Seismic data processing method and apparatus

Abstract

This application provides a seismic data processing method and apparatus, belonging to the field of seismic exploration technology for oil and gas. The technical solution provided in this application involves obtaining a Wiener filter from the original wavelet data and the desired wavelet data. Based on the Wiener filter, the original wavelet data is band-extended to minimize the error of the band-extended wavelet data. Further processing yields a second frequency component, i.e., a low-frequency component, in the band-extended wavelet data. This second frequency component is then superimposed on the original wavelet data to obtain compensated wavelet data. The low-frequency component in the compensated wavelet data is compensated, avoiding the low-frequency reduction problem of seismic data after Q-migrating, thereby obtaining high-resolution, wideband seismic data.

Description

Technical Field

[0001] This application relates to the field of seismic exploration technology for oil and gas, and in particular to a seismic data processing method and apparatus. Background Technology

[0002] In the process of oil and gas exploration, especially in the exploration of shale oil fields, seismic exploration technology is often used to study the properties of the formation in order to predict the sweet spot of shale oil. By processing the seismic data, the effective information in the seismic data can be fully extracted, and then the reservoir can be described in detail.

[0003] Currently used seismic data processing methods include denoising, deconvolution, dynamic and static correction, velocity analysis, stacking, and migration. Since seismic data is generally a broadband signal containing both high-frequency and low-frequency signals, noise attenuation, deconvolution, and stacking steps during processing have a significant impact on the low-frequency components of the data. The Q-migration technique, a viscous pre-stack depth migration seismic imaging method that has become popular in recent years, can more accurately compensate for energy attenuation and phase distortion of seismic waves during propagation, and has a significant frequency upscaling effect. However, the higher the gain limit, the more pronounced the frequency upscaling effect and the more severe the low-frequency attenuation. Summary of the Invention

[0004] This application provides a seismic data processing method and apparatus that can compensate for low-frequency components after processing, avoiding the low-frequency attenuation problem of seismic data after Q-migrations, thereby obtaining high-resolution, broadband seismic data. The technical solution is as follows:

[0005] On the one hand, a seismic data processing method is provided, characterized in that the method includes:

[0006] Based on seismic data, multiple first arrival information is obtained. These multiple first arrival information are information processed by seismic quality factor Q migration, and the signal-to-noise ratios of these multiple first arrival information are different.

[0007] Based on the first arrival information with a signal-to-noise ratio greater than a preset threshold, the corresponding original wavelet data is obtained;

[0008] The raw wavelet data is shaped to obtain the desired wavelet data;

[0009] Based on the original wavelet data and the desired wavelet data, a Wiener filter is obtained, wherein the desired wavelet data is wavelet data with a preset dominant frequency, and the minimum square error between the wavelet data after the bandwidth extension obtained by the Wiener filter and the desired wavelet data is less than a preset error threshold.

[0010] Based on the original wavelet data and the Wiener filter, the band-extended wavelet data is obtained.

[0011] The original wavelet data is subtracted from the expanded wavelet data to obtain the expanded wavelet data.

[0012] Filter the first frequency component in the extended wavelet data to obtain the second frequency component in the extended wavelet data, wherein the first frequency is greater than a preset frequency and the second frequency is less than or equal to the preset frequency.

[0013] The second frequency component in the extended wavelet data is superimposed on the original wavelet data to obtain the compensated wavelet data.

[0014] In one possible implementation, multiple first-arrival information items are obtained based on seismic data, including:

[0015] Based on the seismic data, the seismic data is extracted to the common shot point domain in order to extract the first arrival information and obtain multiple first arrival information.

[0016] In one possible implementation, the original wavelet data is shaped to obtain the desired wavelet data, including:

[0017] The original wavelet is extracted and edited to obtain the wavelet shape of the desired wavelet;

[0018] The expected value of the dominant frequency of the desired wavelet is obtained based on geological requirements.

[0019] In one possible implementation, after superimposing the second frequency component of the extended wavelet data with the original wavelet data to obtain compensated wavelet data, the method further includes:

[0020] By comparing the extended wavelet data with the well logging data, we can obtain a characterization of the lateral changes in reservoir energy by the compensated wavelet data.

[0021] In one possible implementation, after superimposing the second frequency component of the extended wavelet data with the original wavelet data to obtain compensated wavelet data, the method further includes:

[0022] Based on the compensated wavelet data, well location calibration was performed to obtain the attribute influence of the compensated wavelet data on the seismic data.

[0023] On one hand, a seismic data processing apparatus is provided, characterized in that the apparatus comprises:

[0024] The information acquisition module is used to acquire multiple first arrival information based on seismic data. These multiple first arrival information are information processed by Q migration, and the signal-to-noise ratios of these multiple first arrival information are different.

[0025] The wavelet acquisition module is used to acquire the corresponding raw wavelet data based on the first arrival information where the signal-to-noise ratio is greater than a preset threshold.

[0026] The shaping module is used to shape the raw wavelet data in order to obtain the desired wavelet data;

[0027] The filtering module is used to obtain a Wiener filter based on the original wavelet data and the desired wavelet data, wherein the desired wavelet data is wavelet data with a preset dominant frequency, and the minimum square error between the wavelet data after the bandwidth extension obtained by the Wiener filter and the desired wavelet data is less than a preset error threshold.

[0028] An extension module is used to obtain band-extended wavelet data based on the original wavelet data and the Wiener filter.

[0029] The extension module is also used to subtract the original wavelet data from the wavelet data after the frequency band extension to obtain the extended wavelet data;

[0030] A filtering module is used to filter the first frequency component in the extended wavelet data to obtain the second frequency component in the extended wavelet data, wherein the first frequency is greater than a preset frequency and the second frequency is less than or equal to the preset frequency.

[0031] The superposition module is used to superimpose the second frequency component in the extended wavelet data with the original wavelet data to obtain the compensated wavelet data.

[0032] In one possible implementation, the information acquisition module is used for:

[0033] Based on the seismic data, the seismic data is extracted to the common shot point domain in order to extract the first arrival information and obtain multiple first arrival information.

[0034] In one possible implementation, the shaping module is used to: shape the original wavelet data, including:

[0035] The original wavelet is extracted and edited to obtain the wavelet shape of the desired wavelet;

[0036] The expected value of the dominant frequency of the desired wavelet is obtained based on geological requirements.

[0037] In one possible implementation, the device further includes: a comparison module, used for:

[0038] By comparing the extended wavelet data with the well logging data, we can obtain a characterization of the lateral changes in reservoir energy by the compensated wavelet data.

[0039] In one possible implementation, the device further includes: a calibration module, used for:

[0040] Based on the compensated wavelet data, well location calibration was performed to obtain the attribute influence of the compensated wavelet data on the seismic data.

[0041] The technical solution provided in this application obtains a Wiener filter from the original wavelet data and the desired wavelet data. Based on the Wiener filter, the original wavelet is band-extended, resulting in wavelet data with minimal error after band extension. The second frequency component, i.e. the low-frequency component, in the band-extended wavelet data is then obtained through processing. This second frequency component is superimposed on the original wavelet data to obtain compensated wavelet data. The low-frequency component in the compensated wavelet data is compensated, avoiding the low-frequency reduction problem of seismic data after Q-migrating, thereby obtaining high-resolution, wideband seismic data. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of a seismic data processing method provided in an embodiment of this application;

[0044] Figure 2 This is a flowchart of a seismic data processing method provided in an embodiment of this application;

[0045] Figure 3 This is a schematic diagram of the structure of an earthquake data processing device provided in an embodiment of this application;

[0046] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0048] Figure 1 This is a flowchart of a seismic data processing method provided in an embodiment of this application. Please refer to [link / reference]. Figure 1 This method can be applied to computer devices, and the method includes:

[0049] 101. Based on seismic data, obtain multiple first arrival information.

[0050] These multiple first arrival information are seismic data information processed by seismic quality factor Q (seismic quality factor) migration, and the signal-to-noise ratios of these multiple first arrival information are different.

[0051] 102. Based on the first arrival information where the signal-to-noise ratio is greater than a preset threshold, obtain the corresponding original wavelet data.

[0052] 103. Shape the original wavelet data to obtain the desired wavelet data.

[0053] 104. Based on the original wavelet data and the desired wavelet data, obtain the Wiener filter.

[0054] The desired wavelet data is wavelet data with a preset dominant frequency. After the frequency band is extended by the Wiener filter, the minimum square error between the wavelet data and the desired wavelet data is less than a preset error threshold.

[0055] 105. Based on the original wavelet data and the Wiener filter, obtain the band-extended wavelet data.

[0056] 106. Subtract the original wavelet data from the extended wavelet data to obtain the extended wavelet data.

[0057] 107. Filter the first frequency component in the extended wavelet data to obtain the second frequency component in the extended wavelet data.

[0058] The first frequency is greater than the preset frequency, and the second frequency is less than or equal to the preset frequency.

[0059] 108. The second frequency component in the extended wavelet data is superimposed on the original wavelet data to obtain the compensated wavelet data.

[0060] The method provided in this application embodiment obtains a Wiener filter from the original wavelet data and the desired wavelet data. Based on the Wiener filter, the original wavelet is band-extended, resulting in wavelet data with smaller errors. The second frequency component, i.e. the low-frequency component, in the band-extended wavelet data is then obtained through processing. This second frequency component is superimposed on the original wavelet data to obtain compensated wavelet data. The low-frequency component in the compensated wavelet data is compensated, avoiding the low-frequency reduction problem of seismic data after Q-migrating, thereby obtaining high-resolution, wideband seismic data.

[0061] In one possible implementation, multiple first-arrival information items are obtained based on seismic data, including:

[0062] Based on the seismic data, the seismic data is extracted to the common shot point domain in order to extract the first arrival information and obtain multiple first arrival information.

[0063] In one possible implementation, the original wavelet data is shaped to obtain the desired wavelet data, including:

[0064] The original wavelet is extracted and edited to obtain the wavelet shape of the desired wavelet;

[0065] The expected value of the dominant frequency of the desired wavelet is obtained based on geological requirements.

[0066] In one possible implementation, after superimposing the second frequency component of the extended wavelet data with the original wavelet data to obtain compensated wavelet data, the method further includes:

[0067] By comparing the extended wavelet data with the well logging data, we can obtain a characterization of the lateral changes in reservoir energy by the compensated wavelet data.

[0068] In one possible implementation, after superimposing the second frequency component of the extended wavelet data with the original wavelet data to obtain compensated wavelet data, the method further includes:

[0069] Based on the compensated wavelet data, well location calibration was performed to obtain the attribute influence of the compensated wavelet data on the seismic data.

[0070] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0071] Figure 2 This is a flowchart of a seismic data processing method provided in an embodiment of this application. Please refer to [link / reference]. Figure 2 This method can be applied to computer devices, and the method includes:

[0072] 201. Based on seismic data, obtain multiple first arrival information.

[0073] Among them, the multiple first arrival information are information after Q offset processing, which can be automatically identified by the computer and obtained through human-computer interaction. The signal-to-noise ratios of these multiple first arrival information are different.

[0074] Seismic data refers to seismic signals received and recorded by seismic instruments during oil and gas seismic exploration projects, including sonic logging data and velocity logging data.

[0075] First arrival information refers to the moment when the seismic wave front arrives at a certain observation point and the detector at that point detects the vibration of the particle.

[0076] Q-migrating is a seismic imaging method based on viscous pre-stack depth migration. It obtains the seismic quality factor Q through Q-tomography inversion and then corrects for travel time to achieve amplitude compensation, frequency recovery, and phase correction. Compared to conventional pre-stack depth migration with Q-compensation, Q-migrating more accurately compensates for energy attenuation and phase distortion of seismic waves during propagation, and its frequency upsetting effect is significant. However, the higher the gain limit, the more pronounced the frequency upsetting effect and the more severe the low-frequency attenuation.

[0077] Signal-to-noise ratio (SNR) refers to the ratio of signal to noise in seismic data.

[0078] Step 201 includes:

[0079] (1) Using denoising methods such as classification, step-by-step, domain-by-domain, and frequency-by-frequency, we focus on monitoring the low-frequency effective signals in the seismic data and perform secondary separation of the effective signals remaining in the noise of the target layer to retain the low-frequency information of the target layer to the maximum extent.

[0080] (2) By effectively widening the frequency band through deconvolution, and then reducing well-seismic error through anisotropic tomography inversion, a high-precision depth domain layer velocity model is obtained.

[0081] (3) The near-surface velocity model is obtained by constrained tomography inversion. The surface travel time is calculated by the near-surface velocity model and the depth of the shot receiver. The surface Q field is then estimated by the frequency shift method.

[0082] (4) Through rationalization analysis, the velocity and Q value of the VSP (Vertical Seismic Profile) layer, which eliminates some outliers, are fitted with a formula, and then the mid-deep Q field is obtained by using the fitted formula and velocity.

[0083] (5) Combine the surface Q field and the deep Q field, obtain a high-precision integrated Q field through Q-tomography inversion, perform depth domain Q migration, expand the bandwidth of the high-frequency part of the seismic data, and improve the resolution.

[0084] (6) Based on the broadened seismic data obtained in step (5), the broadened seismic data is extracted into the common shot point domain in order to extract the first arrival information and obtain multiple first arrival information.

[0085] 202. Based on the first arrival information where the signal-to-noise ratio is greater than a preset threshold, obtain the corresponding original wavelet data.

[0086] In this step, the preset threshold can be set according to the signal-to-noise ratio of each initial arrival information, but this embodiment does not limit it.

[0087] Specifically, first arrival information with a high signal-to-noise ratio in seismic data should be selected for wavelet acquisition. It is best to perform wavelet statistics on multiple points within the work area and then obtain stable wavelet information through statistical methods.

[0088] 203. Shape the original wavelet data to obtain the desired wavelet data.

[0089] In this step, obtaining the desired wavelet data requires two factors: the desired wavelet shape and the desired wavelet dominant frequency.

[0090] 1. The wavelet shape is obtained by extracting the original wavelet and editing it with the wavelet editor to eliminate the sidelobes of the wavelet and obtain the ideal wavelet shape;

[0091] 2. Determine the expected value of the wavelet dominant frequency based on actual geological requirements. For example, the expected value of the wavelet dominant frequency can be 35Hz.

[0092] 204. Based on the original wavelet data and the desired wavelet data, obtain the Wiener filter.

[0093] The desired wavelet data is wavelet data with a preset dominant frequency.

[0094] The unshaped raw wavelet data and the desired wavelet data are matched to obtain the matching factor, which is the Wiener filter. This Wiener filter is used for subsequent frequency extension.

[0095] Specifically, the relationship between the original wavelet data, the desired wavelet data, and the Wiener filter is shown in Equation 1:

[0096] The relation f(t) = d(t) / s(t) is given by equation 1.

[0097] Where d(t) represents the desired wavelet data;

[0098] f(t) represents the Wiener filter, and (f0, f1, ..., fn-1) represent the multiple coefficients of the filter;

[0099] s(t) represents the original wavelet data.

[0100] 205. Based on the original wavelet data and the Wiener filter, obtain the band-extended wavelet data.

[0101] The band-extended wavelet data is obtained after filtering by the Wiener filter, see Equation 2.

[0102] The relation y(t) = f(t) * s(t) is given by equation 2.

[0103] Where y(t) represents the band-extended wavelet data;

[0104] f(t) represents the Wiener filter;

[0105] s(t) represents the original wavelet data.

[0106] The minimum squared error E between the band-extended wavelet data and the desired wavelet data can be expressed by Equation 3:

[0107]

[0108] Where f(t) represents the Wiener filter;

[0109] d(t) represents the desired wavelet data;

[0110] y(t) represents the wavelet data after bandwidth extension.

[0111] In this step, the Wiener filter is calculated from the expected wavelet data, and then the band-extended wavelet data is calculated based on the Wiener filter. This minimizes E and ensures the accuracy of the band-extended wavelet data.

[0112] 206. Subtract the original wavelet data from the expanded wavelet data to obtain the expanded wavelet data.

[0113] The extended wavelet data is used for subsequent superposition.

[0114] 207. Compare the extended wavelet data with the logging data to obtain the impact of the compensated wavelet data on the lateral variation of reservoir energy.

[0115] Specifically, the extended wavelet data obtained in step 206 needs to be processed, interpreted, and integrated for quality control. Combined with well logging data, it is necessary to analyze whether the compensated low-frequency part meets the requirements for characterizing the lateral changes in reservoir energy.

[0116] 208. Filter the first frequency component in the extended wavelet data to obtain the second frequency component in the extended wavelet data.

[0117] The first frequency is greater than the preset frequency, and the second frequency is less than or equal to the preset frequency.

[0118] Specifically, the first frequency component is the high-frequency component, and the second frequency component is the low-frequency component. The preset frequency can be set as needed, and this embodiment does not limit it.

[0119] 209. The second frequency component in the extended wavelet data is superimposed on the original wavelet data to obtain the compensated wavelet data.

[0120] Since the data after Q offset suffers from low-frequency attenuation, the low-frequency components are retained and subsequently compensated into the wavelet data to improve the aforementioned problem.

[0121] 210. Based on the compensated wavelet data, well location calibration is performed to obtain the attribute influence of the compensated wavelet data on the seismic data.

[0122] Specifically, well location calibration is performed based on the compensated wavelet data obtained in step 209 to ensure that there are no abnormalities in the phase, wave group characteristics, and other attributes of the seismic data.

[0123] The method provided in this application embodiment obtains a Wiener filter from the original wavelet data and the desired wavelet data. Based on the Wiener filter, the original wavelet is band-extended to obtain band-extended wavelet data. Then, the second frequency component, i.e. the low-frequency component, in the band-extended wavelet data is obtained through processing. The second frequency component is superimposed on the original wavelet data to obtain compensated wavelet data. The low-frequency component in the compensated wavelet data is compensated, avoiding the low-frequency reduction problem of seismic data after Q-migrating, thereby obtaining high-resolution, wideband seismic data.

[0124] Figure 3 This is a schematic diagram of the structure of a seismic data processing device provided in an embodiment of this application. Please refer to [link / reference]. Figure 3 The device includes:

[0125] The information acquisition module 301 is used to acquire multiple first arrival information based on seismic data. These multiple first arrival information are seismic data information after Q migration processing.

[0126] The wavelet acquisition module 302 is used to acquire the corresponding raw wavelet data based on the first arrival information where the signal-to-noise ratio is greater than a preset threshold.

[0127] The shaping module 303 is used to shape the original wavelet data in order to obtain the desired wavelet data;

[0128] The filtering module 304 is used to obtain a Wiener filter based on the original wavelet data and the desired wavelet data, wherein the desired wavelet data is wavelet data with a preset main frequency, and the minimum square error between the wavelet data after the bandwidth extension obtained by the Wiener filter and the desired wavelet data is less than a preset error threshold.

[0129] The extension module 305 is used to obtain band-extended wavelet data based on the original wavelet data and the Wiener filter.

[0130] The extension module 305 is also used to subtract the original wavelet data from the extended wavelet data to obtain the extended wavelet data;

[0131] Filtering module 306 is used to filter the first frequency component in the extended wavelet data to obtain the second frequency component in the extended wavelet data, wherein the first frequency is greater than a preset frequency and the second frequency is less than or equal to the preset frequency.

[0132] The superposition module 307 is used to superimpose the second frequency component in the extended wavelet data with the original wavelet data to obtain the compensated wavelet data.

[0133] In one possible implementation, the information acquisition module 301 is used for:

[0134] Based on the seismic data, the seismic data is extracted to the common shot point domain in order to extract the first arrival information and obtain multiple first arrival information.

[0135] In one possible implementation, the shaping module 303 is used for:

[0136] The original wavelet is extracted and edited to obtain the wavelet shape of the desired wavelet;

[0137] The expected value of the dominant frequency of the desired wavelet is obtained based on geological requirements.

[0138] In one possible implementation, the device further includes: a comparison module, used for:

[0139] By comparing the extended wavelet data with the well logging data, it was found that the compensated wavelet data provides a more accurate characterization of the lateral changes in reservoir energy.

[0140] In one possible implementation, the device further includes: a calibration module, used for:

[0141] Based on the compensated wavelet data, well location calibration was performed to obtain the influence of the compensated wavelet data on the amplitude and phase attributes of the seismic data.

[0142] The apparatus provided in this application obtains a Wiener filter from the original wavelet data and the desired wavelet data. Based on the Wiener filter, the original wavelet is band-extended, resulting in wavelet data with smaller errors. The second frequency component, i.e., the low-frequency component, in the band-extended wavelet data is then obtained through processing. This second frequency component is superimposed on the original wavelet data to obtain compensated wavelet data. The low-frequency component in the compensated wavelet data is compensated, avoiding the low-frequency reduction problem of seismic data after Q-migrating, thereby obtaining high-resolution, wideband seismic data.

[0143] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Please refer to [link / reference]. Figure 4The computer device 400 can vary considerably due to differences in configuration or performance. It may include one or more central processing units (CPUs) 401 and one or more memories 402. The memories 402 store at least one line of program code, which is loaded and executed by the processors 401 to implement the seismic data processing methods provided in the various method embodiments described above. Of course, the computer device may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The computer device may also include other components for implementing device functions, which will not be elaborated upon here.

[0144] In some embodiments, the computer program involved in the present application embodiments may be deployed and executed on multiple computer devices located in one location, or on multiple computer devices distributed in multiple locations and interconnected through a communication network. The multiple computer devices distributed in multiple locations and interconnected through a communication network may form a blockchain (computer cluster) system.

[0145] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including program code that can be executed by a processor in a computer device to perform the seismic data processing method in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or disk data storage device, etc.

[0146] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0147] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A seismic data processing method, characterized in that, The method includes: Based on seismic data, multiple first arrival information is obtained. The multiple first arrival information is information processed by seismic quality factor Q migration, and the signal-to-noise ratios of the multiple first arrival information are different. Based on the first arrival information with a signal-to-noise ratio greater than a preset threshold, the corresponding raw wavelet data is obtained, wherein the preset threshold is set according to the signal-to-noise ratio of each first arrival information; The original wavelet data is shaped to obtain the desired wavelet data; Based on the original wavelet data and the desired wavelet data, a Wiener filter is obtained, wherein the desired wavelet data is wavelet data with a preset dominant frequency, and the minimum square error between the wavelet data after the bandwidth extension obtained by the Wiener filter and the desired wavelet data is less than a preset error threshold. Based on the original wavelet data and the Wiener filter, the band-extended wavelet data is obtained. The original wavelet data is subtracted from the expanded wavelet data to obtain the expanded wavelet data. Filter the first frequency component in the extended wavelet data to obtain the second frequency component in the extended wavelet data. The first frequency is greater than a preset frequency, the second frequency is less than or equal to the preset frequency, and the second frequency component is a low frequency component. The second frequency component in the extended wavelet data is superimposed on the original wavelet data to obtain the compensated wavelet data; Based on the compensated wavelet data, well location calibration is performed to obtain the attribute influence of the compensated wavelet data on the seismic data.

2. The method according to claim 1, characterized in that, The first arrival information obtained based on seismic data includes: Based on the seismic data, the seismic data is extracted to the common shot point domain in order to extract the first arrival information and obtain multiple first arrival information.

3. The method according to claim 1, characterized in that, Shaping the original wavelet data to obtain the desired wavelet data includes: The original wavelet is extracted and edited to obtain the wavelet shape of the desired wavelet; The expected value of the dominant frequency of the desired wavelet is obtained based on geological requirements.

4. The method according to claim 1, characterized in that, After superimposing the second frequency component in the extended wavelet data with the original wavelet data to obtain the compensated wavelet data, the method further includes: By comparing the extended wavelet data with the logging data, the compensated wavelet data is used to characterize the lateral changes in reservoir energy.

5. A seismic data processing device, characterized in that, The device includes: The information acquisition module is used to acquire multiple first arrival information based on seismic data. The multiple first arrival information is information processed by Q migration, and the signal-to-noise ratio of the multiple first arrival information is different. The wavelet acquisition module is used to acquire the corresponding raw wavelet data based on the first arrival information with a signal-to-noise ratio greater than a preset threshold. The preset threshold is set according to the signal-to-noise ratio of each first arrival information. A shaping module is used to shape the original wavelet data in order to obtain the desired wavelet data; The filtering module is used to obtain a Wiener filter based on the original wavelet data and the desired wavelet data, wherein the desired wavelet data is wavelet data with a preset main frequency, and the minimum square error between the wavelet data after the bandwidth expansion obtained by the Wiener filter and the desired wavelet data is less than a preset error threshold. An extension module is used to obtain band-extended wavelet data based on the original wavelet data and the Wiener filter. The extension module is also used to subtract the original wavelet data from the extended wavelet data to obtain the extended wavelet data; The filtering module is used to filter the first frequency component in the extended wavelet data to obtain the second frequency component in the extended wavelet data. The first frequency is greater than a preset frequency, the second frequency is less than or equal to the preset frequency, and the second frequency component is a low frequency component. The superposition module is used to superimpose the second frequency component in the extended wavelet data with the original wavelet data to obtain the compensated wavelet data. The device further includes: a calibration module, used for: Based on the compensated wavelet data, well location calibration is performed to obtain the attribute influence of the compensated wavelet data on the seismic data.

6. The apparatus according to claim 5, characterized in that, The information acquisition module is used for: Based on the seismic data, the seismic data is extracted to the common shot point domain in order to extract the first arrival information and obtain multiple first arrival information.

7. The apparatus according to claim 5, characterized in that, The shaping module is used to: shape the original wavelet data, including: The original wavelet is extracted and edited to obtain the wavelet shape of the desired wavelet; The expected value of the dominant frequency of the desired wavelet is obtained based on geological requirements.

8. The apparatus according to claim 5, characterized in that, The device further includes: a comparison module, used for: By comparing the extended wavelet data with the logging data, the compensated wavelet data is used to characterize the lateral changes in reservoir energy.

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