Sand body configuration boundary intelligent extraction method based on seismic data

Abstract

The invention provides a sand body configuration boundary intelligent extraction method based on seismic data, and the method comprises the steps: collecting the seismic data of a research region, and obtaining the sand body thickness seismic attribute prediction data of the research region; performing morphological reconstruction top-hat transformation on the seismic attribute prediction data to obtain attribute data after morphological reconstruction; performing morphological gradient calculation on the reconstructed attribute data according to a designed morphological gradient operator to obtain a discontinuous boundary of the sand body in the research area; complexity parameter calculation is carried out on the extracted boundary, the irregularity degree of the discontinuous boundary is quantitatively represented according to the proposed complexity parameter, and the area division number of each closed contour is determined; and feature point screening is carried out according to the curvature of the internal boundary of the area, an expansion table is constructed, then closed contour matching is carried out, and the sand body configuration boundary of the research area is identified. According to the method, the boundary information of the sand body and the characteristics of the lithologic trap can be more accurately captured.

Description

Technical Field

[0001] The invention belongs to the field of geology, and in particular relates to a method for intelligently extracting sand body configuration boundaries based on seismic data. Background Art

[0002] In the field of oil and gas exploration, the identification of lithologic traps and discontinuous boundaries of sand bodies is the key to finding hidden oil and gas reservoirs. Traditionally, seismic interpretation technology and geological modeling and simulation are the main means to identify these geological features, but due to the limitations of seismic data resolution and the complexity of geological conditions, these methods often fail to meet the requirements of high-precision and high-efficiency identification. Especially in sedimentary basins, the formation of discontinuous boundaries of sand bodies is affected by many factors, such as river diversion, changes in sedimentary environment, and later diagenesis, which further increases the difficulty of identification. Summary of the invention

[0003] The purpose of the present invention is to provide a method for intelligently extracting sand body configuration boundaries based on seismic data.

[0004] The technical solution of the present invention is as follows:

[0005] A method for intelligently extracting sand body configuration boundaries based on seismic data, the method comprising:

[0006] Collect seismic data of the study area and obtain seismic attribute prediction data of sand body thickness in the study area;

[0007] Perform morphological reconstruction top-hat transformation on the earthquake attribute prediction data to obtain the attribute data after morphological reconstruction;

[0008] The morphological gradient operator designed is used to calculate the morphological gradient of the reconstructed attribute data to obtain the discontinuous boundary of the sand body in the study area.

[0009] The complexity parameters of the extracted boundaries are calculated, and the irregularity of the discontinuous boundaries is quantitatively characterized according to the proposed complexity parameters, and the number of regional divisions of each closed contour is determined;

[0010] Feature points are screened according to the curvature of the internal boundary of the region, an extended table is constructed, and then closed contour matching is performed to identify the sand body configuration boundary of the study area.

[0011] Furthermore, the morphological reconstruction top-hat transformation is performed on the earthquake attribute prediction data to obtain the attribute data after morphological reconstruction, which is specifically:

[0012] The morphological opening operation is used to process the earthquake attribute prediction data and obtain the opening operation result;

[0013] Subtract the opening operation result from the earthquake attribute prediction data to obtain the top hat transformation result, and use the minimum operation to compare it with the earthquake attribute prediction data to obtain a labeled data;

[0014] Perform morphological dilation on the labeled data to obtain the reconstructed data. If the labeled data is not equal to the reconstructed data, continue to iterate the dilation until the two are equal:

[0015] The reconstruction result is obtained by subtracting the reconstruction data from the earthquake attribute prediction data.

[0016] Furthermore, the morphological gradient operator designed is used to calculate the morphological gradient of the reconstructed attribute data to obtain the discontinuous boundary of the sand body in the study area:

[0017] Design morphological gradient operators in specific directions;

[0018] The designed gradient operator is used to accurately identify and extract the boundaries of the sand body through morphological reconstruction data.

[0019] Furthermore, the morphological gradient operator in the specific direction includes:

[0020] There are 16 directions of morphological gradient operators, including eight directions of 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5° and their corresponding gradient operators in opposite directions.

[0021] Furthermore, the adaptive boundary detection algorithm is specifically as follows:

[0022] Define ω1, ω2, ω3, ω4, ω5, ω6, ω7, and ω8 as the weights of the structural operators in the directions of 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5° respectively; define the weights as ω i is the weight of each direction, D i is the amplitude in each direction, and D is the sum of the amplitudes in each direction; the calculated weights are assigned to the detection boundaries in the corresponding directions to obtain the adaptive detection algorithm for discontinuous boundaries of layer seismic attributes in, k=1,2,3,4; b1 is a cross-shaped structural element; b2 is a rectangular structural element; b 3k Structural elements in different directions.

[0023] Furthermore, the calculation of the complexity parameter of the extracted boundary is specifically as follows:

[0024] Calculate the boundary complexity of each extracted boundary contour;

[0025] According to the complexity distribution range of the extracted boundary, the number of region divisions of the boundary is determined.

[0026] Furthermore, the formula for calculating the boundary complexity of each extracted boundary contour is:

[0027]

[0028] C represents the boundary complexity of the contour; L R , L E Respectively represent the perimeter of the minimum circumscribed rectangle and the perimeter of the irregular boundary; A E , A R are the area of ​​the irregular boundary and the area of ​​the minimum enclosing rectangle respectively.

[0029] When the number of divided regions is large, the number of calculations will increase and the detection time will be increased. The number of divided regions is determined according to the complexity distribution of all boundaries. The more regular the boundaries are, the fewer feature points the image has, so the image can be divided into fewer regions.

[0030] Furthermore, feature points are screened according to the curvature of the internal boundary of the region, an extended table is constructed, and then closed contour matching is performed to identify the sand body configuration boundary of the study area as follows:

[0031] The boundary is divided into several regions, and the feature points with larger curvature in each region are selected to construct the fast random generalized Hough transform extension C;

[0032] Detect irregular closed graphics based on the constructed extended C table and filter out fragmented and unclosed boundaries:

[0033] When creating the extension C, ensure that the selected points come from different regions than those divided, and give priority to points with larger curvature in each region;

[0034] Based on the improved fast randomized generalized Hough transform: Through the quantitative evaluation of boundary complexity, the detected boundary is divided into several regions, and the feature points with larger curvature in each region are preferentially selected to construct the extended C table for query matching. On the basis of ensuring the overall shape of the boundary contour, the local features of the boundary are highlighted, and the accuracy of the closed boundary detection results is improved. The detected closed figures are matched with closed contours to identify the sand body configuration boundary of the study area.

[0035] Compared with the prior art, the present invention has the following advantages:

[0036] The method of the present invention combines multiple technologies such as morphological reconstruction, adaptive boundary detection, boundary complexity calculation and fast random generalized Hough transform to improve the accuracy and efficiency of identification. By collecting and processing high-quality seismic data, the method can more accurately capture the boundary information of sand bodies and the characteristics of lithological traps. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings generally illustrate various embodiments by way of example and not limitation, and together with the description and claims, serve to illustrate the embodiments of the invention. Where appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive embodiments of the present apparatus or method.

[0038] Figure 1 A schematic flow chart of the method steps of the present invention is shown;

[0039] Figure 2 It is the raster grayscale image of the seismic attribute prediction data of sand body thickness in the study area;

[0040] Figure 3 Top-hat transform the seismic attribute raster grayscale image for morphological reconstruction;

[0041] Figure 4 Extract raster grayscale images for discontinuous boundaries of sand bodies in the study area;

[0042] Figure 5 is the frequency distribution histogram of the complexity parameters of the discontinuous boundaries of the sand bodies in the study area;

[0043] Figure 6 16 directional gradient operators designed;

[0044] Figure 7 The detection results of the closed boundary and the distribution of well points. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] like Figure 1-Figure 7 As shown, an embodiment of the present invention provides a method for intelligently extracting sand body configuration boundaries based on seismic data, the method comprising:

[0047] S1 collects seismic data of the study area and obtains seismic attribute prediction data of sand body thickness in the study area;

[0048] S2 performs morphological reconstruction top-hat transformation on the earthquake attribute prediction data to obtain the attribute data after morphological reconstruction;

[0049] Specifically:

[0050] The morphological opening operation is used to process the earthquake attribute prediction data and obtain the opening operation result;

[0051] Subtract the opening operation result from the earthquake attribute prediction data to obtain the top hat transformation result, and use the minimum operation to compare it with the earthquake attribute prediction data to obtain a labeled data;

[0052] Perform morphological dilation on the labeled data to obtain the reconstructed data. If the labeled data is not equal to the reconstructed data, continue to iterate the dilation until the two are equal:

[0053] The reconstruction result is obtained by subtracting the reconstruction data from the earthquake attribute prediction data.

[0054] S3 calculates the morphological gradient of the reconstructed attribute data according to the designed morphological gradient operator to obtain the discontinuous boundary of the sand body in the study area;

[0055] Specifically:

[0056] Design morphological gradient operators in specific directions;

[0057] The designed gradient operator is used to accurately identify and extract the boundaries of the sand body through morphological reconstruction data.

[0058] The morphological gradient operator in a specific direction is as follows Figure 6 As shown, including:

[0059] Morphological gradient operators in 16 directions: 0°, 180°, 22.5°, 202.5°, 45°, 225°, 67.5°, 247.5°, 90°, 270°, 112.5°, 292.5°, 135°, 315°, 157.5°, and 337.5°. Among them, 22.5°, 67.5°, 112.5°, 157.5° and their corresponding opposite directions are extended direction operators, which add gradient changes in eight additional directions, thereby more effectively extracting boundary information.

[0060] According to trigonometric calculations, the tangent value of 22.5° is Therefore, according to the influence of pixel values ​​in different directions on the gradient calculation position, the weight design of the operator is adjusted, such as Figure 6 The 2, 4, 6, and 8 direction operators are shown.

[0061] The adaptive boundary detection algorithm is as follows: define ω1, ω2, ω3, ω4, ω5, ω6, ω7, and ω8 as the structural operator weights in the directions of 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5° respectively; define the weights as ωi is the weight of each direction, D i is the amplitude in each direction, and D is the sum of the amplitudes in each direction; the calculated weights are assigned to the detection boundaries in the corresponding directions to obtain the adaptive detection algorithm for discontinuous boundaries of layer seismic attributes in,

[0062] b1 is a cross-shaped structural element; b2 is a rectangular structural element; b 3k Structural elements in different directions

[0063] S4 calculates the complexity parameter of the extracted boundary, quantitatively characterizes the irregularity of the discontinuous boundary according to the proposed complexity parameter, and determines the number of region divisions for each closed contour;

[0064] The calculation of the complexity parameter of the extracted boundary is specifically as follows:

[0065] The formula for calculating the boundary complexity of each extracted boundary contour is:

[0066]

[0067] C represents the boundary complexity of the contour; L R , L E Respectively represent the perimeter of the minimum circumscribed rectangle and the perimeter of the irregular boundary; A E , A R They are the area of ​​the irregular boundary and the area of ​​the minimum circumscribed rectangle, respectively. The closer C is to 1, the more regular the outline is and the lower its complexity is.

[0068] According to the complexity distribution range of the extracted boundary, the number of region divisions of the boundary is determined.

[0069] S5 screens feature points according to the curvature of the internal boundary of the region, constructs an extension table, and then performs closed contour matching to identify the sand body configuration boundary of the study area.

[0070] Specifically:

[0071] The boundary is divided into several regions, and the feature points with larger curvature in each region are selected to construct the fast random generalized Hough transform extension C;

[0072] Detect irregular closed graphics based on the constructed extended C table and filter out fragmented and unclosed boundaries:

[0073] When creating the extension C, ensure that the selected points come from different regions than those divided, and give priority to points with larger curvature in each region;

[0074] Based on the closed figures detected by the improved fast random generalized Hough transform, closed contour matching is performed to identify the sand body configuration boundary of the study area. The improved fast random generalized Hough transform not only retains the overall trend of the closed boundary, but also highlights the local characteristics, filters out the interference of small areas, and highlights the large lithological closures.

[0075] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for intelligently extracting sand body configuration boundaries based on seismic data, characterized in that: The method comprises: Collect seismic data of the study area and obtain seismic attribute prediction data of sand body thickness in the study area; Perform morphological reconstruction top-hat transformation on the earthquake attribute prediction data to obtain the attribute data after morphological reconstruction; The morphological gradient operator designed is used to calculate the morphological gradient of the reconstructed attribute data to obtain the discontinuous boundary of the sand body in the study area. The complexity parameters of the extracted boundaries are calculated, and the irregularity of the discontinuous boundaries is quantitatively characterized according to the proposed complexity parameters, and the number of regional divisions of each closed contour is determined; Feature points are screened according to the curvature of the internal boundary of the region, an extended table is constructed, and then closed contour matching is performed to identify the sand body configuration boundary of the study area.

2. The method for intelligently extracting sand body configuration boundaries based on seismic data according to claim 1, characterized in that: The morphological reconstruction top-hat transformation is performed on the earthquake attribute prediction data to obtain the attribute data after morphological reconstruction: The morphological opening operation is used to process the earthquake attribute prediction data and obtain the opening operation result; Subtract the result of the opening operation from the earthquake attribute prediction data to obtain the top hat transformation result, and use the minimum operation to compare it with the earthquake attribute prediction data to obtain a labeled data; Perform morphological dilation on the labeled data to obtain the reconstructed data. If the labeled data is not equal to the reconstructed data, continue to iterate the dilation until the two are equal: The reconstruction result is obtained by subtracting the reconstruction data from the earthquake attribute prediction data.

3. The method for intelligently extracting sand body configuration boundaries based on seismic data according to claim 1, characterized in that: The morphological gradient operator designed is used to calculate the morphological gradient of the reconstructed attribute data to obtain the discontinuous boundary of the sand body in the study area: Design morphological gradient operators in specific directions; The designed gradient operator is used to accurately identify and extract the boundaries of the sand body through morphological reconstruction data.

4. The method for intelligently extracting sand body configuration boundaries based on seismic data according to claim 1, characterized in that: The morphological gradient operator in a specific direction includes: Morphological gradient operators in 16 directions: 0°, 180°, 22.5°, 202.5°, 45°, 225°, 67.5°, 247.5°, 90°, 270°, 112.5°, 292.5°, 135°, 315°, 157.5°, and 337.5°.

5. The method for intelligently extracting sand body configuration boundaries based on seismic data according to claim 1, characterized in that: The calculation of the complexity parameter of the extracted boundary is specifically as follows: Calculate the boundary complexity of each extracted boundary contour; According to the complexity distribution range of the extracted boundary, the number of region divisions of the boundary is determined.

6. The method for intelligently extracting sand body configuration boundaries based on seismic data according to claim 5 is characterized in that: The formula for calculating the boundary complexity of each extracted boundary contour is: C represents the boundary complexity of the contour; L R , L E Respectively represent the perimeter of the minimum circumscribed rectangle and the perimeter of the irregular boundary; A E , A R are the area of ​​the irregular boundary and the area of ​​the minimum enclosing rectangle respectively.

7. The method for intelligently extracting sand body configuration boundaries based on seismic data according to claim 1, characterized in that: Feature points are screened according to the curvature of the internal boundary of the region, an extended table is constructed, and then closed contour matching is performed to identify the sand body configuration boundary of the study area as follows: The boundary is divided into several regions, and the feature points with larger curvature in each region are selected to construct the fast random generalized Hough transform extension C; Detect irregular closed graphics based on the constructed extended C table and filter out fragmented and unclosed boundaries: When creating the extension C, ensure that the selected points come from different regions than those divided, and give priority to points with larger curvature in each region; Based on the closed figures detected by fast random generalized Hough transform, closed contour matching is performed to identify the boundaries of the sand body architecture in the study area.

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