Method and device for improving illuminance by using spatial deconvolution operator, and storage medium
By using spatial deconvolution operators, the problem of uneven illumination in migration imaging during oil exploration is solved, achieving high-efficiency, low-cost, high-quality imaging that is suitable for complex geological structures and irregular observation systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-08
- Publication Date
- 2026-04-10
AI Technical Summary
Existing technologies are difficult to effectively solve the problem of uneven illumination in migration imaging in oil exploration, especially in complex media and irregular observation systems, which leads to a decrease in imaging quality and high computational costs, making it difficult to apply on a large scale.
By employing a spatial deconvolution operator, and through pre-stack depth migration, forward modeling, and spatial deconvolution processing, we can compensate for illumination inhomogeneities on the migrated profile, improve imaging resolution and amplitude uniformity, and reduce computational workload.
It achieves high-efficiency, low-cost, high-quality imaging, overcomes uneven illumination, improves imaging resolution and amplitude uniformity, is suitable for complex geological structures and irregular observation systems, and reduces computational costs and processing cycles.
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Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of seismic data processing in oil exploration, and relates to a method for improving illumination by using a spatial deconvolution operator, and also relates to a device for improving illumination by using a spatial deconvolution operator and a storage medium. BACKGROUND
[0002] The acquisition of seismic data in oil exploration is to record reflected waves reaching the ground in a certain way, and the analysis of seismic data is to process the observation data by using a computer according to a certain calculation method, so that the reflected interface image reflecting the position of the stratified face and the reflection coefficient value of the underground is obtained, and the migration imaging technology is a technology for best imaging of the reflected interface.
[0003] With the oil and gas exploration target moving towards double complex areas and super deep layers, the demand for high-precision migration imaging is increasing, and uneven illumination in migration imaging is a key factor affecting the quality of migration imaging, and uneven illumination is mainly caused by the irregularity of the observation and acquisition system, incomplete sampling and the influence of complex underground structure on wave field propagation, therefore, improving uneven illumination is the key to improving imaging quality.
[0004] The current migration imaging technology mainly includes two categories of ray-based migration and wave field continuation migration, wherein the ray-based migration is a high-frequency approximation assumption of the wave equation, is greatly affected by complex media, and thus is difficult to accurately image; the wave field continuation migration has better illumination effect in theory and has the ability of accurate imaging, but the calculation amount is huge. The least square migration technology based on linear seismic inversion theory can theoretically solve the problem of uneven migration illumination, improve the imaging resolution and amplitude balance, but needs to be iterated through forward and inversion, and the calculation cost is high, which is dozens of times higher than that of conventional migration, and is difficult to be widely applied. SUMMARY
[0005] The purpose of the present application is to provide a method for improving illumination by using a spatial deconvolution operator, so as to improve the imaging resolution and amplitude balance by improving the illumination, reduce the calculation and storage cost, and improve the calculation efficiency; The second purpose of the present application is to provide a device for improving illumination by using a spatial deconvolution operator, which is used for outputting high-quality imaging results with clear illumination; The third purpose of the present application is to provide a computer readable storage medium for storing the corresponding computer program of the method for improving illumination by using a spatial deconvolution operator.
[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is: A method for improving illumination by using a spatial deconvolution operator, comprising the following steps, S1: Obtain the layer velocity model of the subsurface medium, perform pre-stack depth migration, obtain pre-stack depth migration imaging data volume, generate migration profile, and determine multiple different analysis control points at different spatial and depth locations; S2: Scale the pre-stack depth migration imaging data to the time domain, denoise the data, improve the signal-to-noise ratio, and select locations with relatively high signal-to-noise ratio and continuity of the reflection phase axis to extract seismic wavelets. A relatively high signal-to-noise ratio means that the signal-to-noise ratio of the reflection in-phase axis is not less than 3. Relatively high continuity means that there are in-phase axes that can be continuously tracked from shallow to deep; S3: Using the extracted seismic wavelet and the layer velocity model of S1, forward modeling of the wave equation is performed according to the observation system of the imaging data volume using the finite difference method to obtain forward modeling data; S4: Using the obtained forward modeling data and the layer velocity model of S1, pre-stack depth migration processing is performed on the positions of each analysis control point in S1 to obtain the migration results at each analysis control point. S5: Taking each analysis control point as the center, select seismic traces within a spatial range smaller than the analysis control point interval to calculate the root mean square amplitude of each analysis control point, forming multiple functions of spatial position and amplitude, and obtaining the spatial wavelet of each analysis control point. S6: Set a symmetrical desired spatial wavelet for the entire region. The main lobe width of the desired spatial wavelet should be smaller than the main lobe width of the spatial wavelet at each analysis control point. Use the desired spatial wavelet as the objective function to obtain the inverse of the spatial wavelet at each analysis control point, i.e., the spatial deconvolution operator. S7: Perform convolution processing using the offset profile generated in S1 and the spatial deconvolution operator generated in S6 to obtain the final imaging result.
[0007] As a limitation of the present invention, in S1, the layer velocity model of the underground medium is obtained by depth migration velocity analysis technology.
[0008] As a further limitation of the present invention, in S1, pre-stack depth migration is performed using ray-based or wave field extension methods.
[0009] As another limitation of the present invention, in S1, positions at different spatial and depth levels are selected at equal intervals to obtain different analysis control points, so that the selected analysis control points can cover the entire imaging data volume.
[0010] A device for calculating the aspect ratio of a three-dimensional observation system, comprising the apparatus for improving illumination using the spatial deconvolution operator as described in any one of the above claims: Pre-stack depth migration device: Performs pre-stack depth migration on the layer velocity model of the subsurface medium to obtain imaging data volume and generate migration profile; Seismic wavelet extraction device: The pre-stack depth migration imaging data is scaled up to the time domain, denoised, and then the signal-to-noise ratio of the data is processed to extract the seismic wavelet. Forward modeling data calculation device: The seismic wavelet layer velocity model is forward modeled using the wave equation finite difference method according to the observation system of the original data to obtain forward modeling data; Migration result calculation device: Using forward modeling data and layer velocity model, pre-stack depth migration processing is performed on the positions of each analysis control point to obtain the migration results at each analysis control point; Space wavelet extraction device: Select seismic traces within a certain spatial range to calculate the root mean square amplitude of each analysis control point, forming multiple functions of spatial position and amplitude, and obtain the space wavelet of each analysis control point; Spatial deconvolution operator computational device: A symmetrical desired spatial wavelet is set in the whole area. The desired spatial wavelet is used as the objective function. The inverse of the spatial wavelet at each analysis control point is obtained to obtain the spatial deconvolution operator. Imaging device: The offset profile and spatial deconvolution operator are convolved to obtain the final imaging result and output a visualized image.
[0011] A computer-readable storage medium storing a computer program that performs any of the above-described methods for improving illumination using a spatial deconvolution operator.
[0012] By adopting the above technical solution, the technical progress achieved by this invention compared with the prior art is as follows: This invention further processes pre-stack depth migration by employing forward modeling to calculate the wavefield. Through migration processing, spatial wavelets at different spatial analysis control points in the longitudinal and transverse directions on the migration profile are obtained. Then, spatial deconvolution is performed on the spatial wavelets, and the resulting illumination compensation operator is applied to the conventional migration results to obtain improved seismic imaging. This invention focuses on a novel technical process for compensating for discrepancies in erroneous migration imaging. This processing method improves imaging resolution and amplitude fidelity, is flexible and efficient, and has a low cost. It also avoids repeated iterations, significantly reducing computational workload and shortening the processing cycle. It can improve illumination imbalances caused by irregular observation systems and complex media, and can be widely applied to practical production, demonstrating high economic value and strong practicality.
[0013] In summary, this invention offers high imaging resolution, low computational workload, short processing cycle, and strong practicality, making it suitable for the field of seismic exploration. Attached Figure Description
[0014] Figure 1 This is the seismic wavelet map extracted from the pre-stack depth migration data volume in Embodiment 1 of the present invention; Figure 2 This is the forward modeling data diagram from Embodiment 1 of the present invention; Figure 3 This is a diagram showing the offset results at each analysis control point in Embodiment 1 of the present invention; Figure 4 This is a process diagram of extracting spatial wavelets at different positions in Embodiment 1 of the present invention. In this diagram, a is the offset result diagram at each analysis control point, b is an enlarged view of the box position in a, and c is the spatial wavelet diagram extracted from the five analysis control points in b. Figure 5 It is the symmetric expected space wavelet diagram set in Embodiment 1 of the present invention; Figure 6 These are comparison images of imaging using existing technology and imaging using the method of this embodiment in Embodiment 1 of the present invention. In this image, a is the imaging result using existing technology, b is the slice image using existing technology, c is the imaging result using the method of this embodiment, and d is the slice image using the method of this embodiment. Detailed Implementation
[0015] The present invention will be further described in detail below through specific embodiments. It should be understood that the described embodiments are only for explaining the present invention and do not limit the present invention.
[0016] Example 1: A method for improving illumination using a spatial deconvolution operator This embodiment extracts seismic wavelets from seismic data, performs forward modeling according to the observation system and velocity model, migrates the forward modeling data to obtain spatial wavelets at the analysis control points, then sets the expectation function, obtains the spatial deconvolution operator, and uses the spatial deconvolution operator to perform convolution calculations on the conventional migration profile to obtain a final result with amplitude balance and high resolution.
[0017] This embodiment was applied to a three-dimensional block in a marine-continental transition zone. Due to obstacles such as fishing grounds, factories, and residential areas on the surface, the layout of the observation system's shot-receiver points was extremely uneven. Furthermore, the geological structure was complex with well-developed underground faults, resulting in uneven energy distribution in conventional migration imaging. The following details how the method described in this embodiment obtains high-quality imaging results and reduces computational load in the process.
[0018] S1: A layer velocity model of the subsurface medium is obtained through depth migration velocity analysis. Pre-stack depth migration is then performed using ray-based or wavefield extrapolation methods to obtain a pre-stack depth migration imaging volume and generate a migration profile. This is the existing method, and the process ends after generating the migration profile. However, the imaging results of this profile are significantly affected by illumination. This embodiment further processes this process by determining multiple different analysis control points on the migration profile and continuing with steps S2-S7 below. The principle for determining the analysis control points is to select them at equal intervals at different spatial and depth locations, ensuring that the selected analysis control points uniformly cover the imaging volume.
[0019] S2: The pre-stack depth migration imaging data is scaled up to the time domain, and denoising is performed to improve the signal-to-noise ratio. Locations with relatively high signal-to-noise ratio and continuity along the reflection phase axis are selected to extract seismic wavelets, such as... Figure 1 As shown in the figure, this is a seismic wavelet extracted from the pre-stack depth migration data volume. The dominant frequency of the seismic wavelet is 20 Hz, which is consistent with the dominant frequency of the imaging data volume. The sampling interval is 2 ms, which can be used for subsequent processing. Figure 1 It can be seen from this that the wavelet length is 100ms.
[0020] The relatively high signal-to-noise ratio in this embodiment means that the signal-to-noise ratio of the reflection in-phase axis is not less than 3. The relatively high continuity in this embodiment means that there are in-phase axes that can be continuously tracked from shallow to deep.
[0021] S3: Using the extracted seismic wavelet and the layer velocity model of S1, forward modeling of the wave equation using the finite difference method is performed according to the observation system of the imaging data volume, resulting in the following... Figure 2 The forward modeling data shown is from Figure 2 As can be seen, the forward modeling data has a trace length of 6 seconds and a maximum offset of 5000 meters, both of which are consistent with the imaging data volume and can be used for subsequent processing.
[0022] S4: Using the obtained forward modeling data and the layer velocity model of S1, pre-stack depth migration is performed on the locations of each analysis control point selected in S1 to obtain the following results: Figure 3 The offset results at each analysis control point shown are from... Figure 3 As can be seen, there are significant differences in the energy of the migration results at different analysis control points, and further processing is needed to eliminate these energy differences.
[0023] S5: Using each analysis control point as the center, select seismic traces within a spatial range smaller than the interval between analysis control points to calculate the root mean square amplitude of each analysis control point, forming multiple functions of spatial location and amplitude, thus obtaining the spatial wavelet of each analysis control point. In this embodiment, the interval between analysis control points is 400 meters, and seismic traces within a spatial range of 200 meters are selected for calculation. Figure 4 As shown, spatial wavelets were extracted from the migration results at five analysis control points, resulting in five types of spatial wavelets. Spatial wavelets were extracted from other analysis control points using the same method. Figure 4 It can be seen that there are differences in the spatial wavelet energy and main lobe width at different locations, and these differences need to be eliminated through further processing.
[0024] S6: As Figure 5 As shown, a symmetrical desired spatial wavelet is set for the entire region. The desired spatial wavelet has uniform energy. The main lobe width of the desired spatial wavelet should be smaller than the main lobe width of the spatial wavelet at each analysis control point. The desired spatial wavelet is used as the objective function to obtain the inverse of the spatial wavelet at each analysis control point, i.e., the spatial deconvolution operator.
[0025] S7: Perform convolution using the offset profile generated in S1 and the spatial deconvolution operator generated in S6 to obtain the final imaging result. For example... Figure 6 As shown, the left side displays the imaging results and slice images of a three-dimensional block in a land-sea transition zone obtained without applying the method of this embodiment. Figure 6 As can be seen from a: due to the irregularity of the observation system and the influence of the complex medium, the amplitude energy of the migration imaging results is uneven, and the fault location is not clear or accurate enough; from Figure 6 As can be seen in b, the structural orientation and fault location of the slice are not clear enough, and the energy is not uniform. The right side shows the imaging results and slice images obtained using the method of this embodiment for a three-dimensional block of the marine-continental transition zone. Figure 6 As can be seen from c in the figure, after processing by this method, due to the compensation for the influence of irregularity in the observation system and complex media on illumination, the pre-stack depth migration imaging results have more balanced amplitude, better energy focusing, better spatial consistency, and clearer fault structure; from Figure 6 As can be seen from image d, the clarity of the structural trend and fault location is significantly improved, and the energy is more balanced. The image comparison results show that after applying the method of this embodiment, the imaging results have higher resolution and clearer fault structures, overcoming the uneven illumination phenomenon caused by the irregularity of the observation system.
[0026] After processing by the method in this embodiment, the influence of irregular and complex media on illumination in the observation system is compensated, the amplitude of the pre-stack depth migration imaging results is more balanced, the energy is focused, the spatial consistency is better, and the fault structure is clearer.
[0027] Example 2: A device for improving illumination using a spatial deconvolution operator This embodiment is used to implement the improved method of Embodiment 1, including a pre-stack depth migration device, a seismic wavelet extraction device, a forward modeling data calculation device, a migration result calculation device, a spatial wavelet extraction device, a spatial deconvolution operator calculation device, and an imaging device.
[0028] Pre-stack depth migration device: Performs pre-stack depth migration on the layer velocity model of the subsurface medium to obtain imaging data volume and generate migration profile.
[0029] Seismic wavelet extraction device: The pre-stack depth migration imaging data is scaled up to the time domain, denoised, and then the signal-to-noise ratio of the data is processed to extract the seismic wavelet.
[0030] Forward modeling data calculation device: The seismic wavelet layer velocity model is forward modeled using the wave equation finite difference method according to the observation system of the original data to obtain forward modeling data.
[0031] Migration result calculation device: Using forward modeling data and layer velocity model, pre-stack depth migration processing is performed on the positions of each analysis control point to obtain the migration results at each analysis control point.
[0032] Space wavelet extraction device: Select seismic traces within a certain spatial range to calculate the root mean square amplitude of each analysis control point, forming multiple functions of spatial position and amplitude, and obtain the space wavelet of each analysis control point.
[0033] Spatial deconvolution operator computational device: A symmetrical desired spatial wavelet is set for the entire region. The desired spatial wavelet is used as the objective function. The inverse of the spatial wavelet at each analysis control point is obtained to obtain the spatial deconvolution operator.
[0034] Imaging device: The offset profile and spatial deconvolution operator are convolved to obtain the final imaging result and output a visualized image.
[0035] Example 3: Computer-readable storage medium The computer-readable storage medium in this embodiment stores a computer program, which, when executed by a processor, is used to implement the method of improving illumination using a spatial deconvolution operator as described in Embodiment 1.
[0036] The computer-readable storage medium stores non-transitory computer-readable instructions thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments are performed.
[0037] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
[0038] The above description is merely an optional embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.
Claims
1. A method for improving illumination using a spatial deconvolution operator, characterized in that, Includes the following steps, S1: Obtain the layer velocity model of the subsurface medium, perform pre-stack depth migration, obtain pre-stack depth migration imaging data volume, generate migration profile, and determine multiple different analysis control points at different spatial and depth locations; S2: The pre-stack depth migration imaging data volume is scaled up to the time domain, the data is denoised to improve the signal-to-noise ratio, and the location with relatively high signal-to-noise ratio and continuity of the reflection phase axis is selected to extract the seismic wavelet. A relatively high signal-to-noise ratio means that the signal-to-noise ratio of the reflection in-phase axis is not less than 3. Relatively high continuity means that there are in-phase axes that can be continuously tracked from shallow to deep; S3: Using the extracted seismic wavelet and the layer velocity model of S1, forward modeling of the wave equation is performed according to the observation system of the imaging data volume using the finite difference method to obtain forward modeling data; S4: Using the obtained forward modeling data and the layer velocity model of S1, pre-stack depth migration processing is performed on the positions of each analysis control point in S1 to obtain the migration results at each analysis control point. S5: Taking each analysis control point as the center, select seismic traces within a spatial range smaller than the analysis control point interval to calculate the root mean square amplitude of each analysis control point, forming multiple functions of spatial position and amplitude, and obtaining the spatial wavelet of each analysis control point; S6: Set a symmetrical desired spatial wavelet for the entire region. The main lobe width of the desired spatial wavelet should be smaller than the main lobe width of the spatial wavelet at each analysis control point. Use the desired spatial wavelet as the objective function to obtain the inverse of the spatial wavelet at each analysis control point, i.e., the spatial deconvolution operator. S7: Perform convolution processing using the offset profile generated in S1 and the spatial deconvolution operator generated in S6 to obtain the final imaging result.
2. The method for improving illumination using a spatial deconvolution operator according to claim 1, characterized in that, In S1, the layer velocity model of the subsurface medium is obtained through depth migration velocity analysis technology.
3. The method for improving illumination using a spatial deconvolution operator according to claim 2, characterized in that, In S1, pre-stack depth migration is performed using ray-based or wavefield extension methods.
4. The method for improving illumination using a spatial deconvolution operator according to claim 2, characterized in that, In S1, positions at different spatial and depth levels are selected at equal intervals to obtain different analysis control points, so that the selected analysis control points can cover the entire imaging data volume.
5. A device for improving illumination using a spatial deconvolution operator, characterized in that, The apparatus for using the method of improving illumination by spatial deconvolution operator according to any one of claims 1-4 comprises: Pre-stack depth migration device: Performs pre-stack depth migration on the layer velocity model of the subsurface medium to obtain imaging data volume and generate migration profile; Seismic wavelet extraction device: The pre-stack depth migration imaging data is scaled up to the time domain, denoised, and then the signal-to-noise ratio of the data is processed to extract the seismic wavelet. Forward modeling data calculation device: The seismic wavelet layer velocity model is forward modeled using the wave equation finite difference method according to the observation system of the original data to obtain forward modeling data; Migration result calculation device: Using forward modeling data and layer velocity model, pre-stack depth migration processing is performed on the positions of each analysis control point to obtain the migration results at each analysis control point; Space wavelet extraction device: Select seismic traces within a certain spatial range to calculate the root mean square amplitude of each analysis control point, forming multiple functions of spatial position and amplitude, and obtain the space wavelet of each analysis control point; Spatial deconvolution operator computational device: A symmetrical desired spatial wavelet is set in the whole area. The desired spatial wavelet is used as the objective function. The inverse of the spatial wavelet at each analysis control point is obtained to obtain the spatial deconvolution operator. Imaging device: The offset profile and spatial deconvolution operator are convolved to obtain the final imaging result and output a visualized image.
6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs the method for improving illumination using a spatial deconvolution operator as described in any one of claims 1-4.