Data-driven pseudo three-dimensional imaging method and device, equipment and storage medium

Abstract

The invention belongs to the technical field of oil exploration seismic data processing, and discloses a data-driven pseudo three-dimensional imaging method. Carrying out demigration, data matching and three-dimensional gridding, sparse data inter-channel interpolation, data driving inclination angle weight factor constraint data reconstruction and data driving step-by-step iterative interpolation on the two-dimensional post-stack migration data to obtain a pseudo three-dimensional data volume, and establishing a pseudo three-dimensional velocity field through multi-information constraint; performing post-stack migration on the pseudo three-dimensional data volume by using the pseudo three-dimensional velocity field to obtain a migration result, and performing post-stack modification and high-resolution processing on the migration result to realize data-driven pseudo three-dimensional imaging; the invention also discloses a data-driven pseudo three-dimensional imaging device, equipment and a storage medium. The method is suitable for pseudo three-dimensional imaging of the old two-dimensional data, the utilization rate of the old two-dimensional data is greatly improved, the closing problem of different two-dimensional data is solved, and a more visual and convenient data body is provided for interpretation personnel.

Description

Technical Field

[0001] The invention belongs to the technical field of oil exploration seismic data processing, and relates to a data-driven pseudo three-dimensional imaging method, device, equipment and storage medium. Background Art

[0002] With the continuous deepening of oil and gas exploration, the density of two-dimensional seismic exploration data in key exploration areas at home and abroad is very high. However, due to the different acquisition times and parameters of these two-dimensional seismic exploration data, and the different processing methods for these data, many problems will be encountered when comprehensively interpreting these data, such as the difficulty of closing between work areas and the multi-solution of structural interpretation. Therefore, how to tap the potential of old two-dimensional data has become a hot topic and key to current research.

[0003] Pseudo three-dimensional imaging is a method of converting old two-dimensional data into a pseudo three-dimensional data body through data interpolation. However, the current pseudo three-dimensional imaging methods lack corresponding supporting technologies, such as anti-migration technology, sparse data interpolation methods, etc., and the current interpolation methods do not consider the impact of formation dip on data reconstruction. These factors will lead to a large number of abnormal traces and spatial false frequencies in the interpolated pseudo three-dimensional data body, which makes it difficult for interpreters to use them. Therefore, it is necessary to study a pseudo three-dimensional imaging technology with strong applicability and higher imaging accuracy to fully tap the utilization potential of old two-dimensional data. Summary of the invention

[0004] The purpose of the present invention is to provide a data-driven pseudo three-dimensional imaging method, which obtains a pseudo three-dimensional data volume by processing and interpolating two-dimensional post-stack migration data to improve the utilization rate of old two-dimensional data and solve the closure problem of different two-dimensional data.

[0005] Another object of the present invention is to provide a data-driven pseudo three-dimensional imaging device, equipment and storage medium.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A data-driven pseudo three-dimensional imaging method comprises the following steps:

[0008] The two-dimensional post-stack migration data is processed to obtain a pseudo-three-dimensional data volume, a pseudo-three-dimensional velocity field is established through multi-information constraints, the pseudo-three-dimensional data volume is post-stack migrated using the pseudo-three-dimensional velocity field, the migration result is obtained, the migration result is post-stack modified and high-resolution processed, and data-driven pseudo-three-dimensional imaging is realized;

[0009] The method of processing the two-dimensional post-stack migration data to obtain a pseudo three-dimensional data volume comprises the following steps:

[0010] S1, 2D post-stack migration data de-migration

[0011] Obtain a 2D post-stack time migration section of a 2D work area, perform post-stack de-migration on the 2D post-stack time migration section, and obtain a self-excited and self-received stack section;

[0012] S2. Data matching and 3D meshing

[0013] The obtained self-excited and self-received superimposed sections are subjected to high signal-to-noise ratio processing and Fourier transformation to obtain a superimposed section after data matching; the superimposed section after data matching is subjected to three-dimensional grid processing to obtain a three-dimensional grid model of the superimposed section;

[0014] S3, sparse data interpolation

[0015] For the stacked section 3D grid model, if the line distance value of the sparse data area is greater than the set value, the internal inter-trace interpolation boundary and the external inter-trace interpolation boundary of the stacked section 3D grid model are determined according to the line distance, and the grids in the internal inter-trace interpolation boundary area are interpolated to obtain the pseudo 3D stacked data volume 3D grid model;

[0016] The line distance is the distance between two parallel two-dimensional lines;

[0017] S4. Data-driven inclination weight factor constrains data reconstruction

[0018] The three-dimensional grid model of the pseudo three-dimensional stacked data volume is subjected to denoising to obtain pseudo three-dimensional post-stack seismic data, the three-dimensional post-stack seismic data is subjected to discrete Fourier transform to obtain an initial Fourier spectrum, the dip factor weight of the Fourier spectrum is iteratively calculated, the Fourier spectrum component with the maximum energy after weighting according to the dip factor weight is selected, the Fourier spectrum component is added to the estimated spectrum, and then multiplied with the spatial sampling operator, and the product result is subtracted from the Fourier spectrum to update the Fourier spectrum, and it is iterated repeatedly, and finally the estimated spectrum is subjected to inverse Fourier transform and output to the desired position, so as to complete the reconstruction of the dip factor reflection and obtain the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume;

[0019] S5. Data-driven step-by-step iterative interpolation

[0020] The grid in the three-dimensional grid model of the pseudo three-dimensional superimposed data volume obtained in step S3 is used as the first-level grid, and the size of the first-level grid is iterated step by step in the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume obtained in step S4, so that the size of the next-level grid is adjusted to half of the previous level until it is reduced to the target grid, and the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume after each iteration is interpolated to obtain a pseudo three-dimensional data volume.

[0021] As a limitation, the multi-information in the multi-information constraint includes a two-dimensional velocity file, a velocity field of an adjacent three-dimensional work area, well logging data, and a structural steering velocity smoothing parameter.

[0022] As a further limitation, in step S1, a finite difference method is used to perform post-stack de-migration on the two-dimensional post-stack time migration profile.

[0023] As a further limitation, in step S2, when the superimposed cross section after data matching is subjected to three-dimensional gridding, the size of the grid is:

[0024]

[0025] Wherein, Line gap represents the line distance between adjacent two-dimensional lines.

[0026] As a further limitation, in step S5, discrete Fourier transform is performed on the three-dimensional post-stack seismic data to obtain the initial Fourier spectrum calculation formula:

[0027]

[0028] in, is the initial amplitude spectrum, the initial Fourier spectrum, D in is the amplitude spectrum of the input data, F is the positive Fourier transform, T is the matrix mask data, γ is the tilt weight factor, and p is a non-negative exponential correction term used to control the size of γ;

[0029] The calculation formula for the inclination factor weight of the iterative calculation of the Fourier spectrum is:

[0030] γ(k x ,k y ,f)=A(θ x ,θ y )=∫|D in (θ x ,θ y ,r)|dr

[0031]

[0032] Among them, k x is the data of the main survey line direction after discrete Fourier transform of 3D post-stack seismic data, k y is the data of the tie line direction after the three-dimensional post-stack seismic data is subjected to discrete Fourier transform, f is the frequency domain part after the three-dimensional post-stack seismic data is subjected to discrete Fourier transform; θ x is the effective apparent inclination angle of radial Fourier transform along the main survey line, θ y is the effective apparent inclination angle of the radial Fourier transform along the tie line direction, and A is θ x and θ y The sum of the amplitude spectra.

[0033] The present invention also provides a data-driven pseudo three-dimensional imaging device, comprising:

[0034] A pseudo three-dimensional data volume module is used to process two-dimensional post-stack migration data to obtain a pseudo three-dimensional data volume;

[0035] A multi-information constrained pseudo 3D velocity field module is used to establish a pseudo 3D velocity field through multi-information constraints;

[0036] Migration module, used to perform post-stack migration on pseudo 3D data volume using pseudo 3D velocity field to obtain migration results;

[0037] Data-driven pseudo 3D imaging is used to perform post-stack modification and high-resolution processing on the migration results to achieve data-driven pseudo 3D imaging.

[0038] As a limitation, the pseudo three-dimensional data volume module further includes:

[0039] The 2D post-stack migration data de-migration module is used to obtain the 2D post-stack time migration section of the 2D work area, perform post-stack de-migration on the 2D post-stack time migration section, and obtain the self-excited and self-collected stack section;

[0040] The data matching and three-dimensional gridding module is used to perform high signal-to-noise ratio processing and Fourier transform on the obtained superimposed sections of self-excited and self-received data to obtain the superimposed sections after data matching; the superimposed sections after data matching are subjected to three-dimensional gridding processing to obtain a three-dimensional gridding model of the superimposed sections;

[0041] The sparse data interpolation module is used for determining the internal interpolation boundary and the external interpolation boundary of the stacked section 3D grid model according to the line distance if the line distance value of the sparse data area is greater than the set value, and performing interpolation on the grids in the internal interpolation boundary area to obtain a pseudo 3D stacked data volume 3D grid model;

[0042] A data-driven dip weight factor constrained data reconstruction module is used to perform noise reduction processing on a three-dimensional grid model of a pseudo three-dimensional stacked data volume, obtain pseudo three-dimensional post-stack seismic data, perform discrete Fourier transform on the three-dimensional post-stack seismic data, obtain an initial Fourier spectrum, iteratively calculate the dip factor weight of the Fourier spectrum, select the Fourier spectrum component with the maximum energy after weighting according to the dip factor weight, add the Fourier spectrum component to the estimated spectrum, then multiply it with the spatial sampling operator, and subtract the product result from the Fourier spectrum to update the Fourier spectrum, iterate repeatedly, and finally perform inverse Fourier transform on the estimated spectrum and output it to a desired position, thereby completing the reconstruction of the dip factor reflection and obtaining a three-dimensional grid model of a pseudo three-dimensional reconstructed data volume;

[0043] A data-driven step-by-step iterative interpolation module is used to use the grids in the obtained three-dimensional grid model of the pseudo three-dimensional superimposed data volume as the first-level grids, and to iterate the size of the first-level grids in the obtained three-dimensional grid model of the pseudo three-dimensional reconstructed data volume step by step so that the size of the next-level grid is adjusted to half of the previous level, and to interpolate the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume after each iteration to obtain a pseudo three-dimensional data volume.

[0044] The present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor to execute the above-mentioned data-driven pseudo three-dimensional imaging method.

[0045] The present invention also provides a storage medium, wherein the storage medium is used to store at least one section of a computer program, and the at least one section of the computer program is used to execute the above-mentioned data-driven pseudo three-dimensional imaging method.

[0046] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0047] (1) The present invention processes and interpolates two-dimensional post-stack migration data to obtain a pseudo three-dimensional data volume, which greatly improves the utilization rate of old two-dimensional data and solves the closure problem of different two-dimensional data, providing a more intuitive and convenient data volume for interpreters;

[0048] (2) The present invention establishes a pseudo 3D velocity field through multi-information constraints, provides a reasonable velocity field for the next step of post-stack migration of the pseudo 3D data volume, and realizes data-driven pseudo 3D imaging after post-stack modification and high-resolution processing of the migration results, providing more efficient and reliable pseudo 3D data for interpreters, greatly improving the utilization potential of old 2D data;

[0049] (3) The present invention can restore the two-dimensional post-stack time migration section to a self-excited and self-collected stack section by performing post-stack de-migration on the two-dimensional post-stack time migration section, thereby improving the closure accuracy;

[0050] (4) The present invention performs interpolation between sparse data traces for the three-dimensional grid model of the stacked profile, thereby solving the interpolation anomaly problem between the large spacings of the two-dimensional seismic exploration lines; and constrains data reconstruction by using data-driven dip weight factors to obtain an interpolation result with higher accuracy and fewer anomalies.

[0051] In summary, the present invention is suitable for pseudo three-dimensional imaging of old two-dimensional data, which greatly improves the utilization rate of old two-dimensional data and solves the closure problem of different two-dimensional data, providing interpreters with more intuitive and convenient data bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Shown is a flow chart of the method of embodiment 1 of the present invention;

[0053] FIG2( a ) shows a two-dimensional post-stack time migration section in Example 1 of the present invention;

[0054] FIG2( b ) shows the stacked cross section of self-excitation and self-collection obtained after post-stack demigration in Example 1 of the present invention;

[0055] FIG3( a ) shows an arbitrary line profile before data matching time difference correction in Example 1 of the present invention;

[0056] FIG3( b ) shows an arbitrary line profile after data matching time difference correction in Example 1 of the present invention;

[0057] FIG4( a ) shows a three-dimensional grid model of a superimposed cross section obtained in Example 1 of the present invention;

[0058] FIG4( b ) shows a three-dimensional grid model of a pseudo three-dimensional stacked data volume after interpolation between sparse data channels in Example 1 of the present invention;

[0059] Figure 5 It is a comparison diagram of data-driven tilt angle weight factor constrained data reconstruction in Example 1 of the present invention;

[0060] Figure 6 The figure shows an example of a grid of data-driven step-by-step iterative interpolation in Embodiment 1 of the present invention;

[0061] Figure 7 It is a cross-sectional comparison diagram of the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume before interpolation, the conventional pseudo three-dimensional interpolation, and the pseudo three-dimensional data volume by data-driven step-by-step iterative interpolation in Example 1 of the present invention;

[0062] Figure 8 It shows a slice comparison diagram of a three-dimensional grid model of a pseudo three-dimensional reconstructed data volume before interpolation, a conventional pseudo three-dimensional interpolation, and a pseudo three-dimensional data volume by data-driven step-by-step iterative interpolation in Example 1 of the present invention;

[0063] FIG9( a ) shows the pseudo three-dimensional velocity field before smoothing in Example 1 of the present invention;

[0064] FIG9( b ) shows the pseudo three-dimensional velocity field after smoothing in Example 1 of the present invention;

[0065] FIG10( a ) is a cross-sectional view of a pseudo three-dimensional data volume before post-stack migration in Example 1 of the present invention;

[0066] FIG10( b ) is a cross-sectional view of the pseudo three-dimensional data volume after post-stack migration in Example 1 of the present invention;

[0067] Fig.11The figure shows a comparison diagram of the original 3D imaging and the data-driven pseudo 3D imaging of a certain block in Example 1 of the present invention;

[0068] Fig.12 The figure shows a superimposed display diagram of a pseudo three-dimensional imaging time slice driven by data of a certain block in Example 1 of the present invention and an uncertainty analysis diagram;

[0069] Fig.13 FIG. 2 is a block diagram of a device according to Embodiment 2 of the present invention;

[0070] Fig.14 FIG. 2 is a structural block diagram of a pseudo three-dimensional data volume module in Embodiment 2 of the present invention;

[0071] Fig.15 FIG. 2 is a schematic diagram showing the structure of a computer device in Embodiment 2 of the present invention;

[0072] Fig.16 The figure is a schematic diagram of the structure of the computer storage medium in the second embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0074] Embodiment 1 A data driven pseudo three-dimensional imaging method

[0075] like Figure 1 As shown, this embodiment is a data-driven pseudo three-dimensional imaging method, that is, a method of simulating three-dimensional seismic data imaging processing based on processed two-dimensional stacked seismic data. Since it is not true three-dimensional seismic data, it is called a pseudo three-dimensional imaging method.

[0076] The processing flow of this embodiment includes the following steps:

[0077] S1, 2D post-stack migration data de-migration

[0078] Obtain a 2D post-stack time migration section of a 2D work area, perform post-stack de-migration on the 2D post-stack time migration section, and obtain a self-excited and self-received stack section;

[0079] In this step, the finite difference method is used to perform post-stack de-migration on the two-dimensional post-stack time migration section. In the data preparation stage of data-driven pseudo-three-dimensional imaging, after obtaining the two-dimensional post-stack time migration section of the two-dimensional work area, since the closure of seismic data must be carried out on the basis of self-excitation and self-collection, post-stack de-migration of the data must be performed.

[0080] As shown in Figure 2(a) and Figure 2(b), the two-dimensional post-stack time migration section obtained in this step and the superimposed section of self-excitation and self-collection obtained after post-stack de-migration are shown. As shown in Figure 2(b), after the two-dimensional post-stack time migration section is de-migrated, the diffraction wave is de-migrated successfully.

[0081] S2. Data matching and 3D meshing

[0082] The obtained self-excited and self-received superimposed sections are subjected to high signal-to-noise ratio processing and Fourier transformation to obtain a superimposed section after data matching; the superimposed section after data matching is subjected to three-dimensional grid processing to obtain a three-dimensional grid model of the superimposed section;

[0083] When the superimposed section after data matching is processed into three-dimensional grid, the size of the grid is:

[0084]

[0085] Among them, Line gap represents the line distance between adjacent two-dimensional lines;

[0086] In this step, high signal-to-noise ratio processing is performed on the obtained self-excited and self-collected superimposed profile to remove random noise in the superimposed profile, and Fourier transform can greatly reduce the generation of abnormal channels. Since the acquisition time and method of different two-dimensional work areas are different, and the processing methods of different data are also different, the obtained self-excited and self-collected superimposed profile is processed with high signal-to-noise ratio and Fourier transform, and after data matching, the phase, time difference, frequency and amplitude between different two-dimensional work areas can be adjusted to gradually reduce the difference between them. Therefore, data matching is the key to finally realizing data-driven pseudo three-dimensional imaging;

[0087] As shown in Figure 3(a) and Figure 3(b), the arbitrary line profiles before and after the time difference correction for data matching in this step are shown. It can be seen that after the time difference correction, the time difference between different two-dimensional lines in different work areas is eliminated, and a closed state is achieved;

[0088] After data matching, the stacked sections after data matching are processed into three-dimensional grids, that is, all the two-dimensional data in the stacked sections after data matching need to be applied to a three-dimensional grid to prepare for subsequent three-dimensional interpolation;

[0089] S3, sparse data interpolation

[0090] For the stacked section 3D grid model, if the line distance value of the sparse data area is greater than the set value, the internal and external trace interpolation boundaries of the stacked section 3D grid model are determined according to the line distance, and the grids in the internal trace interpolation boundary area are interpolated to obtain a pseudo 3D stacked data volume 3D grid model; wherein the line distance is the distance between two parallel 2D lines;

[0091] In this step, the setting value is two kilometers. For the sparse data area in the three-dimensional grid model of the stacked profile, the line spacing can be reduced by interpolation. The biggest problem solved by interpolation is that it may cause instability when interpolating too sparse data. Therefore, when the line spacing value of the sparse data area is greater than two kilometers, interpolation is performed on the grids in the internal interpolation boundary area to reduce the abnormal tracks generated by interpolation. In addition, by determining the internal and external interpolation boundaries of the three-dimensional grid model of the stacked profile according to the line spacing, the range of interpolation can be controlled. Although interpolation can improve the accuracy of interpolation in the main structural direction, it may damage the details of the interpolated layer.

[0092] As shown in Figure 4(a) and Figure 4(b), the three-dimensional grid model of the stacked section and the three-dimensional grid model of the pseudo three-dimensional stacked data volume after interpolation between sparse data traces are shown. It can be seen from the figure that the interpolation between traces can greatly reduce the abnormal traces generated by the interpolation, and also improve the accuracy of the interpolation in the main structural direction;

[0093] S4. Data-driven inclination weight factor constrains data reconstruction

[0094] The three-dimensional grid model of the pseudo three-dimensional stacked data volume is subjected to denoising to obtain pseudo three-dimensional post-stack seismic data, the three-dimensional post-stack seismic data is subjected to discrete Fourier transform to obtain an initial Fourier spectrum, the dip factor weight of the Fourier spectrum is iteratively calculated, the Fourier spectrum component with the maximum energy after weighting according to the dip factor weight is selected, the Fourier spectrum component is added to the estimated spectrum, and then multiplied with the spatial sampling operator, and the product result is subtracted from the Fourier spectrum to update the Fourier spectrum, and it is iterated repeatedly, and finally the estimated spectrum is subjected to inverse Fourier transform and output to the desired position, so as to complete the reconstruction of the dip factor reflection and obtain the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume;

[0095] This step can greatly reduce the abnormal situation of pseudo three-dimensional interpolation; the first round of estimated spectrum is the initial Fourier spectrum of the data. The estimated spectrum after iteration is the Fourier spectrum of the seismic data after enhanced dip interpolation. Since the Fourier spectrum is a sparse Fourier spectrum before interpolation, the spatial sampling rate after interpolation must be considered, so it is multiplied by the spatial sampling operator to eliminate false frequencies;

[0096] In this step, discrete Fourier transform is performed on the three-dimensional post-stack seismic data to obtain the initial Fourier spectrum calculation formula:

[0097]

[0098] in, is the initial amplitude spectrum, that is, the initial Fourier spectrum, D inis the amplitude spectrum of the input data, F is the positive Fourier transform, T is the matrix mask data, γ is the tilt weight factor, and p is a non-negative exponential correction term used to control the size of γ;

[0099] The calculation formula for the inclination factor weight of the iterative calculation of the Fourier spectrum is:

[0100] γ(k x ,k y ,f)=A(θ x ,θ y )=∫|D in (θ x ,θ y ,r)|dr

[0101]

[0102] Among them, k x is the data of the main survey line direction after discrete Fourier transform of 3D post-stack seismic data, k y is the data of the tie line direction after the three-dimensional post-stack seismic data is subjected to discrete Fourier transform, f is the frequency domain part after the three-dimensional post-stack seismic data is subjected to discrete Fourier transform; θ x is the effective apparent inclination angle of radial Fourier transform along the main survey line, θ y is the effective apparent inclination angle of the radial Fourier transform along the tie line direction, and A is θ x and θ y Sum of amplitude spectra;

[0103] like Figure 5 The figure shows the comparison of data-driven dip weight factor constrained data reconstruction. It can be seen from the figure that the seismic trace reconstructed by the dip weight factor constrained data is more reasonable;

[0104] S5. Data-driven step-by-step iterative interpolation

[0105] The grid in the three-dimensional grid model of the pseudo three-dimensional superimposed data volume obtained in step S3 is used as the first-level grid, and the size of the first-level grid is iterated step by step in the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume obtained in step S4, so that the size of the next-level grid is adjusted to half of the previous level until it is reduced to the target grid, and the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume after each iteration is interpolated to obtain a pseudo three-dimensional data volume;

[0106] like Figure 6 The figure shows an example of a grid of data-driven step-by-step iterative interpolation in this step. In the figure, during the iteration process, the size of the next-level grid is adjusted to half of the previous level, and the interpolated grid is reduced step by step until it is reduced to the size of the target grid.

[0107] like Figure 7The figure shows the cross-section comparison of the 3D grid model of the pseudo 3D reconstructed data volume before interpolation, the conventional pseudo 3D interpolation, and the pseudo 3D data volume with data-driven step-by-step iterative interpolation. Among them, the conventional pseudo 3D interpolation is a method of directly interpolating without step-by-step processing operations; it can be seen from the figure that the pseudo 3D data volume obtained by data-driven step-by-step iterative interpolation has fewer abnormal channels, more reasonable structure, and richer details;

[0108] like Figure 8 The figure shows the slice comparison of the 3D grid model of the pseudo 3D reconstructed data volume before interpolation, the conventional pseudo 3D interpolation, and the pseudo 3D data volume by data-driven step-by-step iterative interpolation. It can be seen from the figure that the pseudo 3D data slices obtained by data-driven step-by-step iterative interpolation have richer details, especially in the sparse data area at the boundary of the work area, and the structure is more reasonable.

[0109] S6. Establishing a pseudo 3D velocity field through multi-information constraints, performing post-stack migration on the pseudo 3D data volume using the pseudo 3D velocity field, obtaining a migration result, performing post-stack modification and high-resolution processing on the migration result, and realizing data-driven pseudo 3D imaging;

[0110] Among them, when the pseudo 3D velocity field is established by multi-information constraints, the multi-information in the multi-information constraints includes 2D velocity files, adjacent 3D work area velocity fields, logging data and structural steering velocity smoothing parameters; as shown in FIG9(a) and FIG9(b), the pseudo 3D velocity fields before and after smoothing by structural steering velocity smoothing parameters in this step are shown. It can be seen from the figure that after velocity smoothing, the velocity difference between different 2D lines is reduced, and the velocity changes reasonably along the structural direction, which is more beneficial to the later post-stack migration;

[0111] By using the pseudo 3D velocity field to perform post-stack migration on the pseudo 3D data volume, the reflection wave can be returned to its position and the diffraction wave can be converged, and finally the data on the seismic record can be restored to its real spatial position for subsequent interpretation and analysis. As shown in Figures 10(a) and 10(b), the cross-section diagrams of the pseudo 3D data volume before and after post-stack migration are shown. It can be seen from the figures that post-stack migration solves the problem of reflection wave return and the diffraction wave is also converged, which is more conducive to the subsequent interpretation and analysis.

[0112] After the data-driven pseudo 3D imaging is obtained through the above steps, this embodiment can perform uncertainty analysis on the obtained data-driven pseudo 3D imaging, evaluate the reliability and accuracy of the seismic data processing results, determine the impact of various errors and uncertainties on the processing results, and provide corresponding improvement plans; in the evaluation of data-driven pseudo 3D imaging, the uncertainty analysis technology of error analysis is added to analyze the interpolation results to determine whether the interpolated results are reasonable, which can improve the accuracy and reliability of the final results.

[0113] Taking a two-dimensional post-stack time migration section obtained in a block in Indonesia as an example, the obtained two-dimensional post-stack time migration section is processed by the method of this embodiment to obtain the following: Fig.11 The original 3D imaging of the block and the data-driven pseudo 3D imaging obtained by this embodiment are shown as comparison diagrams, wherein the left diagram is the original 3D imaging of the block, and the right diagram is the data-driven pseudo 3D imaging obtained by this embodiment. After comparing the data-driven pseudo 3D imaging obtained by this embodiment with the original 3D imaging of the block, it can be seen that the 2D work area data in the original 3D imaging of the block is very sparse, while the data-driven pseudo 3D imaging obtained by this embodiment has clear structure, rich details, and high precision, which can fully meet the structural interpretation work of the interpreter.

[0114] like Fig.12 The figure shows the superposition of the time slice and uncertainty analysis diagram of the data-driven pseudo 3D imaging in this area. It can be seen from the figure that the uncertainty analysis value of data-driven pseudo 3D imaging is higher in areas with higher data density, proving that the pseudo 3D imaging results in this area are reliable, but the credibility of the interpolation results is lower for areas with sparse data.

[0115] Embodiment 2 A data driven pseudo three-dimensional imaging device

[0116] like Fig.13 FIG. 1 is a block diagram of a data-driven pseudo 3D imaging device provided according to Embodiment 1. The device includes:

[0117] A pseudo three-dimensional data volume module is used to process two-dimensional post-stack migration data to obtain a pseudo three-dimensional data volume;

[0118] A multi-information constrained pseudo 3D velocity field module is used to establish a pseudo 3D velocity field through multi-information constraints;

[0119] Migration module, used to perform post-stack migration on pseudo 3D data volume using pseudo 3D velocity field to obtain migration results;

[0120] Data-driven pseudo 3D imaging is used to perform post-stack modification and high-resolution processing on the migration results to achieve data-driven pseudo 3D imaging.

[0121] like Fig.14 As shown, the pseudo three-dimensional data volume module also includes:

[0122] The 2D post-stack migration data de-migration module is used to obtain the 2D post-stack time migration section of the 2D work area, perform post-stack de-migration on the 2D post-stack time migration section, and obtain the self-excited and self-collected stack section;

[0123] The data matching and three-dimensional gridding module is used to perform high signal-to-noise ratio processing and Fourier transform on the obtained superimposed sections of self-excited and self-received data to obtain the superimposed sections after data matching; the superimposed sections after data matching are subjected to three-dimensional gridding processing to obtain a three-dimensional gridding model of the superimposed sections;

[0124] The sparse data interpolation module is used for determining the internal interpolation boundary and the external interpolation boundary of the stacked section 3D grid model according to the line distance if the line distance value of the sparse data area is greater than the set value, and performing interpolation on the grids in the internal interpolation boundary area to obtain a pseudo 3D stacked data volume 3D grid model;

[0125] A data-driven dip weight factor constrained data reconstruction module is used to perform noise reduction processing on a three-dimensional grid model of a pseudo three-dimensional stacked data volume, obtain pseudo three-dimensional post-stack seismic data, perform discrete Fourier transform on the three-dimensional post-stack seismic data, obtain an initial Fourier spectrum, iteratively calculate the dip factor weight of the Fourier spectrum, select the Fourier spectrum component with the maximum energy after weighting according to the dip factor weight, add the Fourier spectrum component to the estimated spectrum, then multiply it with the spatial sampling operator, and subtract the product result from the Fourier spectrum to update the Fourier spectrum, iterate repeatedly, and finally perform inverse Fourier transform on the estimated spectrum and output it to a desired position, thereby completing the reconstruction of the dip factor reflection and obtaining a three-dimensional grid model of a pseudo three-dimensional reconstructed data volume;

[0126] A data-driven step-by-step iterative interpolation module is used to use the grids in the obtained three-dimensional grid model of the pseudo three-dimensional superimposed data volume as the first-level grids, and to iterate the size of the first-level grids in the obtained three-dimensional grid model of the pseudo three-dimensional reconstructed data volume step by step so that the size of the next-level grid is adjusted to half of the previous level, and to interpolate the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume after each iteration to obtain a pseudo three-dimensional data volume.

[0127] The data-driven pseudo 3D imaging device provided in this embodiment only uses the division of the above functional modules as an example to illustrate when performing data processing. In actual applications, the above functions can be assigned to different functional modules as needed.

[0128] Based on the same inventive concept, Fig.15 As shown, this embodiment further provides a computer device, including: at least a processor and a memory, the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor and executes the data-driven pseudo three-dimensional imaging method of embodiment 1.

[0129] Based on the same inventive concept, Fig.16As shown, this embodiment further provides a computer-readable storage medium, the storage medium is used to store at least one computer program, and the at least one computer program is used to execute the data-driven pseudo three-dimensional imaging method of embodiment 1.

[0130] Finally, it should be noted that a person skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0131] Furthermore, it should be appreciated that the computer-readable storage medium (eg, memory) herein may be either a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0132] It will also be appreciated by those skilled in the art that various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, a general description has been given to the functions of various schematic components, blocks, modules, circuits and steps. Whether this function is implemented as software or hardware depends on specific applications and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but this implementation decision should not be interpreted as causing a departure from the disclosed scope of the embodiments of the present invention.

[0133] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data-driven pseudo three-dimensional imaging method, characterized in that: The following steps are involved: The two-dimensional post-stack migration data is processed to obtain a pseudo-three-dimensional data volume, a pseudo-three-dimensional velocity field is established through multi-information constraints, the pseudo-three-dimensional data volume is post-stack migrated using the pseudo-three-dimensional velocity field, the migration result is obtained, the migration result is post-stack modified and high-resolution processed, and data-driven pseudo-three-dimensional imaging is realized; The method of processing the two-dimensional post-stack migration data to obtain a pseudo three-dimensional data volume comprises the following steps: S1, 2D post-stack migration data de-migration Obtain a 2D post-stack time migration section of a 2D work area, perform post-stack de-migration on the 2D post-stack time migration section, and obtain a self-excited and self-received stack section; S2. Data matching and 3D meshing The obtained self-excited and self-received superimposed sections are subjected to high signal-to-noise ratio processing and Fourier transformation to obtain a superimposed section after data matching; the superimposed section after data matching is subjected to three-dimensional grid processing to obtain a three-dimensional grid model of the superimposed section; S3, sparse data interpolation For the stacked section 3D grid model, if the line distance value of the sparse data area is greater than the set value, the internal inter-trace interpolation boundary and the external inter-trace interpolation boundary of the stacked section 3D grid model are determined according to the line distance, and the grids in the internal inter-trace interpolation boundary area are interpolated to obtain the pseudo 3D stacked data volume 3D grid model; The line distance is the distance between two parallel two-dimensional lines; S4. Data-driven inclination weight factor constrains data reconstruction The three-dimensional grid model of the pseudo three-dimensional stacked data volume is subjected to denoising to obtain pseudo three-dimensional post-stack seismic data, the three-dimensional post-stack seismic data is subjected to discrete Fourier transform to obtain an initial Fourier spectrum, the dip factor weight of the Fourier spectrum is iteratively calculated, the Fourier spectrum component with the maximum energy after weighting according to the dip factor weight is selected, the Fourier spectrum component is added to the estimated spectrum, and then multiplied with the spatial sampling operator, and the product result is subtracted from the Fourier spectrum to update the Fourier spectrum, and it is iterated repeatedly, and finally the estimated spectrum is subjected to inverse Fourier transform and output to the desired position, so as to complete the reconstruction of the dip factor reflection and obtain the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume; S5. Data-driven step-by-step iterative interpolation The grid in the three-dimensional grid model of the pseudo three-dimensional superimposed data volume obtained in step S3 is used as the first-level grid, and the size of the first-level grid is iterated step by step in the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume obtained in step S4, so that the size of the next-level grid is adjusted to half of the previous level until it is reduced to the target grid, and the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume after each iteration is interpolated to obtain a pseudo three-dimensional data volume.

2. The data-driven pseudo 3D imaging method according to claim 1, characterized in that: The multi-information in the multi-information constraint includes two-dimensional velocity files, velocity fields of adjacent three-dimensional work areas, logging data and structural steering velocity smoothing parameters.

3. The data-driven pseudo 3D imaging method according to claim 1, characterized in that: In step S1, a finite difference method is used to perform post-stack de-migration on the two-dimensional post-stack time migration profile.

4. The data-driven pseudo 3D imaging method according to claim 1, characterized in that: In step S2, when the stacked section after data matching is processed into a three-dimensional grid, the size of the grid is: Wherein, Line gap represents the line distance between adjacent two-dimensional lines.

5. The data-driven pseudo 3D imaging method according to claim 1, characterized in that: In step S5, discrete Fourier transform is performed on the three-dimensional post-stack seismic data to obtain the initial Fourier spectrum calculation formula: in, is the initial amplitude spectrum, that is, the initial Fourier spectrum, D in is the amplitude spectrum of the input data, F is the positive Fourier transform, T is the matrix mask data, γ is the tilt weight factor, and p is a non-negative exponential correction term used to control the size of γ; The calculation formula for the inclination factor weight of the iterative calculation of the Fourier spectrum is: c(k x ,k y ,f)=A(θ x ,i y )=∫|D in (i x ,i y ,r)|dr Among them, k x is the data of the main survey line direction after discrete Fourier transform of 3D post-stack seismic data, k y is the data of the tie line direction after the three-dimensional post-stack seismic data is subjected to discrete Fourier transform, f is the frequency domain part after the three-dimensional post-stack seismic data is subjected to discrete Fourier transform; θ x is the effective apparent inclination angle of radial Fourier transform along the main survey line, θ y is the effective apparent inclination angle of the radial Fourier transform along the tie line direction, and A is θ x and θ y The sum of the amplitude spectra.

6. A data-driven pseudo three-dimensional imaging device, characterized in that: include: A pseudo three-dimensional data volume module is used to process two-dimensional post-stack migration data to obtain a pseudo three-dimensional data volume; A multi-information constrained pseudo 3D velocity field module is used to establish a pseudo 3D velocity field through multi-information constraints; Migration module, used to perform post-stack migration on pseudo 3D data volume using pseudo 3D velocity field to obtain migration results; Data-driven pseudo 3D imaging is used to perform post-stack modification and high-resolution processing on the migration results to achieve data-driven pseudo 3D imaging.

7. The data driven pseudo 3D imaging device according to claim 6, characterized in that: The pseudo three-dimensional data volume module also includes: The 2D post-stack migration data de-migration module is used to obtain the 2D post-stack time migration section of the 2D work area, perform post-stack de-migration on the 2D post-stack time migration section, and obtain the self-excited and self-collected stack section; The data matching and three-dimensional gridding module is used to perform high signal-to-noise ratio processing and Fourier transform on the obtained superimposed sections of self-excited and self-received data to obtain the superimposed sections after data matching; the superimposed sections after data matching are subjected to three-dimensional gridding processing to obtain a three-dimensional gridding model of the superimposed sections; The sparse data interpolation module is used for determining the internal interpolation boundary and the external interpolation boundary of the stacked section 3D grid model according to the line distance if the line distance value of the sparse data area is greater than the set value, and performing interpolation on the grids in the internal interpolation boundary area to obtain a pseudo 3D stacked data volume 3D grid model; A data-driven dip weight factor constrained data reconstruction module is used to perform noise reduction processing on a three-dimensional grid model of a pseudo three-dimensional stacked data volume, obtain pseudo three-dimensional post-stack seismic data, perform discrete Fourier transform on the three-dimensional post-stack seismic data, obtain an initial Fourier spectrum, iteratively calculate the dip factor weight of the Fourier spectrum, select the Fourier spectrum component with the maximum energy after weighting according to the dip factor weight, add the Fourier spectrum component to the estimated spectrum, then multiply it with the spatial sampling operator, and subtract the product result from the Fourier spectrum to update the Fourier spectrum, iterate repeatedly, and finally perform inverse Fourier transform on the estimated spectrum and output it to a desired position, thereby completing the reconstruction of the dip factor reflection and obtaining a three-dimensional grid model of a pseudo three-dimensional reconstructed data volume; A data-driven step-by-step iterative interpolation module is used to use the grids in the obtained three-dimensional grid model of the pseudo three-dimensional superimposed data volume as the first-level grids, and to iterate the size of the first-level grids in the obtained three-dimensional grid model of the pseudo three-dimensional reconstructed data volume step by step so that the size of the next-level grid is adjusted to half of the previous level, and to interpolate the three-dimensional grid model of the pseudo three-dimensional reconstructed data volume after each iteration to obtain a pseudo three-dimensional data volume.

8. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor and executes the data-driven pseudo three-dimensional imaging method according to any one of claims 1 to 5.

9. A storage medium, characterized in that: The storage medium is used to store at least one computer program, and the at least one computer program is used to execute the data-driven pseudo three-dimensional imaging method according to any one of claims 1 to 5.

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