An intelligent extraction method for sand body configuration boundaries based on seismic data

Abstract

The present invention provides an intelligent extraction method for sandbody configuration boundaries based on seismic data. The method includes: collecting seismic data of the study area and obtaining predicted seismic attribute data of the sandbody thickness in the study area; performing morphological reconstruction top-hat transformation on the predicted seismic attribute data to obtain the attribute data after morphological reconstruction; calculating the morphological gradient of the reconstructed attribute data according to the designed morphological gradient operator to obtain the discontinuous boundaries of the sandbodies in the study area; calculating the complexity parameters of the extracted boundaries, quantitatively characterizing the irregularity degree of the discontinuous boundaries according to the proposed complexity parameters, and determining the number of regional divisions of each closed contour; screening feature points according to the curvature of the internal boundaries of the regions, constructing an extended table, and then performing closed contour matching to identify the sandbody configuration boundaries in the study area. The present invention can more accurately capture the boundary information of the sandbodies and the characteristics of lithologic traps.

Description

Technical Field

[0001] The present invention belongs to the field of geology, and particularly relates to an intelligent extraction method for sandbody configuration boundaries based on seismic data. Background Art

[0002] In the field of oil and gas exploration, the identification of lithologic traps and sandbody discontinuous boundaries is the key to finding subtle oil and gas reservoirs. Traditionally, seismic interpretation techniques and geological modeling and simulation are the main means to identify these geological features. However, 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 sandbody discontinuous boundaries is affected by various factors, such as river channel change, sedimentary environment change, and later diagenesis, which further increases the difficulty of identification. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent extraction method for sandbody configuration boundaries based on seismic data.

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

[0005] An intelligent extraction method for sandbody configuration boundaries based on seismic data, the method comprising:

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

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

[0008] Perform morphological gradient calculation on the reconstructed attribute data according to the designed morphological gradient operator to obtain the sandbody discontinuous boundary in the study area;

[0009] Calculate the complexity parameter of the extracted boundary, quantitatively characterize the irregularity of the discontinuous boundary according to the proposed complexity parameter, and determine the number of regional divisions of each closed contour;

[0010] Screen feature points according to the curvature of the internal boundary of the region, construct an extension table, and then perform closed contour matching to identify the sandbody configuration boundary in the study area.

[0011] Further, the performing morphological reconstruction top-hat transformation on the seismic attribute prediction data to obtain the attribute data after morphological reconstruction is specifically:

[0012] Process the seismic attribute prediction data by using morphological opening operation to obtain the result of the opening operation;

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

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

[0015] Subtract the reconstructed data from the seismic attribute prediction data to obtain the reconstruction result.

[0016] Furthermore, the morphological gradient calculation 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 is specifically:

[0017] Design a morphological gradient operator in a specific direction;

[0018] Use the designed gradient operator to accurately identify and extract the boundary of the sand body through the morphological reconstruction data.

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

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

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

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

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

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

[0025] Determine the number of regional divisions of the boundary according to the complexity distribution range of the extracted boundary.

[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 respectively are the area of the irregular boundary and the area of the minimum circumscribed rectangle.

[0029] When the number of divided regions is large, the number of calculations will increase, and the detection time will increase. Determine the number of regional divisions according to the complexity distribution of all boundaries. The more regular the boundary, the fewer the feature points of the image. Therefore, the figure can be divided into fewer regions.

[0030] Furthermore, screen the feature points according to the curvature of the internal boundary of the region, construct an extended table, and then perform closed contour matching to identify the specific sand body configuration boundary of the study area as:

[0031] Divide the boundary into several regions, and select the feature points with larger curvature in each region to construct the extended C of the fast random generalized Hough transform;

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

[0033] Ensure that the selected points come from different regions during the creation of the extended C, and give priority to selecting the points with larger curvature in each region;

[0034] Based on the improved fast random generalized Hough transform: Through the quantitative evaluation of the boundary complexity, divide the detected boundary into several regions, and give priority to selecting the feature points with larger curvature in each region 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 result is improved. Perform closed contour matching on the detected closed figures 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 described in the present invention combines various techniques such as morphological reconstruction, adaptive boundary detection, boundary complexity calculation, and fast random generalized Hough transform, improving the accuracy and efficiency of recognition. By collecting and processing high-quality seismic data, this method can more accurately capture the boundary information of sand bodies and the characteristics of lithologic traps. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings generally illustrate various embodiments by way of example and not limitation, and are used in conjunction with the description and the claims to explain 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 an exhaustive or exclusive embodiment of the apparatus or method.

[0038] Figure 1 It shows a schematic flow diagram of the method steps of the present invention;

[0039] Figure 2 It is a grid grayscale map of seismic attribute prediction data for the sand body thickness in the study area;

[0040] Figure 3 It is a grid grayscale map of seismic attributes of morphological reconstruction top-hat transform;

[0041] Figure 4 It is a grid grayscale map for extracting discontinuous boundaries of sand bodies in the study area;

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

[0043] Figure 6 They are 16 designed directional gradient operators;

[0044] Figure 7 They are the detection results of closed boundaries and the well point distribution. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine with the embodiments to detail this application.

[0046] As Figures 1-7 shown, an embodiment of the present invention provides an intelligent extraction method for sand body configuration boundaries based on seismic data, and the method includes:

[0047] S1 Collect seismic data of the study area to obtain seismic attribute prediction data of the sand body thickness in the study area;

[0048] S2 Perform morphological reconstruction top-hat transform on the seismic attribute prediction data to obtain the attribute data after morphological reconstruction;

[0049] Specifically:

[0050] The seismic attribute prediction data is processed by morphological opening operation to obtain the opening operation result;

[0051] The seismic attribute prediction data is subtracted from the opening operation result to obtain the top-hat transformation result, and the minimum operation is used to compare with the seismic attribute prediction data to obtain a marked data;

[0052] The marked data is subjected to morphological dilation operation to obtain the reconstructed data. If the marked data is not equal to the reconstruction, the dilation is continued iteratively until the two are equal:

[0053] The seismic attribute prediction data is subtracted from the reconstructed data to obtain the reconstruction result.

[0054] S3 performs morphological gradient calculation on 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 a morphological gradient operator in a specific direction;

[0057] Using the designed gradient operator, the boundary of the sand body is accurately identified and extracted through the morphological reconstruction data.

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

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

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

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

[0062] b1 is a cross-shaped structural element; b2 is a rectangular structural element; b 3k is the structural element 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 regional divisions of each closed contour;

[0064] The specific calculation of the complexity parameter for the extracted boundary is 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 respectively are the area of the irregular boundary and the area of the minimum circumscribed rectangle. The closer C is to 1, the more regular the contour is and the lower its complexity;

[0068] Determine the number of regional divisions of the boundary according to the complexity distribution range of the extracted boundary.

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

[0070] Specifically:

[0071] Divide the boundary into several regions, select the feature points with larger curvature in each region for the construction of the extended C of the fast random generalized Hough transform;

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

[0073] Ensure that the selected points come from different regions of the division during the creation of the extended C, and give priority to selecting the points with larger curvature in each region;

[0074] Based on the closed figure detected by the improved fast random generalized Hough transform, closed contour matching is carried out to identify the sand body configuration boundary in 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 features, filters out the interference of small areas, and highlights large lithologic traps.

[0075] As mentioned above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within 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 area divisions for 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 3 is 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 sand body configuration boundary in the study area.

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