by stratum structure according to claim 8, characterized in that: The gray level co-occurrence matrix G based on the stratum structure orientation calculates the texture feature attribute, including: Based on the stratum structure-oriented gray-level co-occurrence matrix G, the statistical probability p of the element G(i,j) in the gray-level co-occurrence matrix G is obtained. ij ;in, Indicates the selected gray level; Based on the statistical probability p of the element G(i,j) in the gray-level co-occurrence matrix G ij , calculate texture feature attributes.

11. The stratigraphic structure-guided prestack texture attribute calculation method according to claim 10, characterized in that the texture feature attribute comprises an energy value; The energy value is calculated as follows: in, Energy indicates the energy value.

12. The method for calculating prestack texture attributes guided by stratum structure according to claim 10, characterized in that: The texture feature attributes include contrast; The calculation formula of the contrast ratio is: Among them, Contrast represents contrast.

13. The method for calculating prestack texture attributes guided by stratum structure according to claim 10, characterized in that: The texture feature attributes include homogeneity; The calculation formula of the homogeneity is: Among them, Homogeneity means homogeneity.

14. The method for calculating prestack texture attributes guided by stratum structure according to claim 10, characterized in that: The texture feature attribute includes an entropy value; The calculation formula of the entropy value is: Among them, Entropy represents the entropy value.

15. The method for calculating prestack texture attributes guided by stratum structure according to claim 10, characterized in that: The texture feature attributes include correlation; The calculation formula of the correlation is: in, Correlation means correlation.

16. A stratigraphic structure-guided prestack texture attribute calculation system, characterized in that: include: A preliminary dip data volume calculation module is used to collect four-dimensional seismic data u (x, y, t, d) and calculate the preliminary dip data volume corresponding to the four-dimensional seismic data u (x, y, t, d); A first dip data volume calculation module, used for constructing a formation structure-oriented gradient structure tensor matrix GST according to the preliminary dip data volume, and performing first dip data volume calculation based on the formation structure-oriented gradient structure tensor matrix GST; The texture feature attribute calculation module is used to construct a stratum structure-oriented grayscale co-occurrence matrix G according to the first dip angle data volume, and calculate the texture feature attributes based on the stratum structure-oriented grayscale co-occurrence matrix G to obtain the texture feature attribute calculation result.

17. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the stratigraphic structure-guided prestack texture attribute calculation method as claimed in any one of claims 1 to 15.

18. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for calculating prestack texture attributes guided by the formation structure as described in any one of claims 1 to 15 is implemented.

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