Diffracted wave texture attribute extraction method and device, electronic equipment and storage medium

Abstract

The invention discloses a diffracted wave texture attribute extraction method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a diffracted wave three-dimensional seismic data volume, an inclination angle volume and an azimuth angle volume of a full wave field based on a full wave field three-dimensional seismic data volume; performing graying processing on the diffracted wave three-dimensional seismic data volume to obtain a diffracted wave gray-scale three-dimensional image volume; under the constraint of the time slices of the inclination angle body and the azimuth angle body, scanning each diffracted wave gray scale image time slice of the diffracted wave gray scale three-dimensional image body at different sampling times to obtain a diffracted wave gray scale co-occurrence matrix of each diffracted wave gray scale image time slice, and obtaining a texture feature parameter corresponding to each gray scale image slice; combining the texture feature parameters of different diffracted wave grayscale image time slices into a three-dimensional diffracted wave texture attribute body; and normalizing the texture attribute body of the three-dimensional diffracted wave, and carrying out equal weight fusion to obtain a comprehensive texture attribute. According to the invention, the fracture-cavity attribute identification precision of the carbonate reservoir can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas geophysical exploration, and more specifically, relates to a diffraction wave texture attribute extraction method, device, electronic equipment and storage medium. Background Art

[0002] The fracture-cavity bodies developed in the Ordovician carbonate reservoirs in the Tarim Basin are important oil and gas storage spaces. When seismic waves propagate underground, they will reflect and transmit when they pass through the interface between the Ordovician and Carboniferous strata, generating reflection waves, while diffraction will occur at the fracture-cavity bodies to generate diffraction waves. On the full-wavefield seismic imaging profile, the interface between the large strata shows the characteristics of strong energy reflection waves, while the fracture-cavity bodies show the characteristics of weak energy diffraction waves.

[0003] If the 3D seismic data volume is regarded as a 3D image data volume, the texture features of the image can be used to identify cracks and holes. Since the texture is formed by the repeated grayscale distribution in the image space, the grayscale in the image has a spatial correlation characteristic, and the grayscale co-occurrence matrix describes the texture distribution characteristics based on this characteristic.

[0004] Conventional full-wavefield seismic texture attributes based on gray-level co-occurrence matrix are shielded by strong energy reflection phase axes at the stratum interface, and have insufficient recognition ability for fracture-cavity bodies, which cannot meet the current problem of qualitative recognition of fractures and caves in heterogeneous reservoirs. The main reason is that the full-wavefield data contains strong energy reflection wave information, which is the seismic response of layered strata. Strong energy layered phase axes will cover up the diffraction wave characteristics caused by heterogeneous geological anomalies, and have insufficient recognition ability for fracture-cavity bodies near strong energy layered phase axes. Summary of the invention

[0005] The purpose of the present invention is to provide a diffraction wave texture attribute extraction method, device, electronic equipment and storage medium to improve the fracture and cave attribute recognition accuracy of carbonate reservoirs.

[0006] To achieve the above object, in a first aspect, the present invention proposes a diffraction wave texture attribute extraction method, comprising:

[0007] Based on the full-wavefield three-dimensional seismic data volume, the diffraction wave three-dimensional seismic data volume, the dip volume and the azimuth volume of the full-wavefield are obtained;

[0008] grayscale processing is performed on the diffraction wave three-dimensional seismic data volume to obtain a diffraction wave grayscale three-dimensional image volume;

[0009] Under the constraints of the time slices of the inclination body and the azimuth body, scanning and processing are performed on each diffraction wave grayscale image time slice of different sampling times of the diffraction wave grayscale three-dimensional image body to obtain the diffraction wave grayscale co-occurrence matrix of each diffraction wave grayscale image time slice, and the texture feature parameters corresponding to each diffraction wave grayscale image time slice are obtained;

[0010] The texture characteristic parameters of different diffraction wave grayscale image time slices are combined into a three-dimensional diffraction wave texture attribute body;

[0011] The three-dimensional diffraction wave texture attribute body is normalized and fused with equal weights to obtain a comprehensive texture attribute.

[0012] Optionally, after obtaining the comprehensive texture attribute, the method further includes:

[0013] The comprehensive texture attributes are used to predict the fracture and cave development characteristics of carbonate reservoirs.

[0014] Optionally, acquiring a diffraction wave three-dimensional seismic data volume of the full wavefield based on the full wavefield three-dimensional seismic data volume includes:

[0015] The diffraction wave separation is performed on the full-wavefield three-dimensional seismic data volume to obtain the diffraction wave three-dimensional seismic data volume.

[0016] Optionally, obtaining a dip volume and an azimuth volume of the full wavefield based on the full wavefield three-dimensional seismic data volume includes:

[0017] The dip angle and azimuth angle of the full-wavefield three-dimensional seismic data volume are obtained by using a differential method to obtain a dip angle volume and an azimuth angle volume of the full-wavefield.

[0018] Optionally, under the constraints of the time slices of the inclination body and the azimuth body, scanning and processing are performed on each diffraction wave grayscale image time slice of different sampling times of the diffraction wave grayscale three-dimensional image body to obtain the diffraction wave grayscale co-occurrence matrix of each diffraction wave grayscale image time slice, including:

[0019] S01: extracting time slices according to sampling time to obtain time slices of the inclination body and the azimuth body, and compressing horizontal slices of the diffraction wave grayscale three-dimensional image body according to a set grayscale level to obtain diffraction wave grayscale image time slices;

[0020] S02: setting a scanning window on the diffraction wave grayscale image time slice to obtain the full wave field inclination and azimuth corresponding to the central pixel point; selecting any point on the diffraction wave grayscale image time slice in the scanning window and another point deviating from the selected point, forming a point pair from the two points, and obtaining the grayscale value of the point pair;

[0021] S03: Move the selected point in the scanning window to obtain a combination of multiple grayscale values, count the number of pixel pairs of inclination, azimuth, and grayscale value, and arrange them to form a diffraction wave grayscale co-occurrence matrix, so as to convert the spatial coordinates into a description of the grayscale value.

[0022] Optionally, the step of obtaining texture feature parameters corresponding to each diffraction wave grayscale image time slice includes:

[0023] Based on the gray level co-occurrence matrix, calculating the texture feature parameters corresponding to the central pixel;

[0024] The scanning window is moved on the diffraction wave grayscale image time slice, and steps S01-S03 are repeated to obtain the texture feature parameters of each central pixel point in the window, and the texture feature parameters corresponding to the diffraction wave grayscale image time slice are obtained by arranging them in rows and columns.

[0025] Optionally, the texture feature parameters include contrast, energy, entropy and uniformity.

[0026] In a second aspect, the present invention provides a diffraction wave texture attribute extraction device, comprising:

[0027] A data decomposition module is used to obtain a diffraction wave three-dimensional seismic data volume, a dip volume and an azimuth volume of the full wave field based on the full wave field three-dimensional seismic data volume;

[0028] A grayscale processing module, used for performing grayscale processing on the diffraction wave three-dimensional seismic data volume to obtain a diffraction wave grayscale three-dimensional image volume;

[0029] A texture feature calculation module is used to scan and process each diffraction wave grayscale image time slice of different sampling times of the diffraction wave grayscale three-dimensional image volume under the constraints of the time slices of the inclination body and the azimuth body, obtain the diffraction wave grayscale co-occurrence matrix of each diffraction wave grayscale image time slice, and obtain the texture feature parameters corresponding to each diffraction wave grayscale image time slice;

[0030] A texture attribute volume calculation module is used to combine texture feature parameters of different diffraction wave grayscale image time slices into a three-dimensional diffraction wave texture attribute volume;

[0031] The comprehensive texture attribute calculation module is used to normalize the three-dimensional diffraction wave texture attribute volume and obtain the comprehensive texture attribute by equal weight fusion.

[0032] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0033] at least one processor; and,

[0034] a memory communicatively connected to the at least one processor; wherein,

[0035] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the diffraction wave texture attribute extraction method described in any one of the first aspects.

[0036] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which stores computer instructions for causing a computer to execute any of the diffraction wave texture attribute extraction methods described in the first aspect.

[0037] The beneficial effects of the present invention are:

[0038] The method of the present invention first calculates the dip volume and azimuth volume corresponding to the full-wavefield seismic data, grays the diffraction wave data to obtain a three-dimensional gray image volume, and then scans and processes the time slices of each diffraction wave gray image volume under the constraints of the dip volume and azimuth volume time slices of the full-wavefield data to obtain a diffraction wave gray co-occurrence matrix corresponding to the diffraction wave gray image volume at each sampling time, and then obtains the diffraction wave texture feature parameters corresponding to each diffraction wave gray image volume, and finally forms a three-dimensional diffraction wave texture attribute volume with the diffraction wave texture feature parameters of all sampling times, thereby accurately predicting the development characteristics of carbonate fracture-vuggy reservoirs and improving the fracture-vuggy recognition accuracy of carbonate reservoirs.

[0039] The system of the present invention has other characteristics and advantages, which will be apparent from the drawings incorporated herein and the following detailed description, or will be described in detail in the drawings incorporated herein and the following detailed description, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, in which like reference numerals generally represent like components.

[0041] Figure 1 A step diagram of a diffraction wave texture attribute extraction method of the present invention is shown.

[0042] Figure 2 A flow chart of a diffraction wave texture attribute extraction method of Example 3 is shown.

[0043] Figure 3a The time domain full-wavefield seismic profile in Example 3 is shown.

[0044] Figure 3b The time domain diffraction wave seismic profile in Example 3 is shown.

[0045] Figure 4a The time domain diffraction wave texture feature contrast attribute plane in Example 3 is shown.

[0046] Figure 4b The time domain diffraction wave texture characteristic energy attribute plane in Example 3 is shown.

[0047] Figure 4c The time domain diffraction wave texture feature entropy attribute plane in Example 3 is shown.

[0048] Figure 4d The time domain diffraction wave texture feature uniformity attribute plane in Example 3 is shown.

[0049] Figure 5 The time domain diffraction wave comprehensive texture attribute plane in Example 3 is shown.

[0050] Figure 6a The time domain full wavefield texture feature entropy attribute profile in Example 3 is shown.

[0051] Figure 6b The time domain diffraction wave texture characteristic entropy attribute profile in Example 3 is shown. DETAILED DESCRIPTION

[0052] Conventional full-wavefield seismic texture attributes based on gray-level co-occurrence matrix cannot meet the current problem of qualitative identification of fractures and caves in heterogeneous reservoirs.

[0053] It has been found that diffraction wave data only contain seismic responses caused by heterogeneous geological anomalies. Therefore, by using diffraction waves to extract seismic attributes, attribute information that can reflect the heterogeneous characteristics of fracture-vuggy reservoirs can be obtained.

[0054] Aiming at the shortcomings of conventional full-wavefield grayscale co-occurrence matrix seismic texture attributes, the present invention combines the imaging advantages of diffraction wave data for fracture-cavity reservoirs and proposes a diffraction wave texture attribute extraction method, device, electronic device and storage medium. Based on the inclination and azimuth information of full-wavefield seismic data, the present invention constrains the establishment process of the diffraction wave grayscale co-occurrence matrix, obtains the diffraction wave texture characteristic parameters, and forms a diffraction wave texture attribute extraction method, thereby improving the fracture-cavity attribute recognition accuracy of carbonate reservoirs.

[0055] The present invention will be described in more detail below with reference to the accompanying drawings. Although preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0056] Example 1

[0057] like Figure 1 As shown, this embodiment provides a diffraction wave texture attribute extraction method, including:

[0058] S1: Based on the full-wavefield 3D seismic data volume, the diffraction wave 3D seismic data volume, the dip volume and the azimuth volume of the full-wavefield are obtained;

[0059] In this step, obtaining a diffraction wave three-dimensional seismic data volume of the full wave field based on the full wave field three-dimensional seismic data volume includes:

[0060] The diffraction wave separation is performed on the full-wavefield three-dimensional seismic data volume to obtain the diffraction wave three-dimensional seismic data volume.

[0061] The dip volume and azimuth volume of the full wave field are obtained based on the full wave field 3D seismic data volume, including:

[0062] The difference method is used to obtain the dip and azimuth of the full-wavefield three-dimensional seismic data volume, and the dip volume and azimuth volume of the full-wavefield are obtained.

[0063] S2: grayscale the diffraction wave three-dimensional seismic data volume to obtain a diffraction wave grayscale three-dimensional image volume;

[0064] S3: Under the constraints of the time slices of the inclination body and the azimuth body, each diffraction wave grayscale image time slice of the diffraction wave grayscale three-dimensional image body with different sampling times is scanned and processed to obtain the diffraction wave grayscale co-occurrence matrix of each diffraction wave grayscale image time slice, and obtain the texture feature parameters corresponding to each diffraction wave grayscale image time slice;

[0065] This step specifically includes:

[0066] S201: extracting time slices according to the sampling time to obtain time slices of the inclination body and the azimuth body, and compressing the horizontal slices of the diffraction wave grayscale three-dimensional image body according to the set grayscale level to obtain the diffraction wave grayscale image time slices;

[0067] S202: setting a scanning window on the diffraction wave grayscale image time slice, obtaining the full wave field inclination and azimuth corresponding to the central pixel point; selecting any point on the diffraction wave grayscale image time slice in the scanning window and another point deviating from the selected point, forming a point pair from the two points, and obtaining the grayscale value of the point pair;

[0068] S203: Move the selected point in the scanning window to obtain a combination of multiple grayscale values, count the number of pixel pairs of inclination, azimuth, and grayscale value, and arrange them to form a diffraction wave grayscale co-occurrence matrix, thereby converting the spatial coordinates into a description of the grayscale value.

[0069] S204: Calculate texture feature parameters corresponding to the central pixel based on the gray level co-occurrence matrix; the texture feature parameters include contrast, energy, entropy and uniformity.

[0070] S205: Move the scanning window on the diffraction wave grayscale image time slice, repeat steps S201-S203, obtain texture feature parameters of each central pixel in the window, and arrange them in rows and columns to obtain texture feature parameters corresponding to the diffraction wave grayscale image time slice.

[0071] S4: combining texture feature parameters of different diffraction wave grayscale image time slices into a three-dimensional diffraction wave texture attribute volume;

[0072] S5: Normalize the three-dimensional diffraction wave texture attribute volume and fuse them with equal weights to obtain the comprehensive texture attribute.

[0073] In this embodiment, after obtaining the comprehensive texture attribute in step S5, the following steps are further included:

[0074] The comprehensive texture attributes are used to predict the fracture and cavity development characteristics of carbonate reservoirs.

[0075] Example 2

[0076] This embodiment provides a diffraction wave texture attribute extraction method, which uses the inclination and azimuth information of the full wave field data to constrain the extraction of the diffraction wave texture attribute. The method includes three processes: full wave field inclination azimuth calculation, diffraction wave grayscale co-occurrence matrix establishment, and diffraction wave texture attribute body extraction. The diffraction wave grayscale co-occurrence matrix establishment process is used to realize the function of extracting diffraction wave texture feature parameters under the constraints of the full wave field inclination azimuth.

[0077] The method of this embodiment includes the following steps:

[0078] (1) performing diffraction wave separation on the full-wavefield three-dimensional seismic data volume to obtain a diffraction wave three-dimensional seismic data volume;

[0079] In one example, a full-wavefield three-dimensional seismic data volume S (x, y, t) of time length T is subjected to diffraction wave separation to obtain a diffraction wave three-dimensional seismic data volume D (x, y, t), where t = 0, 1, 2, ..., T.

[0080] (2) The dip and azimuth of the full-wavefield seismic data volume are calculated using the differential method to obtain the dip volume and azimuth volume.

[0081] In one example, the difference method is used to obtain the dip and azimuth of the full-wave field seismic data volume S (x, y, t), and obtain the dip volume DIP and the azimuth volume AZI. The principle is to perform central difference format operations on the point (x, y, t) of the three-dimensional spatial coordinate position of the seismic data volume and its adjacent points in the three directions of x, y, and t, and then obtain the differential values ​​of the three directions at the point.

[0082] Δx=(S(x+1,y,t)-S(x-1,y,t)) / 2h x

[0083] Δy=(S(x,y+1,t)-S(x,y-1,t)) / 2h y

[0084] Δt=(S(x,y,t+1)-S(x,y,t-1)) / 2h t

[0085] Then the inclination angle DIP(x,y,t) and azimuth angle AZI(x,y,t) at this point can be expressed as

[0086]

[0087] AZI(x,y,t)=arctan(Δt / Δy) / (Δt / Δx)

[0088] where h x and h y are the surface element spacings of the observation system in the x and y directions, in m; h t It is the time domain sampling interval, in s or ms.

[0089] (3) graying the diffraction wave seismic data volume to obtain a diffraction wave grayscale three-dimensional image volume;

[0090] In one example, the diffraction wave seismic data D(x, y, t) volume is grayed to obtain a grayscale three-dimensional image volume G(x, y, t). The graying process is expressed as:

[0091]

[0092] Among them, D min is the minimum amplitude of the diffraction wave, D max is the maximum amplitude of the diffraction wave, and INT(·) represents the rounding operation.

[0093] (4) extracting time slices according to the sampling time to obtain time slices of the full wave field inclination volume and azimuth volume, and compressing the horizontal slices of the diffraction wave grayscale image volume according to the grayscale level;

[0094] In one example, time slices are extracted according to the sampling time. For the sampling time t, the time slices of the full wave field dip volume and azimuth volume are represented as DIP t AZI t ; Compress the time slice of the diffraction wave grayscale image volume to N levels (the grayscale level N is generally 8 or 16), and the obtained grayscale image is G t Indicates that each pixel in the image corresponds to a grayscale value.

[0095] (5) A scanning window is set on the grayscale image time slice, and the full wave field inclination and azimuth corresponding to the central pixel point can be obtained; any point on the diffraction wave grayscale image time slice in the window and another point deviating from it are selected, and these two points form a point pair, and the grayscale value of the point pair is obtained;

[0096] In one example, in G t A scanning window W is set on the top, whose length and width are N, and the full wave field inclination and azimuth corresponding to the central pixel point are DIP W AZI W ; For the diffraction wave grayscale image G at sampling time t t For any point (x, y) in the window W and another point (x′, y′) deviating from it, the grayscale value of the point pair formed by these two points is (i, j), where i = 1, 2, …, N and j == 1, 2, …, N.

[0097] (6) By moving the point in the window of the grayscale image time slice, a variety of grayscale value combinations can be obtained, and the number of pixel pairs of inclination, azimuth, and grayscale value can be counted, and then arranged to form a grayscale co-occurrence matrix, thereby converting the spatial coordinates into a description of grayscale values;

[0098] In one example, let the point (x, y) move in the window W, and we can get a total of N 2 For the time slice of sampling time t, the inclination angle is DIP W , azimuth is AZI W , the number of pixel pairs with grayscale value (i, j), and then arrange them to form an N×N grayscale co-occurrence matrix L W , thus converting the spatial coordinates of (x, y) into the description of grayscale values ​​(i, j). The grayscale co-occurrence matrix of seismic data can reflect the comprehensive information of the grayscale of seismic images on direction, adjacent intervals, and change amplitude, and is the basis for analyzing the local patterns of images and their arrangement rules.

[0099] (7) Based on the grayscale co-occurrence matrix of the grayscale image time slice window, the texture feature parameters corresponding to the central pixel are calculated, including contrast CON, energy ENG, entropy ENT, and uniformity HOM;

[0100] In one example, the gray level co-occurrence matrix L based on the window W W , calculate the texture feature parameter P corresponding to the central pixel W The P W Including contrast CON, energy ENG, entropy ENT, and uniformity HOM, which are obtained using the following formulas:

[0101] Contrast CON:

[0102]

[0103] Energy ENG:

[0104]

[0105] Entropy ENT:

[0106]

[0107] Homogeneity HOM:

[0108]

[0109] (8) Move the scanning window on the grayscale image time slice, repeat steps (5)(6)(7), obtain the texture feature parameters of each pixel in the window, arrange them in rows and columns to obtain the texture feature parameters of the sampling time, and then combine the texture feature parameters of different sampling times into a three-dimensional diffraction wave texture attribute body.

[0110] In one example, in G t Move the window W upward and repeat steps (5), (6), (7) to obtain the texture feature parameter P of each pixel in the window W. W , Arrange in rows and columns to get the sampling time G t The texture feature parameter P t , and then P with different sampling times t Combined into a three-dimensional diffraction wave texture attribute volume P D .

[0111] (9) The four texture feature attributes of contrast CON, energy ENG, entropy ENT, and uniformity HOM are normalized and fused with equal weights to obtain the comprehensive texture attribute P. The comprehensive texture attribute P is used to predict the fracture and cave development characteristics of carbonate reservoirs.

[0112] In one example, the three-dimensional diffraction wave texture attribute volume P D The four texture feature attributes (including contrast CON, energy ENG, entropy ENT, and uniformity HOM) are normalized and fused with equal weights to obtain the comprehensive texture attribute P, which is used to predict the fracture and cave development characteristics of carbonate reservoirs.

[0113] Example 3

[0114] This embodiment provides a diffraction wave texture attribute extraction method, the specific process is as follows Figure 2 As shown, the following steps are included:

[0115] Step (1) performs diffraction wave separation on the full-wavefield three-dimensional seismic data volume to obtain a diffraction wave three-dimensional seismic data volume. Figure 3aThe S profile of the full-wavefield three-dimensional seismic data body with a time domain range of 3s-4s and a duration of T=1s is displayed. From the profile, it can be seen that within the time range of 3.4s-3.8s, the fracture-cave geological anomaly in the carbonate reservoir presents a "beaded" diffraction wave response characteristic, with strong heterogeneity. Figure 3b The cross-section of the diffraction wave data D obtained using the relevant mature diffraction wave separation method is shown. The diffraction wave has high resolution and rich detail information, which can better characterize the detailed characteristics of the fracture-cavity development.

[0116] Step (2), using the difference method to obtain the inclination and azimuth of the full-wavefield seismic data body S, and obtain the inclination body DIP and the azimuth body AZI.

[0117] Step (3), graying the diffraction wave seismic data volume D to obtain a diffraction wave grayscale three-dimensional image volume G.

[0118] Step (4), extract the time slice according to the sampling time t = 3s to obtain the time slice DIP of the full wave field inclination volume and azimuth volume t AZI t , and the horizontal slice G of the diffraction wave grayscale image volume t (Compressed according to the grayscale number N=16);

[0119] Step (5), in the grayscale image time slice G t A scanning window W is set on the image, and the full wave field inclination angle DIP corresponding to the central pixel point can be obtained. W AZI W ; Select any point (x, y) on the time slice of the diffraction wave grayscale image in the window and another point (x′, y′) deviating from it, these two points form a point pair, and obtain the grayscale value (i, j) of the point pair;

[0120] Step (6), let the point (x, y) be in the grayscale image time slice G t Move in the window W, and you can get N 2 = 256 combinations of grayscale values, count the number of pixel pairs of inclination, azimuth, and grayscale value, and then arrange them to form an N×N grayscale co-occurrence matrix L W , thereby converting the spatial coordinates into a description of grayscale values;

[0121] Step (7), based on the gray-level co-occurrence matrix L of the gray-level image time slice window W W , calculate the texture feature parameter P corresponding to the central pixel W , including contrast CON, energy ENG, entropy ENT, and uniformity HOM.

[0122] Step (8), on the grayscale image time slice G tMove the scanning window and repeat steps (5), (6), (7) to obtain the texture feature parameters of each pixel in the window, arrange them in rows and columns to obtain the texture feature parameters of the sampling time, and then combine the texture feature parameters of different sampling times (time range from t = 3s to t = 4s) into a three-dimensional diffraction wave texture attribute body. The diffraction wave texture attribute bodies are respectively as follows: Figures 4a to 4d As shown. The contrast reflects the clarity of the image and the depth of the texture grooves. The deeper the texture grooves, the larger the scale of the cracks and holes, and the greater the contrast. On the contrary, the smaller the contrast, the shallower the grooves and the blurred boundaries of the cracks and holes. Energy is the sum of the squares of the element values ​​of the grayscale co-occurrence matrix, which reflects the uniformity of the grayscale distribution of the image and the coarseness of the texture. The larger the value, the higher the clutter of the image and the greater the degree of crack development. Entropy indicates the complexity of the grayscale distribution of the image. The larger the entropy value, the clearer the texture and the more complex the image. Uniformity indicates the local grayscale correlation in the image. This characteristic parameter is exactly the opposite of the contrast parameter.

[0123] Step (9), normalize the four texture feature attributes of contrast CON, energy ENG, entropy ENT, and uniformity HOM, and fuse them with equal weights to obtain the comprehensive texture attribute P. The comprehensive texture attribute P is used to predict the fracture and cave development characteristics of carbonate reservoirs, as shown in Figure 5 Through analysis and comparison, the gray-level co-occurrence matrix diffraction wave texture attribute extraction method based on full wave field constraints in this paper can better depict the contours of the fracture-cavity bodies of carbonate reservoirs in the Tarim Basin in detail, as well as the distribution law on the plane, which is more consistent with the actual drilling.

[0124] In order to compare the superiority of the method of the present invention, Figure 3a and Figure 3b The full wave field data and diffraction wave data shown in the figure are used to extract texture attributes. The texture feature entropy attribute profile is shown in Figure 6a and Figure 6b shown. Figure 6a In the full wave field texture feature entropy attribute, the layered feature is obvious, and the small-scale fracture and cave body is shielded by the strong energy layered reflection; Figure 6b The characteristic entropy attribute of the medium diffraction wave texture effectively removes the strong energy reflection interface and the layered reflection phase axis of the underlying strata, highlights the weak energy diffraction wave information, and well characterizes the small-scale fracture and cave bodies, indicating that the method of the present invention is more suitable for the identification of fracture and cave bodies in carbonate reservoirs than the conventional full-wavefield texture attribute method.

[0125] This method is based on the dip and azimuth information of full-wavefield seismic data, constrains the establishment process of the diffraction wave grayscale co-occurrence matrix, obtains the diffraction wave texture characteristic parameters, and forms a diffraction wave texture attribute extraction method, which improves the accuracy of fracture and cave attribute recognition in carbonate reservoirs and provides strong data support for oil and gas geophysical exploration of heterogeneous geological anomalies in the study area.

[0126] Example 4

[0127] This embodiment provides a diffraction wave texture attribute extraction device, including:

[0128] A data decomposition module is used to obtain a diffraction wave three-dimensional seismic data volume, a dip volume and an azimuth volume of the full wave field based on the full wave field three-dimensional seismic data volume;

[0129] A grayscale processing module, used for performing grayscale processing on the diffraction wave three-dimensional seismic data volume to obtain a diffraction wave grayscale three-dimensional image volume;

[0130] A texture feature calculation module is used to scan and process each diffraction wave grayscale image time slice of different sampling times of the diffraction wave grayscale three-dimensional image volume under the constraints of the time slices of the inclination body and the azimuth body, obtain the diffraction wave grayscale co-occurrence matrix of each diffraction wave grayscale image time slice, and obtain the texture feature parameters corresponding to each diffraction wave grayscale image time slice;

[0131] A texture attribute volume calculation module is used to combine texture feature parameters of different diffraction wave grayscale image time slices into a three-dimensional diffraction wave texture attribute volume;

[0132] The comprehensive texture attribute calculation module is used to normalize the three-dimensional diffraction wave texture attribute volume and obtain the comprehensive texture attribute by equal weight fusion.

[0133] Example 5

[0134] This embodiment provides an electronic device, the electronic device comprising:

[0135] at least one processor; and,

[0136] a memory communicatively connected to the at least one processor; wherein,

[0137] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the diffraction wave texture attribute extraction method described in any of the above embodiments.

[0138] Example 6

[0139] This embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the diffraction wave texture attribute extraction method described in any of the above embodiments.

[0140] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A diffraction wave texture attribute extraction method, It is characterized in that include: Based on the full-wavefield three-dimensional seismic data volume, the diffraction wave three-dimensional seismic data volume, the dip volume and the azimuth volume of the full-wavefield are obtained; grayscale processing is performed on the diffraction wave three-dimensional seismic data volume to obtain a diffraction wave grayscale three-dimensional image volume; Under the constraints of the time slices of the inclination body and the azimuth body, scanning and processing are performed on each diffraction wave grayscale image time slice of different sampling times of the diffraction wave grayscale three-dimensional image body to obtain the diffraction wave grayscale co-occurrence matrix of each diffraction wave grayscale image time slice, and the texture feature parameters corresponding to each diffraction wave grayscale image time slice are obtained; The texture characteristic parameters of different diffraction wave grayscale image time slices are combined into a three-dimensional diffraction wave texture attribute body; The three-dimensional diffraction wave texture attribute body is normalized and fused with equal weights to obtain a comprehensive texture attribute.

2. The diffraction wave texture attribute extraction method according to claim 1, It is characterized in that After obtaining the comprehensive texture attributes, it also includes: The comprehensive texture attributes are used to predict the fracture and cave development characteristics of carbonate reservoirs.

3. The diffraction wave texture attribute extraction method according to claim 1, It is characterized in that The full-wavefield diffraction wave 3D seismic data volume is obtained based on the full-wavefield 3D seismic data volume, including: The diffraction wave separation is performed on the full-wavefield three-dimensional seismic data volume to obtain the diffraction wave three-dimensional seismic data volume.

4. The diffraction wave texture attribute extraction method according to claim 1, It is characterized in that The dip volume and azimuth volume of the full wave field are obtained based on the full wave field 3D seismic data volume, including: The dip angle and azimuth angle of the full-wavefield three-dimensional seismic data volume are obtained by using a differential method to obtain a dip angle volume and an azimuth angle volume of the full-wavefield.

5. The diffraction wave texture attribute extraction method according to claim 1, It is characterized in that Under the constraints of the time slices of the inclination body and the azimuth body, scanning and processing are performed on each diffraction wave grayscale image time slice of different sampling times of the diffraction wave grayscale three-dimensional image body to obtain the diffraction wave grayscale co-occurrence matrix of each diffraction wave grayscale image time slice, including: S01: extracting time slices according to sampling time to obtain time slices of the inclination body and the azimuth body, and compressing horizontal slices of the diffraction wave grayscale three-dimensional image body according to a set grayscale level to obtain diffraction wave grayscale image time slices; S02: setting a scanning window on the diffraction wave grayscale image time slice to obtain the full wave field inclination and azimuth corresponding to the central pixel point; selecting any point on the diffraction wave grayscale image time slice in the scanning window and another point deviating from the selected point, forming a point pair from the two points, and obtaining the grayscale value of the point pair; S03: Move the selected point in the scanning window to obtain a combination of multiple grayscale values, count the number of pixel pairs of inclination, azimuth, and grayscale value, and arrange them to form a diffraction wave grayscale co-occurrence matrix, so as to convert the spatial coordinates into a description of the grayscale value.

6. The diffraction wave texture attribute extraction method according to claim 5, It is characterized in that The step of obtaining the texture feature parameters corresponding to each diffraction wave grayscale image time slice comprises: Based on the gray level co-occurrence matrix, calculating the texture feature parameters corresponding to the central pixel; The scanning window is moved on the diffraction wave grayscale image time slice, and steps S01-S03 are repeated to obtain the texture feature parameters of each central pixel point in the window, and the texture feature parameters corresponding to the diffraction wave grayscale image time slice are obtained by arranging them in rows and columns.

7. The diffraction wave texture attribute extraction method according to claim 1, It is characterized in that The texture feature parameters include contrast, energy, entropy and uniformity.

8. A diffraction wave texture attribute extraction device, It is characterized in that include: A data decomposition module is used to obtain a diffraction wave three-dimensional seismic data volume, a dip volume and an azimuth volume of the full wave field based on the full wave field three-dimensional seismic data volume; A grayscale processing module, used for performing grayscale processing on the diffraction wave three-dimensional seismic data volume to obtain a diffraction wave grayscale three-dimensional image volume; A texture feature calculation module is used to scan and process each diffraction wave grayscale image time slice of different sampling times of the diffraction wave grayscale three-dimensional image volume under the constraints of the time slices of the inclination body and the azimuth body, obtain the diffraction wave grayscale co-occurrence matrix of each diffraction wave grayscale image time slice, and obtain the texture feature parameters corresponding to each diffraction wave grayscale image time slice; A texture attribute volume calculation module is used to combine texture feature parameters of different diffraction wave grayscale image time slices into a three-dimensional diffraction wave texture attribute volume; The comprehensive texture attribute calculation module is used to normalize the three-dimensional diffraction wave texture attribute volume and obtain the comprehensive texture attribute by equal weight fusion.

9. An electronic device, It is characterized in that The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the diffraction wave texture attribute extraction method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, It is characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the diffraction wave texture attribute extraction method described in any one of claims 1-7.

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