A method and system for processing five-dimensional data based on offset vector slices

Abstract

The present invention discloses a method and system for processing five-dimensional data based on offset vector slices, comprising the following steps: after performing pre-correction, denoising, and deconvolution processing on the five-dimensional data, obtaining the pre-processed five-dimensional data; dividing the pre-processed five-dimensional data into offset vector slice units to obtain offset vector slice numbers; extracting offset vector slice gathers according to the offset vector slice numbers to obtain the extracted offset vector slice gathers; performing five-dimensional data regularization on the extracted offset vector slice gathers to obtain regularized offset vector slice gathers; and performing pre-stack migration on the regularized offset vector slice gathers to obtain the resulting data. The present invention is used to solve the technical problem that the existing common offset migration method has low processing accuracy, so as to achieve the purpose of improving the processing quality of seismic data and enhancing the quality of seismic data. At the same time, the present invention is conducive to carrying out pre-stack inversion and has the potential to identify anisotropic and fracture characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing methods, and particularly relates to a method and system for processing five-dimensional data based on offset vector slices. Background Art

[0002] In deep geological surveys, obtaining high-quality raw data and adopting effective data processing techniques are the basis for finally obtaining high-quality seismic profiles and then conducting reliable geological interpretations. During the data interpretation process, comprehensively using geophysical and geochemical exploration data and geological data to carry out comprehensive geological interpretation is conducive to obtaining high-quality geological exploration results.

[0003] In conventional seismic data processing methods, the commonly used method is the common offset migration method. The common offset migration method is usually carried out on common shot point, common midpoint or common offset gathers. However, in actual seismic data, there are obvious voids in the common offset gathers with near and far offsets, and they are not single-fold covered. As a result, there are certain accuracy problems when the common offset migration method processes seismic data.

[0004] In addition, conventional common offset domain interpolation usually borrows data from adjacent common offset gathers, which also leads to a decrease in processing accuracy. The main reason is that the azimuth angles of adjacent gathers are different, and there may be differences in the reflections from the same bin with different azimuth angles. Similarly, common offset migration has the problem that the energy of the near and far offsets is weak and the energy of the middle offsets is strong. After stacking, the azimuth angle information cannot be retained, which brings uncertainty to the AVO inversion results. The above differences and uncertainties lead to a further decrease in the processing accuracy of the common offset migration method. Summary of the Invention

[0005] In order to overcome the deficiencies of the prior art, the present invention provides a method and system for processing five-dimensional data based on offset vector slices, which are used to solve the technical problem that the existing common offset migration method has low processing accuracy, so as to achieve the purpose of improving the processing quality of seismic data and enhancing the quality of seismic data.

[0006] To solve the above problems, the technical solutions adopted by the present invention are as follows:

[0007] A method for processing five-dimensional data based on offset vector slices, characterized by comprising the following steps:

[0008] After performing pre-correction, denoising, and deconvolution processing on the five-dimensional data, preprocessed five-dimensional data is obtained;

[0009] Performing offset vector slice unit division on the preprocessed five-dimensional data to obtain offset vector slice numbers;

[0010] Extract the offset vector slice gather according to the offset vector slice number, and obtain the extracted offset vector slice gather;

[0011] Perform five-dimensional data regularization on the extracted offset vector slice gather to obtain a regularized offset vector slice gather;

[0012] Perform pre-stack migration on the regularized offset vector slice gather to obtain the final data.

[0013] As a preferred embodiment of the present invention, when obtaining the offset vector slice gather, it includes:

[0014] According to the shot line distance and the geophone line distance, equally divide the preprocessed five-dimensional data into several rectangles to obtain several offset vector slices;

[0015] Assign numbers to the several offset vector slices to obtain several numbered offset vector slices;

[0016] Obtain the offset vector slice gather according to the several numbered offset vector slices;

[0017] Wherein, one rectangle is one offset vector slice.

[0018] As a preferred embodiment of the present invention, when obtaining the extracted offset vector slice gather, it includes:

[0019] Sort the offset vector slice gather in three-level index according to the line number, common midpoint number and the assigned number to obtain the sorted offset vector slice gather;

[0020] Extract each trace in the sorted offset vector slice gather from different CMP bins in the work area to obtain the extracted offset vector slice gather.

[0021] As a preferred embodiment of the present invention, when performing five-dimensional data regularization on the extracted offset vector slice gather, it includes:

[0022] After obtaining the x and y coordinates of the CMP points on the CMP bin, the projections of the offset in the x and y directions, and the time in five dimensions, perform five-dimensional data regularization.

[0023] As a preferred embodiment of the present invention, when performing five-dimensional data regularization, it includes:

[0024] According to the x and y coordinates of the CMP points, the projections of the offset in the x and y directions, and the time, simultaneously reconstruct all the output seismic traces in the four spatial directions to the grid center points of the offset vector slice, so as to perform regularized reconstruction on the uneven sampling in the four spatial directions and complete the five-dimensional data regularization.

[0025] As a preferred embodiment of the present invention, when regularizing and reconstructing the sampling in four non-uniform spatial directions, it includes:

[0026] Reconstructing the missing seismic traces to obtain more uniform offset attributes and fold attributes.

[0027] As a preferred embodiment of the present invention, when performing prestack migration on the regularized offset vector slice gather, it includes:

[0028] After offsetting each offset vector slice in the regularized offset vector slice gather one by one and outputting them successively, several single migration profiles retaining azimuth information and offset information are obtained;

[0029] According to the several single migration profiles retaining azimuth information and offset information, an offset vector slice prestack time migration CRP gather is obtained, and the acquisition of the result data is completed.

[0030] As a preferred embodiment of the present invention, when offsetting each offset vector slice one by one, it includes:

[0031] After inputting an offset vector slice according to the number of the offset vector slice for offsetting and then outputting a single migration profile, several single migration profiles with similar azimuths and offsets are finally obtained.

[0032] As a preferred embodiment of the present invention, when offsetting each offset vector slice one by one, it further includes:

[0033] Using Kirchhoff integral curved ray prestack time migration to offset each offset vector slice one by one, specifically as shown in Formula 1:

[0034]

[0035] In the formula, Δx, Δy, and Δz are the sampling intervals along x, y, and z, m, n, and l are the sampling serial numbers of the underground points along x, y, and z, m = 0, ±1, ±2, … ±M, n = 0, ±1, ±2, … ±N, l = 0, ±1, ±2, … ±L, ξ and η are the sampling serial numbers of the ground points along x and y, is the first-order difference along the z coordinate.

[0036] A system for processing five-dimensional data based on offset vector slices includes:

[0037] A preprocessing unit: used for performing pre-correction, denoising, and deconvolution processing on the five-dimensional data to obtain preprocessed five-dimensional data;

[0038] Partitioning Unit: It is used to partition the preprocessed five-dimensional data into offset vector slice units to obtain offset vector slice numbers;

[0039] Extraction Unit: It is used to extract offset vector slice gathers according to the offset vector slice numbers to obtain the extracted offset vector slice gathers;

[0040] Regularization Unit: It is used to perform five-dimensional data regularization on the extracted offset vector slice gathers to obtain regularized offset vector slice gathers;

[0041] Migration Unit: It is used to perform pre-stack migration on the regularized offset vector slice gathers to obtain the resulting data.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] (1) The method for processing five-dimensional data based on offset vector slices provided by the present invention effectively improves the processing quality by partitioning offset vector slices of seismic data, regularizing five-dimensional data, and performing pre-stack time migration, improves the quality of seismic data, and is suitable for finely depicting the spatial distribution of small-layer sand bodies and the characteristics of low-amplitude structures;

[0044] (2) The present invention restores the inherent attributes of seismic attribute offset, has excellent characteristics such as being able to retain azimuth information after migration and being able to perform high-precision interpolation, reduces the anisotropy differences caused by uneven seismic data azimuth and coverage times, thereby improving the imaging accuracy of seismic data with strong azimuthal anisotropy and large structural dips, is conducive to carrying out pre-stack inversion, and has the potential to identify anisotropy and fracture characteristics.

[0045] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Description of the Drawings

[0046] Figure 1 - is the OVT azimuth rose diagram and OVT offset vector slice diagram of the embodiment of the present invention;

[0047] Figure 2 - is the schematic diagram of five-dimensional data regularization of the extracted offset vector slice gathers of the embodiment of the present invention;

[0048] Figure 3 - is the comparison diagram of the offset positions before (left) and after (right) five-dimensional data regularization of the embodiment of the present invention;

[0049] Figure 4 - is the comparison diagram of the coverage times before (left) and after (right) five-dimensional data regularization of the embodiment of the present invention;

[0050] Figure 5- is the comparison diagram of the superposition time slices before (left) and after (right) the five-dimensional data regularization in the embodiment of the present invention;

[0051] Figure 6 - is the CRP gather map of the prestack time migration of the offset vector slice in the embodiment of the present invention;

[0052] Figure 7 - is the cross-section comparison diagram of the conventional prestack time migration (upper) and the prestack time migration in the offset vector slice domain (lower) in the embodiment of the present invention;

[0053] Figure 8 - is the signal-to-noise ratio comparison diagram of the conventional prestack time migration (upper) and the prestack time migration in the offset vector slice domain (lower) in the embodiment of the present invention;

[0054] Figure 9 - is the method step diagram for processing five-dimensional data based on the offset vector slice in the embodiment of the present invention. Detailed implementation manners

[0055] The method for processing five-dimensional data based on the offset vector slice provided by the present invention, as Figure 9 shown, includes the following steps:

[0056] Step S1: After performing pre-correction, denoising, and deconvolution processing on the five-dimensional data, obtain the preprocessed five-dimensional data;

[0057] Step S2: Divide the preprocessed five-dimensional data into offset vector slice units to obtain the offset vector slice numbers;

[0058] Step S3: Extract the offset vector slice gathers according to the offset vector slice numbers to obtain the extracted offset vector slice gathers;

[0059] Step S4: Perform five-dimensional data regularization on the extracted offset vector slice gathers to obtain the regularized offset vector slice gathers;

[0060] Step S5: Perform prestack migration on the regularized offset vector slice gathers to obtain the result data.

[0061] In the above step S2, when obtaining the offset vector slice gathers, it includes:

[0062] According to the shot line spacing and the geophone line spacing, equally divide the preprocessed five-dimensional data into several rectangles to obtain several offset vector slices;

[0063] Assign numbers to the several offset vector slices to obtain several numbered offset vector slices;

[0064] Obtain the offset vector slice gathers according to the several numbered offset vector slices;

[0065] Among them, a rectangle is a shot-receiver offset vector slice.

[0066] Specifically, on the basis of doing well in pre-correction, denoising, and deconvolution processing, the five-dimensional data is divided into shot-receiver offset vector slice units, that is, numbers are assigned to the five-dimensional data. Generally, rectangles are divided according to equal distances such as shot line distance and geophone line distance. Each rectangle is a shot-receiver offset vector slice, and each vector slice contains offset and azimuth information. Figure 1 It is the OVT azimuth rose diagram and the OVT shot-receiver offset vector slice diagram, which are the attribute diagrams represented by azimuth, shot-receiver offset, and shot-receiver offset vector slices. Different colors in the shot-receiver offset vector slice diagram represent different divided shot-receiver offset vector slices, and the numbers represent the numbers of each shot-receiver offset vector slice.

[0067] In the above step S3, when obtaining the extracted shot-receiver offset vector slice gather, it includes:

[0068] Sort the shot-receiver offset vector slice gather in three-level index according to line number, common midpoint number, and the assigned number to obtain the sorted shot-receiver offset vector slice gather;

[0069] Extract each trace in the sorted shot-receiver offset vector slice gather from different CMP bins in the work area to obtain the extracted shot-receiver offset vector slice gather.

[0070] Specifically, extract the shot-receiver offset vector slice gather according to the assigned number, that is, sort the shot-receiver offset vector slice gather in three-level index according to line number, common midpoint number, and the assigned number when inputting. Extract each trace in the shot-receiver offset vector slice gather from different CMP bins in the work area, and there are as many traces in a shot-receiver offset vector slice gather as there are CMP bins. Use the extracted shot-receiver offset vector slice gather as the input data for the next step.

[0071] In the above step S4, when performing five-dimensional data regularization on the extracted shot-receiver offset vector slice gather, it includes:

[0072] After obtaining the x and y coordinates of the CMP points on the CMP bin, the projections of the shot-receiver offset in the x and y directions, and the time in five dimensions, perform five-dimensional data regularization.

[0073] Furthermore, when performing five-dimensional data regularization, it includes:

[0074] According to the x and y coordinates of the CMP points, the projections of the shot-receiver offset in the x and y directions, and the time, simultaneously reconstruct all the output seismic traces in the four spatial directions to the grid center points of the shot-receiver offset vector slice, so as to perform regularized reconstruction on the uneven sampling in the four spatial directions and complete the five-dimensional data regularization.

[0075] Further, when regularizing the reconstruction of uneven sampling in four spatial directions, it includes:

[0076] Reconstructing the missing seismic traces to obtain more uniform offset attributes and fold attributes.

[0077] Specifically, five-dimensional data regularization is performed on the extracted offset vector slice gather. Five-dimensional data regularization is carried out for regularization processing in 5 dimensions, which are the x and y coordinates of the CMP point plus the projections of the offset in the x and y directions plus time. The processing idea is as Figure 2 shown. At the same time, regularization processing in four spatial directions is performed, that is, all the output seismic traces are reconstructed to the grid center point, so that the uneven sampling in the spatial direction is regularly reconstructed, and the missing seismic traces can also be reconstructed to a certain extent, thereby improving the unevenness of attributes such as offset and fold, as Figure 3 (Comparison diagram of offset positions before (left) and after (right) five-dimensional data regularization) and Figure 4 (Comparison diagram of fold numbers before (left) and after (right) five-dimensional data regularization) shown. At the same time, the signal-to-noise ratio of the seismic data is improved, as Figure 5 (Comparison diagram of stacked time slices before (left) and after (right) five-dimensional data regularization) shown.

[0078] In the above step S5, when performing prestack migration on the regularized offset vector slice gather, it includes:

[0079] After offsetting each offset vector slice in the regularized offset vector slice gather one by one, the output is carried out successively to obtain a number of single migration profiles retaining azimuth information and offset information;

[0080] According to a number of single migration profiles retaining azimuth information and offset information, an offset vector slice prestack time migration CRP gather is obtained to complete the acquisition of the result data.

[0081] Further, when offsetting each offset vector slice one by one, it includes:

[0082] After inputting an offset vector slice according to the number of the offset vector slice for offsetting, a single migration profile is output, and finally a number of single migration profiles with similar azimuth and offset are obtained.

[0083] Further, when offsetting each offset vector slice one by one, it also includes:

[0084] Using Kirchhoff integral curved ray prestack time migration to offset each offset vector slice one by one, specifically as shown in formula 1:

[0085]

[0086] Wherein, Δx, Δy, and Δz are sampling intervals along x, y, and z, m, n, and l are sampling serial numbers of underground points along x, y, and z, m = 0, ±1, ±2, … ±M, n = 0, ±1, ±2, … ±N, l = 0, ±1, ±2, … ±L, and ξ and η are sampling serial numbers of ground points along x and y. is the first-order difference along the z coordinate.

[0087] Specifically, after the five-dimensional data regularization process, the offset vector slice makes up for data holes, enables the distribution of bin centers and single-fold coverage, and is very suitable for prestack migration. The prestack time migration of the offset vector slice is performed one by one on each offset vector slice. Since the data before migration is numbered according to the offset vector slice, after each input of an offset vector slice is migrated, a single migration profile is output. Each single migration profile has similar azimuth and offset. Therefore, after migration, each trace retains its azimuth information and offset information. The Kirchhoff integral method with curved rays prestack time migration method is selected for the migration method, and is represented by the above formula (1) with a discrete function.

[0088] Using formula (1), through the operation of the five-dimensional data, the prestack time migration imaging of the five-dimensional data can be realized. The prestack time migration CRP gather of the offset vector slice is as Figure 6 shown. Figure 6 In it, blue is for azimuth display, and red is for offset display, representing that the output imaging gather is the seismic response of a specific offset and azimuth, thus being able to improve the imaging accuracy and lateral resolution of the migration result more, being conducive to carrying out prestack inversion, obtaining azimuthal anisotropy, and predicting fractures. Figure 7 and Figure 8 are the cross-section comparison diagram and signal-to-noise ratio comparison diagram of conventional prestack time migration and prestack time migration in the offset vector slice domain. From Figure 7 and Figure 8 it can be seen that compared with conventional prestack time migration, the prestack time migration in the offset vector slice domain has significantly improved shallow continuity, and has higher signal-to-noise ratio and resolution.

[0089] In summary, the method for processing five-dimensional data based on offset vector slices provided by the present invention considers the azimuthal anisotropy problem. The data divided by the offset vector slice has similar offsets and azimuths, high data similarity, and better consistency. The signal-to-noise ratio of the resulting data after five-dimensional data regularization and prestack time migration is significantly improved, and the azimuth information is retained, which can better carry out work such as fracture prediction and extraction of azimuthal anisotropy parameters.

[0090] The system for processing five-dimensional data based on offset vector slices provided by the present invention includes a preprocessing unit, a partitioning unit, an extraction unit, a regularization unit, and a migration unit. The preprocessing unit is used to perform pre-correction, denoising, and deconvolution processing on the five-dimensional data to obtain preprocessed five-dimensional data. The partitioning unit is used to partition the preprocessed five-dimensional data into offset vector slice units to obtain offset vector slice numbers. The extraction unit is used to extract offset vector slice gathers according to the offset vector slice numbers to obtain well-extracted offset vector slice gathers. The regularization unit is used to regularize the five-dimensional data on the well-extracted offset vector slice gathers to obtain regularized offset vector slice gathers. The migration unit is used to perform prestack migration on the regularized offset vector slice gathers to obtain result data.

[0091] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0092] (1) The method for processing five-dimensional data based on offset vector slices provided by the present invention effectively improves the processing quality by performing offset vector slice partitioning, five-dimensional data regularization, and prestack time migration on seismic data, improves the quality of seismic data, and is suitable for finely depicting the spatial distribution of small-layer sand bodies and the characteristics of low-amplitude structures;

[0093] (2) The present invention restores the inherent attributes of the seismic attribute offset, has excellent characteristics such as being able to retain azimuth information after migration and being able to perform high-precision interpolation, reduces the anisotropy differences caused by uneven seismic data azimuth and coverage times, thereby improving the imaging accuracy of seismic data with strong azimuthal anisotropy and large structural dips, is conducive to carrying out prestack inversion, and has the potential to identify anisotropy and fracture characteristics.

[0094] The above embodiments are only the preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantive changes and substitutions made by those skilled in the art based on the present invention belong to the scope of protection required by the present invention.

Claims

1. A method for processing five-dimensional data based on offset vector slices, characterized in that, It includes the following steps: After pre - correcting, denoising, and deconvolving the five - dimensional data, the pre - processed five - dimensional data is obtained; Dividing the pre - processed five - dimensional data into shot - receiver offset vector slice units to obtain shot - receiver offset vector slice numbers; Extracting shot - receiver offset vector slice gathers according to the shot - receiver offset vector slice numbers to obtain the extracted shot - receiver offset vector slice gathers; Regularizing the five - dimensional data on the extracted shot - receiver offset vector slice gathers to obtain regularized shot - receiver offset vector slice gathers; Performing prestack migration on the regularized shot - receiver offset vector slice gathers to obtain the final data; specifically, after offsetting each shot - receiver offset vector slice in the regularized shot - receiver offset vector slice gathers one by one and outputting them successively, a number of single - offset profiles retaining azimuth information and offset information are obtained; according to the number of single - offset profiles retaining azimuth information and offset information, a prestack time migration CRP gather of shot - receiver offset vector slices is obtained to complete the acquisition of the final data; When offsetting each shot - receiver offset vector slice one by one, it further includes: Using Kirchhoff integral curved ray prestack time migration to offset each shot - receiver offset vector slice one by one, as shown in formula 1 specifically; （1）； where Δx, Δy, and Δz are the sampling intervals along x, y, and z, respectively, m, n, and l are the sampling sequence numbers of the subsurface points along x, y, and z, with m = 0, ±1, ±2, … ±M, n = 0, ±1, ±2, … ±N, l = 0, ±1, ±2, … ±L, and ξ, η are the sampling sequence numbers of the surface points along x and y. is the first-order difference along the z coordinate.

2. The method for processing five-dimensional data based on offset vector slices according to claim 1, wherein When obtaining the shot - receiver offset vector slice gathers, it includes: According to the shot line distance and receiver line distance, equally dividing the pre - processed five - dimensional data into several rectangles to obtain several shot - receiver offset vectors; Assigning numbers to the several shot - receiver offset vectors to obtain several numbered shot - receiver offset vectors; Obtaining the shot - receiver offset vector slice gathers according to the several numbered shot - receiver offset vectors; Wherein, one rectangle is one shot - receiver offset vector slice.

3. The method for processing five-dimensional data based on offset vector slices according to claim 2, characterized in that, When obtaining the extracted shot - receiver offset vector slice gathers, it includes: Sorting the shot - receiver offset vector slice gathers in three - level index according to line number, common mid - point number, and assigned number to obtain the sorted shot - receiver offset vector slice gathers; Extracting each trace in the sorted shot - receiver offset vector slice gathers from different CMP bins in the work area to obtain the extracted shot - receiver offset vector slice gathers.

4. The method for processing five-dimensional data based on offset vector slices according to claim 3, wherein When regularizing the five - dimensional data on the extracted shot - receiver offset vector slice gathers, it includes: After obtaining the x and y coordinates of the CMP points on the CMP bin, the projections of the shot - receiver offset in the x and y directions, and the time in five dimensions, perform five - dimensional data regularization.

5. The method for processing five-dimensional data based on offset vector slices according to claim 4, wherein When performing five - dimensional data regularization, it includes: According to the x and y coordinates of the CMP points, the projections of the shot - receiver offset in the x and y directions, and the time, simultaneously reconstruct all the output seismic traces in the four spatial directions to the grid center points of the shot - receiver offset vector slices, so as to perform regularized reconstruction on the uneven sampling in the four spatial directions and complete the five - dimensional data regularization.

6. The method for processing five-dimensional data based on offset vector slices according to claim 5, wherein When performing regularized reconstruction on the uneven sampling in the four spatial directions, it includes: Reconstructing the missing seismic traces to obtain more uniform shot - receiver offset attributes and coverage times attributes.

7. The method for processing five-dimensional data based on offset vector slices according to claim 1, wherein When offsetting each shot - receiver offset vector slice one by one, it includes: After inputting a shot - receiver offset vector slice according to its number for offsetting, output a single - offset profile, and finally obtain a number of single - offset profiles with similar azimuths and offset distances.

8. A system for processing five-dimensional data based on offset vector slices, characterized in that, It includes: Preprocessing unit: used to perform pre-correction, denoising, and deconvolution processing on the five-dimensional data to obtain preprocessed five-dimensional data; Partitioning unit: used to partition the preprocessed five-dimensional data into offset vector slice units to obtain offset vector slice numbers; Extraction unit: used to extract offset vector slice gathers according to the offset vector slice numbers to obtain the extracted offset vector slice gathers; Regularization unit: used to perform five-dimensional data regularization on the extracted offset vector slice gathers to obtain regularized offset vector slice gathers; Migration unit: used to perform prestack migration on the regularized offset vector slice gathers to obtain the final data; specifically, after migrating each offset vector slice in the regularized offset vector slice gathers one by one and outputting them successively, several single migration profiles retaining azimuth information and offset information are obtained; according to the several single migration profiles retaining azimuth information and offset information, a prestack time migration CRP gather of offset vector slices is obtained, completing the acquisition of the final data; When migrating each offset vector slice one by one, it further includes: Using Kirchhoff integral curved ray prestack time migration to migrate each offset vector slice one by one, specifically as shown in Formula 1: （1）； where Δx, Δy, and Δz are the sampling intervals along x, y, and z, m, n, and l are the sampling sequence numbers of the underground points along x, y, and z, m = 0, ±1, ±2, … ±M, n = 0, ±1, ±2, … ±N, l = 0, ±1, ±2, … ±L, and ξ and η are the sampling sequence numbers of the ground points along x and y. is the first-order difference along the z coordinate.

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