Thin sand body identification method and device based on multi-wavelet decomposition, electronic equipment and medium
By using the multi-wavelet decomposition method to identify thin sand bodies, the problems of insufficient resolution and tuning effect interference in the existing technology are solved, and high-precision thin sand body identification is achieved.
Patent Information
- Application Number
- CN202411301971.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-18
- Publication Date
- 2026-03-20
AI Technical Summary
Existing technologies suffer from limited resolution and tuning effect interference in thin sand body recognition, resulting in insufficient recognition accuracy.
The multi-wavelet decomposition method is adopted. By preprocessing and multi-wavelet decomposition of the pre-stack angle gather, combined with well data analysis to screen sensitive frequency bands and angles, multi-wavelet reconstruction and partial superposition are performed to extract the sensitive attributes of sand bodies for identification.
It effectively avoids interference from the tuning effect, improves the identification accuracy and continuity of thin sandstone reservoirs, and increases computational efficiency.
Smart Images
Figure CN121703933A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of geophysical exploration of oil and gas, and more specifically, to a method, apparatus, electronic device and medium for identifying thin sand bodies based on multi-wavelet decomposition. Background Technology
[0002] With the development of oil and gas exploration and development, thin sandstone reservoirs have gradually become one of the main battlegrounds for increasing oil reserves and production. However, due to the limited resolution of seismic data, there are often "illusions" in sandstone identification, making thin sandstone identification a key and difficult point in geophysics. The commonly used thin sandstone prediction methods are mainly as follows: (1) Thin sandstone identification method based on seismic attributes: Sandstone identification is performed by selecting seismic attributes that are sensitive to thin sandstones. However, due to the complex seismic reflection characteristics of thin sandstones, it is difficult to obtain attributes that are sensitive to thin sandstones, resulting in limited accuracy in thin sandstone identification; (2) Thin sandstone identification method based on seismic inversion: Elastic parameters that are sensitive to sandstones are obtained through rock physics analysis, and sandstone identification is performed using the seismic inversion results. This method is limited by factors such as the resolution of seismic data and tuning effects, resulting in limited accuracy in thin sandstone identification. Since the above methods for thin sandstone identification all have certain limitations.
[0003] Therefore, it is necessary to develop a method, device, electronic device, and medium for identifying thin sand bodies based on multi-wavelet decomposition.
[0004] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention
[0005] This invention proposes a method, device, electronic device, and medium for identifying thin sand bodies based on multi-wavelet decomposition. It makes full use of frequency components that are sensitive to thin sand bodies, effectively avoids the interference of tuning effects on the identification of thin sand bodies, and achieves high-precision identification of thin sand body reservoirs.
[0006] In a first aspect, embodiments of this disclosure provide a method for identifying thin sand bodies based on multi-wavelet decomposition, including:
[0007] The pre-stack angle gather is preprocessed and then multi-wavelet decomposition is performed.
[0008] By comparing and analyzing well data with actual decomposed gathers, frequency bands sensitive to thin sandstone reservoirs were selected.
[0009] Multi-wavelet reconstruction is performed on the data after multi-wavelet decomposition to obtain the seismic angle gather data volume;
[0010] Select advantageous angles based on the aforementioned seismic angle gather data;
[0011] Based on the aforementioned advantageous angles, partial superposition is performed to obtain the superimposed seismic data;
[0012] Based on the superimposed seismic data, sensitive attributes of sand bodies are extracted for thin sand body identification.
[0013] As a specific implementation of this disclosure, preprocessing of the pre-stack corner gather includes:
[0014] The pre-stack angle gather data is subjected to noise suppression and layer flattening optimization processing, and data quality control is performed based on the characteristics of the marker layer wave group, the stacking profile, and the AVO characteristics of the target layer.
[0015] As a specific implementation of this disclosure, multi-wavelet decomposition includes:
[0016] A seismic wavelet library was constructed, and multi-wavelet decomposition was performed on the pre-stack angle gathers based on the matching pursuit algorithm.
[0017] As one specific implementation of this disclosure, the seismic wavelet library is constructed using the Lake wavelet.
[0018] As a specific implementation of this disclosure, the search space of the matching pursuit algorithm is narrowed by the distribution characteristics of Gaussian distribution, and a genetic algorithm is applied to the optimal wavelet matching in the matching pursuit algorithm.
[0019] As a specific implementation of this disclosure, multi-wavelet decomposition is performed using a convolution model:
[0020]
[0021] Among them, w i Let R represent the i-th subwavelet. i Let N(t) represent the reflection coefficient corresponding to the i-th wavelet, and let N(t) represent noise.
[0022] As a specific implementation of this disclosure, the selection of advantageous angles based on the seismic angle gather data volume includes:
[0023] Based on the reservoir segment AVO characteristics of the seismic angle gather data, the angle range sensitive to sand bodies is determined.
[0024] Secondly, embodiments of this disclosure also provide a thin sand body identification device based on multi-wavelet decomposition, comprising:
[0025] The decomposition module preprocesses the pre-stack angle gathers and then performs multi-wavelet decomposition.
[0026] The comparison module uses comparative analysis of well data and actual decomposed gathers to screen frequency bands sensitive to thin sandstone reservoirs.
[0027] The reconstruction module performs multi-wavelet reconstruction on the data after multi-wavelet decomposition to obtain the seismic angle gather data volume.
[0028] The filtering module filters advantageous angles based on the seismic angle gather data.
[0029] The overlay module performs partial overlay based on the advantageous angle to obtain the overlaid seismic data.
[0030] The identification module extracts sensitive attributes of sand bodies based on the superimposed seismic data to identify thin sand bodies.
[0031] As a specific implementation of this disclosure, preprocessing of the pre-stack corner gather includes:
[0032] The pre-stack angle gather data is subjected to noise suppression and layer flattening optimization processing, and data quality control is performed based on the characteristics of the marker layer wave group, the stacking profile, and the AVO characteristics of the target layer.
[0033] As a specific implementation of this disclosure, multi-wavelet decomposition includes:
[0034] A seismic wavelet library was constructed, and multi-wavelet decomposition was performed on the pre-stack angle gathers based on the matching pursuit algorithm.
[0035] As one specific implementation of this disclosure, the seismic wavelet library is constructed using the Lake wavelet.
[0036] As a specific implementation of this disclosure, the search space of the matching pursuit algorithm is narrowed by the distribution characteristics of Gaussian distribution, and a genetic algorithm is applied to the optimal wavelet matching in the matching pursuit algorithm.
[0037] As a specific implementation of this disclosure, multi-wavelet decomposition is performed using a convolution model:
[0038]
[0039] Among them, w i Let R represent the i-th subwavelet. i Let N(t) represent the reflection coefficient corresponding to the i-th wavelet, and let N(t) represent noise.
[0040] As a specific implementation of this disclosure, the selection of advantageous angles based on the seismic angle gather data volume includes:
[0041] Based on the reservoir segment AVO characteristics of the seismic angle gather data, the angle range sensitive to sand bodies is determined.
[0042] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:
[0043] Memory, which stores executable instructions;
[0044] A processor that executes the executable instructions in the memory to implement the thin sand body identification method based on multi-wavelet decomposition.
[0045] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned thin sand body identification method based on multi-wavelet decomposition.
[0046] Its beneficial effects are as follows:
[0047] (1) This invention utilizes a genetic algorithm to guide the search for the optimal wavelet in the matching pursuit algorithm, efficiently decomposes the pre-stack angle gather data into multiple wavelets, and effectively screens out the frequency range sensitive to thin sandstone reservoirs through comparative analysis of well data and decomposed gathers, thereby effectively improving the computational efficiency of the algorithm.
[0048] (2) This invention applies AVO analysis technology to gathers after multi-wavelet decomposition and reconstruction, which can help screen out the angle range that is sensitive to thin sand bodies, providing a solid data foundation for subsequent thin sand body identification.
[0049] (3) The present invention makes full use of the frequency components that are sensitive to thin sand bodies, which can effectively avoid the interference of the tuning effect on the identification of thin sand bodies and realize the high-precision identification of thin sand body reservoirs.
[0050] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description
[0051] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.
[0052] Figure 1 A flowchart illustrating the steps of a thin sand body identification method based on multi-wavelet decomposition according to an embodiment of the present invention is shown.
[0053] Figure 2 A schematic diagram of the optimized feed set according to an embodiment of the present invention is shown.
[0054] Figure 3 A schematic diagram of the target layer spectrum according to an embodiment of the present invention is shown.
[0055] Figure 4 A schematic diagram of a reconstructed gather according to an embodiment of the present invention is shown.
[0056] Figure 5 A schematic diagram of the thin sand body identification results according to conventional methods is shown.
[0057] Figure 6 A schematic diagram of the thin sand body identification results according to the present invention is shown.
[0058] Figure 7 A block diagram of a thin sand body identification device based on multi-wavelet decomposition according to an embodiment of the present invention is shown.
[0059] Explanation of reference numerals in the attached figures:
[0060] 201. Decomposition module; 202. Comparison module; 203. Reconstruction module; 204. Filtering module; 205. Overlay module; 206. Recognition module. Detailed Implementation
[0061] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.
[0062] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.
[0063] Example 1
[0064] Figure 1 A flowchart illustrating the steps of a thin sand body identification method based on multi-wavelet decomposition according to an embodiment of the present invention is shown.
[0065] like Figure 1 As shown, the thin sand body identification method based on multi-wavelet decomposition includes:
[0066] Step 101: Preprocess the pre-stack angle gather and then perform multi-wavelet decomposition;
[0067] Step 102: By comparing and analyzing well data with actual decomposed gathers, the frequency band range sensitive to thin sandstone reservoirs is selected.
[0068] Step 103: Perform multi-wavelet reconstruction on the data after multi-wavelet decomposition to obtain the seismic angle gather data volume;
[0069] Step 104: Select the dominant angles based on the seismic angle gather data volume;
[0070] Step 105: Perform partial overlay based on the dominant angle to obtain the overlaid seismic data;
[0071] Step 106: Based on the superimposed seismic data, extract the sand body sensitive attributes to identify thin sand bodies.
[0072] In one example, preprocessing of the pre-stack corner gather includes:
[0073] Noise suppression and layer flattening were performed on the pre-stack angle gather data, and data quality control was carried out based on the characteristics of the marker layer wave group, the stacking profile, and the AVO characteristics of the target layer.
[0074] In one example, performing multi-wavelet decomposition includes:
[0075] A seismic wavelet library was constructed, and multi-wavelet decomposition was performed on the pre-stack angle gathers based on the matching pursuit algorithm.
[0076] In one example, a seismic wavelet library is constructed using the Lake wavelet.
[0077] In one example, the search space of the matching pursuit algorithm is narrowed by the characteristics of the Gaussian distribution, and a genetic algorithm is applied to the optimal wavelet matching in the matching pursuit algorithm.
[0078] In one example, multi-wavelet decomposition is performed using a convolution model:
[0079]
[0080] Among them, w i Let R represent the i-th subwavelet. i Let N(t) represent the reflection coefficient corresponding to the i-th wavelet, and let N(t) represent noise.
[0081] In one example, selecting the dominant angle based on the seismic angle gather data volume includes:
[0082] Based on the reservoir segment AVO characteristics of the seismic angle gather data, the angle range sensitive to sand bodies is determined.
[0083] Specifically, quality control and optimization were performed on the pre-stack angle gathers. Noise suppression and layer leveling were performed on the pre-stack angle gather data, and data quality control was conducted based on the characteristics of the marker layer wave group, the stacking profile, and the AVO characteristics of the target layer to ensure the rationality of the gathers.
[0084] A seismic wavelet library suitable for the study area was constructed, and a matching pursuit algorithm optimized by a genetic algorithm was used to perform multi-wavelet decomposition on the pre-stack angle gathers.
[0085] Seismic data multi-wavelet decomposition is based on a convolution model, which decomposes seismic traces into a series of seismic wavelet combinations with different shapes and energies:
[0086]
[0087] Among them, w i Let R represent the i-th subwavelet. i Let N(t) represent the reflection coefficient corresponding to the i-th wavelet, and let N(t) represent noise.
[0088] This invention uses the Ricker wavelet to construct a seismic wavelet library. The mathematical expression for the Ricker wavelet is:
[0089]
[0090] Here, f represents the wavelet frequency, and different wavelet frequencies can be set to obtain corresponding Rayleigh wavelets. The frequency band range of the seismic wavelet library is determined by analyzing the frequency band range of seismic data in the study area.
[0091] Matching pursuit algorithm is a common method for multi-wavelet decomposition, but its search process requires traversing all seismic wavelets, resulting in an excessively large search space and long computation time. To address this issue, this invention utilizes the distribution characteristics of the Gaussian distribution (99.7% of the energy of the Gaussian function lies in the distribution of the Gaussian distribution). This narrows the search space and applies the genetic algorithm to the process of finding the best wavelet match in the matching pursuit algorithm, thereby improving the computational efficiency of the algorithm.
[0092] By comparing and analyzing well data with actual decomposed gathers, frequency bands sensitive to thin sandstone reservoirs are selected to eliminate anomalies in thin sandstone reflection characteristics caused by factors such as tuning effects. Multi-wavelet reconstruction is performed on the data after multi-wavelet decomposition, and the selected wavelet components are linearly superimposed to obtain a new seismic angle gather data volume. Based on the reservoir segment AVO characteristics of the seismic angle gather data volume, angle ranges sensitive to sandstone bodies are selected. Partial superposition is performed based on the selected advantageous angles to obtain superimposed seismic data. Based on the superimposed seismic data, sandstone-sensitive attributes are extracted for thin sandstone body identification.
[0093] Example 2
[0094] The present invention also provides a thin sand body identification device based on multi-wavelet decomposition, comprising:
[0095] The decomposition module preprocesses the pre-stack angle gathers and then performs multi-wavelet decomposition.
[0096] The comparison module uses comparative analysis of well data and actual decomposed gathers to screen frequency bands sensitive to thin sandstone reservoirs.
[0097] The reconstruction module performs multi-wavelet reconstruction on the data after multi-wavelet decomposition to obtain the seismic angle gather data volume.
[0098] The filtering module filters advantageous angles based on the seismic angle gather data volume;
[0099] The overlay module performs partial overlay based on the advantageous angle to obtain the overlaid seismic data.
[0100] The identification module extracts sensitive attributes of sand bodies based on the superimposed seismic data to identify thin sand bodies.
[0101] In one example, preprocessing of the pre-stack corner gather includes:
[0102] Noise suppression and layer flattening were performed on the pre-stack angle gather data, and data quality control was carried out based on the characteristics of the marker layer wave group, the stacking profile, and the AVO characteristics of the target layer.
[0103] In one example, performing multi-wavelet decomposition includes:
[0104] A seismic wavelet library was constructed, and multi-wavelet decomposition was performed on the pre-stack angle gathers based on the matching pursuit algorithm.
[0105] In one example, a seismic wavelet library is constructed using the Lake wavelet.
[0106] In one example, the search space of the matching pursuit algorithm is narrowed by the characteristics of the Gaussian distribution, and a genetic algorithm is applied to the optimal wavelet matching in the matching pursuit algorithm.
[0107] In one example, multi-wavelet decomposition is performed using a convolution model:
[0108]
[0109] Among them, w i Let R represent the i-th subwavelet. i Let N(t) represent the reflection coefficient corresponding to the i-th wavelet, and let N(t) represent noise.
[0110] In one example, selecting the dominant angle based on the seismic angle gather data volume includes:
[0111] Based on the reservoir segment AVO characteristics of the seismic angle gather data, the angle range sensitive to sand bodies is determined.
[0112] Specifically, quality control and optimization were performed on the pre-stack angle gathers. Noise suppression and layer leveling were performed on the pre-stack angle gather data, and data quality control was conducted based on the characteristics of the marker layer wave group, the stacking profile, and the AVO characteristics of the target layer to ensure the rationality of the gathers.
[0113] A seismic wavelet library suitable for the study area was constructed, and a matching pursuit algorithm optimized by a genetic algorithm was used to perform multi-wavelet decomposition on the pre-stack angle gathers.
[0114] Seismic data multi-wavelet decomposition is based on a convolution model, which decomposes seismic traces into a series of seismic wavelet combinations with different shapes and energies:
[0115]
[0116] Among them, w i Let R represent the i-th subwavelet. i Let N(t) represent the reflection coefficient corresponding to the i-th wavelet, and let N(t) represent noise.
[0117] This invention uses the Ricker wavelet to construct a seismic wavelet library. The mathematical expression for the Ricker wavelet is:
[0118]
[0119] Here, f represents the wavelet frequency, and different wavelet frequencies can be set to obtain corresponding Rayleigh wavelets. The frequency band range of the seismic wavelet library is determined by analyzing the frequency band range of seismic data in the study area.
[0120] Matching pursuit algorithm is a common method for multi-wavelet decomposition, but its search process requires traversing all seismic wavelets, resulting in an excessively large search space and long computation time. To address this issue, this invention utilizes the distribution characteristics of the Gaussian distribution (99.7% of the energy of the Gaussian function lies in the distribution of the Gaussian distribution). This narrows the search space and applies the genetic algorithm to the process of finding the best wavelet match in the matching pursuit algorithm, thereby improving the computational efficiency of the algorithm.
[0121] By comparing and analyzing well data with actual decomposed gathers, frequency bands sensitive to thin sandstone reservoirs are selected to eliminate anomalies in thin sandstone reflection characteristics caused by factors such as tuning effects. Multi-wavelet reconstruction is performed on the data after multi-wavelet decomposition, and the selected wavelet components are linearly superimposed to obtain a new seismic angle gather data volume. Based on the reservoir segment AVO characteristics of the seismic angle gather data volume, angle ranges sensitive to sandstone bodies are selected. Partial superposition is performed based on the selected advantageous angles to obtain superimposed seismic data. Based on the superimposed seismic data, sandstone-sensitive attributes are extracted for thin sandstone body identification.
[0122] Example 3
[0123] Taking data from a specific work area in China as an example, this study investigates the identification of thin sand bodies.
[0124] Figure 2 A schematic diagram of the optimized feed set according to an embodiment of the present invention is shown.
[0125] Pre-stack angle gather quality control and optimization. For pre-stack angle gather data, noise suppression and layer leveling were performed for gather optimization. Data quality control was conducted based on marker layer wave group characteristics, stacking profiles, and target layer AVO characteristics to ensure the gathers' rationality. The optimized gathers are shown below. Figure 2 As shown, the signal-to-noise ratio of the data set is high, and there is no significant time difference.
[0126] Figure 3 A schematic diagram of the target layer spectrum according to an embodiment of the present invention is shown.
[0127] Multi-wavelet decomposition. This is achieved by analyzing the frequency band range of seismic data in the study area, such as... Figure 3 As shown, the wavelet frequency band is determined to be 0-80Hz. A seismic wavelet library is constructed using the Lake wavelet, and multi-wavelet decomposition is performed on the pre-stack angle gather based on a matching pursuit algorithm optimized by a genetic algorithm.
[0128] Preferred frequency band screening. By comparing and analyzing well data with actual decomposed gathers, frequency bands sensitive to thin sandstone reservoirs are screened, revealing that components in the 25-35Hz frequency range are more sensitive to sandstone bodies.
[0129] Figure 4 A schematic diagram of a reconstructed gather according to an embodiment of the present invention is shown.
[0130] Multi-wavelet reconstruction. The selected components are linearly superimposed to obtain a new seismic angle gather data volume. Figure 4 The reconstruction of the angle gather is shown, and it can be seen that there is no obvious tuning effect in the target layer after reconstruction.
[0131] Advantageous angle selection. Based on the analysis of well logging data and the reconstruction of reservoir section AVO characteristics, the angle range sensitive to sand bodies is selected, with the target layer sensitive angle range being 13°-35°.
[0132] Partial overlay. Based on the selected advantageous angle range, partial overlay is performed to obtain the overlaid seismic data.
[0133] Figure 5 A schematic diagram of the thin sand body identification results according to conventional methods is shown.
[0134] Figure 6 A schematic diagram of the thin sand body identification results according to the present invention is shown.
[0135] Thin sand body identification. Based on the stacked seismic data, attributes sensitive to sand bodies were extracted. The amplitude attributes in the study area are relatively sensitive to sand bodies. Figure 5 and Figure 6 The amplitude attributes after conventional stacking and the amplitude attributes extracted by the present invention after multi-wavelet reconstruction and dominant angle superposition are shown respectively. It can be seen that compared with the conventional method, the present invention has higher thin sand body identification accuracy, better channel sand continuity, and higher consistency with wellbore data, which is better than the conventional thin sand body identification results, proving that the method has better application effect.
[0136] Example 4
[0137] Figure 7 A block diagram of a thin sand body identification device based on multi-wavelet decomposition according to an embodiment of the present invention is shown.
[0138] like Figure 7 As shown, the thin sand body identification device based on multi-wavelet decomposition includes:
[0139] Decomposition module 201 preprocesses the pre-stack angle gather and then performs multi-wavelet decomposition.
[0140] Comparison module 202 uses comparative analysis of well data and actual decomposed gathers to screen frequency bands sensitive to thin sandstone reservoirs;
[0141] Reconstruction module 203 performs multi-wavelet reconstruction on the data after multi-wavelet decomposition to obtain seismic angle gather data volume;
[0142] Filtering module 204 filters advantageous angles based on the seismic angle gather data volume;
[0143] The overlay module 205 performs partial overlay based on the dominant angle to obtain the overlaid seismic data.
[0144] The identification module 206 extracts sensitive attributes of sand bodies based on the superimposed seismic data to identify thin sand bodies.
[0145] In one example, preprocessing of the pre-stack corner gather includes:
[0146] Noise suppression and layer flattening were performed on the pre-stack angle gather data, and data quality control was carried out based on the characteristics of the marker layer wave group, the stacking profile, and the AVO characteristics of the target layer.
[0147] In one example, performing multi-wavelet decomposition includes:
[0148] A seismic wavelet library was constructed, and multi-wavelet decomposition was performed on the pre-stack angle gathers based on the matching pursuit algorithm.
[0149] In one example, a seismic wavelet library is constructed using the Lake wavelet.
[0150] In one example, the search space of the matching pursuit algorithm is narrowed by the characteristics of the Gaussian distribution, and a genetic algorithm is applied to the optimal wavelet matching in the matching pursuit algorithm.
[0151] In one example, multi-wavelet decomposition is performed using a convolution model:
[0152]
[0153] Among them, w i Let R represent the i-th subwavelet. i Let N(t) represent the reflection coefficient corresponding to the i-th wavelet, and let N(t) represent noise.
[0154] In one example, selecting the dominant angle based on the seismic angle gather data volume includes:
[0155] Based on the reservoir segment AVO characteristics of the seismic angle gather data, the angle range sensitive to sand bodies is determined.
[0156] Example 5
[0157] This disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the aforementioned thin sand body identification method based on multi-wavelet decomposition.
[0158] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
[0159] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.
[0160] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.
[0161] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.
[0162] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.
[0163] Example 6
[0164] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the thin sand body identification method based on multi-wavelet decomposition.
[0165] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.
[0166] 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).
[0167] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.
[0168] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.
Claims
1. A method for identifying thin sand bodies based on multi-wavelet decomposition, characterized in that, include: The pre-stack angle gather is preprocessed and then multi-wavelet decomposition is performed. By comparing and analyzing well data with actual decomposed gathers, frequency bands sensitive to thin sandstone reservoirs were selected. Multi-wavelet reconstruction is performed on the data after multi-wavelet decomposition to obtain the seismic angle gather data volume; Select advantageous angles based on the aforementioned seismic angle gather data; Based on the aforementioned advantageous angles, partial superposition is performed to obtain the superimposed seismic data; Based on the superimposed seismic data, sensitive attributes of sand bodies are extracted for thin sand body identification.
2. The thin sand body identification method based on multi-wavelet decomposition according to claim 1, wherein, Preprocessing of pre-stack corner gathers includes: The pre-stack angle gather data is subjected to noise suppression and layer flattening optimization processing, and data quality control is performed based on the characteristics of the marker layer wave group, the stacking profile, and the AVO characteristics of the target layer.
3. The thin sand body identification method based on multi-wavelet decomposition according to claim 1, wherein, Performing multi-wavelet decomposition includes: A seismic wavelet library was constructed, and multi-wavelet decomposition was performed on the pre-stack angle gathers based on the matching pursuit algorithm.
4. The thin sand body identification method based on multi-wavelet decomposition according to claim 3, wherein, The seismic wavelet library is constructed using the Lake wavelet.
5. The thin sand body identification method based on multi-wavelet decomposition according to claim 3, wherein, The search space of the matching pursuit algorithm is narrowed by utilizing the distribution characteristics of the Gaussian distribution, and a genetic algorithm is applied to the optimal wavelet matching in the matching pursuit algorithm.
6. The thin sand body identification method based on multi-wavelet decomposition according to claim 3, wherein, Multi-wavelet decomposition using a convolution model: Among them, w i Let R represent the i-th subwavelet. i Let N(t) represent the reflection coefficient corresponding to the i-th wavelet, and let N(t) represent noise.
7. The thin sand body identification method based on multi-wavelet decomposition according to claim 1, wherein, The advantageous angles selected based on the aforementioned seismic angle gather data volume include: Based on the reservoir segment AVO characteristics of the seismic angle gather data, the angle range sensitive to sand bodies is determined.
8. A thin sand body identification device based on multi-wavelet decomposition, characterized in that, include: The decomposition module preprocesses the pre-stack angle gathers and then performs multi-wavelet decomposition. The comparison module uses comparative analysis of well data and actual decomposed gathers to screen frequency bands sensitive to thin sandstone reservoirs. The reconstruction module performs multi-wavelet reconstruction on the data after multi-wavelet decomposition to obtain the seismic angle gather data volume. The filtering module filters advantageous angles based on the seismic angle gather data. The overlay module performs partial overlay based on the advantageous angle to obtain the overlaid seismic data. The identification module extracts sensitive attributes of sand bodies based on the superimposed seismic data to identify thin sand bodies.
9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the thin sand body identification method based on multi-wavelet decomposition as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the thin sand body identification method based on multi-wavelet decomposition as described in any one of claims 1-7.