An optimization method and system for seismic velocity analysis supergathers

Abstract

This invention belongs to the field of seismic processing in oil and gas exploration, and relates to an optimization method and system for super gathers in seismic velocity analysis. The method, without compromising the fidelity of subsequent processed data, adds seismic head information to low signal-to-noise ratio gather data used for seismic velocity analysis, enabling flexible extraction of super gathers and CMP gathers. This suppresses linear and random noise in the super gathers, and is further supplemented by filtering and amplitude equalization to enhance the energy of the dominant frequency band of reflected waves. This improves the signal-to-noise ratio of super gathers in velocity analysis of oil and gas exploration data, mitigates the energy cluster divergence problem in the velocity spectrum, and achieves high-precision velocity analysis. The method of this invention can make the velocity spectrum energy clusters more focused, improve the signal-to-noise ratio of super gathers, and enhance the energy of the dominant frequency band of reflected waves, thereby significantly improving the accuracy and efficiency of velocity picking, obtaining more accurate velocities, and improving the signal-to-noise ratio and imaging accuracy of seismic data.

Description

Technical Field

[0001] This invention belongs to the field of seismic processing in oil and gas exploration, and relates to an optimization method and system for seismic velocity analysis super gathers. Background Technology

[0002] Velocity analysis is a crucial part of seismic data processing, aiming to obtain accurate velocity parameters to improve the precision of seismic imaging. Velocity analysis is fundamental for stacking and migration imaging, and forms the basis for reservoir prediction and inversion. As exploration progresses, the demand for velocity accuracy in seismic imaging increases. The main factors limiting the accuracy of velocity analysis are the focusing degree of energy clusters in the velocity spectrum and the signal-to-noise ratio of the super-gather.

[0003] When manually picking velocity spectra, it is necessary to consider whether the energy clusters of the velocity spectrum are focused, whether the phase axes of the super gathers are leveled, and the signal-to-noise ratio of the stacking segments. These three factors must be considered together to determine the accuracy of velocity picking. Typically, stacking velocity analysis requires combining seismic data with adjacent CMP gathers to form super gathers before calculating the velocity spectrum to improve the signal-to-noise ratio of the velocity analysis. Therefore, the quality of the super gathers is a key factor limiting the accuracy of velocity analysis picking.

[0004] Existing technologies propose a three-dimensional filtering approach for the generated velocity spectrum. This involves applying FK filtering to dynamically corrected CMP gather data, followed by reverse correction, to achieve the sorting of super-gathers and generate the velocity spectrum. However, while FK filtering improves the signal-to-noise ratio of the input data for the interactive velocity spectrum, it inevitably introduces some artificial artifacts or fails to completely eliminate them. Furthermore, FK filtering is global and eliminates effective waves overlapping with linear noise bands, thus degrading the characteristics and continuity of the effective waves after processing, significantly reducing the lateral resolution of the seismic record. Existing technologies also propose multi-stage optimized velocity analysis methods involving gather data noise attenuation, in-gather phase time difference correction, and picking constraints, but no approach has been found to further denoise the super-gathers for linear noise.

[0005] In the seismic data processing prior to velocity analysis, it is necessary to balance the signal-to-noise ratio with the fidelity of the signal for subsequent processing. Some weak linear noise features remaining on CMP gathers are not obvious, but after combining CMP gathers, they become prominent on super gathers, obscuring the effective signal and reducing the accuracy of manually picked velocities. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention aims to provide an optimization method and system for seismic velocity analysis super gathers. Before velocity spectrum calculation, the present invention adds seismic trace head information to achieve flexible extraction of super gathers and CMP gathers, thereby suppressing linear noise generated by gather combination and random noise on CMP gathers. Furthermore, it employs filtering and equalization processing to enhance the energy of the dominant frequency band of reflected waves, thereby improving the signal-to-noise ratio of super gathers in the velocity analysis of oil and gas exploration data, improving the energy cluster divergence problem in the velocity spectrum, and achieving the goal of high-precision velocity analysis.

[0007] To achieve the above objectives, the present invention employs the following technical solution:

[0008] This invention discloses an optimization method for super-gathers in seismic velocity analysis, comprising the following steps:

[0009] CMP gather A1 is obtained by gather sorting the conventionally processed seismic data;

[0010] Interactive velocity analysis is performed on CMP gather A1 to pick up stacking velocity V1. Seismic head calculation is performed on CMP gather A1 to extract super gather A2 for denoising. Linear noise suppression is performed on super gather A2 to generate super gather A3.

[0011] Dynamic correction processing is performed on super gather A3 using superposition speed V1 to obtain dynamic correction CMP gather A4. Noise reduction, reverse correction, frequency filtering and amplitude equalization processing are performed on dynamic correction CMP gather A4 to obtain CMP gather A7.

[0012] A velocity spectrum is generated using CMP gather A7 and a super gather A8 is selected. The interaction velocity spectrum is calculated based on the super gather A8, and the super velocity analysis is performed to obtain the superimposed velocity V2. The superimposed velocity V2 is then combined with CMP gather A1 for subsequent superimposed processing.

[0013] Based on the above method, the present invention also discloses an optimization system for seismic velocity analysis super-gathers, comprising:

[0014] The gather sorting module is used to sort the conventionally processed seismic data to obtain CMP gather A1.

[0015] The gather processing module is used to perform interactive velocity analysis on CMP gather A1 and pick up the stacking velocity V1; perform seismic head calculation on CMP gather A1, extract the super gather A2 for denoising, perform linear noise suppression on super gather A2, and generate super gather A3.

[0016] CMP gather generation module: used to perform dynamic correction processing on super gather A3 using superposition speed V1 to obtain dynamic correction CMP gather A4, and to perform noise reduction, reverse correction, frequency filtering and amplitude equalization processing on dynamic correction CMP gather A4 to obtain CMP gather A7.

[0017] Data overlay module: Used to create velocity spectrum using CMP gather A7 and sort out super gather A8, calculate the interaction velocity spectrum based on super gather A8, perform interaction velocity analysis to obtain overlay velocity V2, and combine overlay velocity V2 with CMP gather A1 for subsequent overlay processing.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] This invention, without compromising the fidelity of subsequent data processing, adds seismic head information to low signal-to-noise ratio (SNR) gather data used for seismic velocity analysis. This allows for flexible extraction of super gathers and CMP gathers, suppressing linear and random noise in the super gathers. Furthermore, filtering and amplitude equalization are applied to enhance the energy of the dominant frequency band of reflected waves, thereby improving the SNR of super gathers in velocity analysis of oil and gas exploration data, mitigating energy cluster divergence in the velocity spectrum, and achieving high-precision velocity analysis. This method enables more focused energy clusters in the velocity spectrum, improves the SNR of super gathers, and enhances the energy of the dominant frequency band of reflected waves, significantly improving the accuracy and efficiency of velocity acquisition, obtaining more precise velocities, and enhancing the SNR and imaging accuracy of seismic data.

[0020] The system of this invention includes a gather sorting module, a gather processing module, a CMP gather generation module, and a data overlay module. The gather sorting module is used to sort the conventionally processed seismic data to obtain CMP gather A1. The gather processing module is used to perform interactive velocity analysis on CMP gather A1, picking the stacking velocity V1; performing seismic head calculation on CMP gather A1, extracting super gather A2 for denoising, and performing linear noise suppression on super gather A2 to generate super gather A3. The CMP gather generation module is used to perform dynamic correction processing on super gather A3 using the stacking velocity V1 to obtain dynamically corrected CMP gather A4; and performing denoising, inverse correction, frequency filtering, and amplitude equalization processing on dynamically corrected CMP gather A4 to obtain CMP gather A7. The data overlay module is used to generate a velocity spectrum from CMP gather A7 and sort out super gather A8. Based on super gather A8, an interactive velocity spectrum is calculated, and interactive velocity analysis is performed to obtain the overlay velocity V2. Overlay velocity V2 is then combined with CMP gather A1 for subsequent overlay processing. These modules work together to achieve flexible extraction of super gathers and CMP gathers, suppressing linear noise caused by gather combination and random noise on CMP gathers. Furthermore, filtering and equalization processing is used to enhance the energy of the dominant frequency band of reflected waves, thereby improving the signal-to-noise ratio of super gathers in the velocity analysis of oil and gas exploration data, mitigating the energy cluster divergence problem in the velocity spectrum, and achieving high-precision velocity analysis. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method of the present invention;

[0022] Figure 2a This is a CMP gather A1 diagram for velocity analysis according to the present invention;

[0023] Figure 2b This is another CMP gather A1 diagram for velocity analysis according to the present invention;

[0024] Figure 2c This is another CMP gather A1 diagram for velocity analysis according to the present invention;

[0025] Figure 3a This is a super-gathering A2 diagram of the CMP gather A1 before optimization processing according to the present invention;

[0026] Figure 3b The super gather diagram of the present invention is extracted after dynamic correction of CMP gather A1, suppression of linear noise by FK filtering, and subsequent reaction correction.

[0027] Figure 3c This is a diagram of super gather A3 after suppressing linear noise using KL transform, as shown in the present invention.

[0028] Figure 3dThe super gather A2 of the present invention is the super gather A8 after KL transform to suppress linear noise + random noise + filtering equalization;

[0029] Figure 4 The velocity spectrum generated by the CMP gather A1 of this invention;

[0030] Figure 5 The velocity spectrum of the CMP gather A1 of this invention after suppressing linear noise by FK filtering;

[0031] Figure 6 The velocity spectrum of the super gather A2 of the present invention after suppressing linear noise, random noise, and filtering equalization by KL transform;

[0032] Figure 7a This is an overlay plot of the velocity V1 obtained before the optimization processing of the CMP gather A1 in this invention;

[0033] Figure 7b This is a superimposed image of the velocity V2 obtained after optimization processing of the CMP gather A1 according to the present invention.

[0034] Figure 8a The velocity V1 diagram generated before super gather optimization for signal-to-noise ratio analysis of the super-data volume of this invention;

[0035] Figure 8b The velocity V2 diagram generated after super gather optimization for signal-to-noise ratio analysis of the superstructured data volume of this invention;

[0036] Figure 9 This is a flowchart illustrating the method of an embodiment of the present invention;

[0037] Figure 10 This is a system module diagram of the present invention. Detailed Implementation

[0038] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0039] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0040] The present invention will now be described in further detail with reference to the accompanying drawings:

[0041] See Figure 1 This invention discloses an optimization method for super gathers used in seismic velocity analysis, comprising the following steps:

[0042] S1. The CMP gather A1 is obtained by gathering the conventionally processed seismic data;

[0043] S2. Perform interactive velocity analysis on CMP gather A1, pick up stacking velocity V1, perform seismic head calculation on CMP gather A1, extract super gather A2 for denoising, perform linear noise suppression on super gather A2, and generate super gather A3.

[0044] S3. Use the super gather A3 to perform dynamic correction processing to obtain the dynamic correction CMP gather A4. Perform noise reduction, reverse correction, frequency filtering and amplitude equalization processing on the dynamic correction CMP gather A4 to obtain the CMP gather A7.

[0045] S4. Use CMP gather A7 to create a velocity spectrum and sort out super gather A8. Calculate the interaction velocity spectrum based on super gather A8, perform interaction velocity analysis to obtain the superimposed velocity V2, and combine the superimposed velocity V2 with CMP gather A1 for subsequent superimposed processing.

[0046] See Figure 1In another feasible embodiment of the present invention, the following modifications are made as needed. The conventionally processed seismic data is sorted into CMP gathers A1. Interactive velocity analysis is performed on CMP gather A1 to obtain stacking velocity V1. Seismic head calculations are performed on CMP gather A1 to extract super gather A2 for denoising. Linear noise suppression is applied to super gather A2 to generate super gather A3. Dynamic correction processing is performed on super gather A3 using stacking velocity V1 to obtain dynamically corrected CMP gather A4. Denoising, reaction correction, frequency filtering, and amplitude equalization are performed on dynamically corrected CMP gather A4 to obtain CMP gather A7. A velocity spectrum is generated using CMP gather A7, and super gather A8 is sorted out. Interactive velocity spectra are calculated based on super gather A8, and interactive velocity analysis is performed to obtain stacking velocity V2. Stacking velocity V2 is combined with CMP gather A1 for subsequent stacking processing. This invention, without compromising the fidelity of subsequent data processing, adds seismic head information to low signal-to-noise ratio (SNR) gather data used for seismic velocity analysis. This allows for flexible extraction of super gathers and CMP gathers, suppressing linear and random noise in the super gathers. Furthermore, filtering and amplitude equalization are applied to enhance the energy of the dominant frequency band of reflected waves, thereby improving the SNR of super gathers in oil and gas exploration data velocity analysis, mitigating energy cluster divergence in the velocity spectrum, and achieving high-precision velocity analysis. This method enables more focused energy clusters in the velocity spectrum, improves the SNR of super gathers, and enhances the energy of the dominant frequency band of reflected waves, significantly improving the accuracy and efficiency of velocity acquisition, obtaining more precise velocities, and enhancing the SNR and imaging accuracy of seismic data.

[0047] Example 1:

[0048] See Figure 1 This invention discloses an optimization method for super gathers used in seismic velocity analysis, comprising the following steps:

[0049] S1. The CMP gather A1 is obtained by gathering the conventionally processed seismic data;

[0050] The first key for the Dao Collection sorting input is CMPLINE, the second key is CMP, and the third key is offset.

[0051] S2. Perform interactive velocity analysis on CMP gather A1 and pick up the stacking velocity V1; perform seismic head calculation on CMP gather A1, extract the super gather A2 for denoising, perform linear noise suppression on super gather A2, and generate super gather A3.

[0052] Seismic head calculations were performed on CMP gather A1, and the super gather A2 used for denoising was extracted as follows:

[0053] Assign each M header word CMPLINE in CMP set A1 to a new header word iuse1, i.e., iuse1 = int(CMPLINE / M) + 1. Assign each N header word CMP in CMP set A1 to a new header word iuse2, i.e., iuse2 = int(CMP / N) + 1. The newly generated header words iuse1, iuse2, and offset can be extracted into the super set A2 for noise reduction.

[0054] Linear noise suppression is applied to super gather A2 to generate super gather A3, as follows:

[0055] Linear noise suppression is performed on super gather A2 using KL transform intrinsic filtering. This involves determining the dominant frequency and bandwidth of the linear noise through spectral analysis, predicting the linear noise within a defined time window based on the minimum and maximum apparent velocity ranges of the linear noise and the apparent velocity range of the effective wave, and then subtracting the predicted noise from super gather A2 to obtain super gather A3.

[0056] S3. Apply dynamic correction to super gather A3 using superposition velocity V1 to obtain dynamically corrected CMP gather A4. Perform noise reduction, inverse correction, frequency filtering, and amplitude equalization on dynamically corrected CMP gather A4 to obtain CMP gather A7, as detailed below:

[0057] Dynamic correction is applied to super gather A3 using superposition velocity V1 to obtain dynamic correction CMP gather A4. Random noise suppression is applied to dynamic correction CMP gather A4 to generate denoised dynamic correction CMP gather A5. Reverse correction is applied to dynamic correction CMP gather A5 to generate CMP gather A6. Frequency filtering and amplitude equalization are applied to CMP gather A6 to obtain CMP gather A7.

[0058] Dynamic correction processing is performed on super gather A3 using the superposition velocity V1 to obtain dynamically corrected CMP gather A4, as follows:

[0059] The super gather A3 is sorted and input using CMPLINE, CMP, and offset lead information. Dynamic correction processing is then performed on super gather A3 using the stacking velocity V1 to obtain the dynamically corrected CMP gather A4.

[0060] The formula for determining the dynamic correction amount for dynamic correction processing of super gather A3 is as follows:

[0061]

[0062] Where x is the gun-receiver distance, v1 is the first effective wave velocity, t(x) is the travel time of the reflected wave at non-zero gun-receiver distance, t(0) is the travel time of the reflected wave at zero gun-receiver distance, and Δt(x) is the dynamic correction.

[0063] Random noise suppression is applied to the dynamic correction CMP gather A4 to generate the denoised dynamic correction CMP gather A5, as follows:

[0064] Four-dimensional pre-stack random noise attenuation is performed on the dynamically corrected CMP gather A4. That is, the three-dimensional pre-stack seismic data is regarded as a four-dimensional data volume, with the four dimensions being the CMPLINE line number, CMP number, offset, and time. Based on the F-XYO domain prediction theory of three-dimensional frequency space, the three-dimensional prediction operator is obtained using the multi-channel complex least squares principle. The three-dimensional prediction operator is then used to perform prediction filtering on the four-dimensional seismic data volume of each frequency component to attenuate random noise and generate the dynamically corrected CMP gather A5.

[0065] The frequency filtering is a bandpass filter.

[0066] S4. Use CMP gather A7 to create a velocity spectrum and sort out super gather A8. Calculate the interaction velocity spectrum based on super gather A8, perform interaction velocity analysis to obtain the superimposed velocity V2, and combine the superimposed velocity V2 with CMP gather A1 for subsequent superimposed processing.

[0067] The velocity spectrum was constructed using CMP gather A7, and the super gather A8 was then sorted out as follows:

[0068] The first key for data sorting input is CMPLINE, the second key is CMP, and the third key is offset. The gather flag is set to CMP. For 3D seismic data, a rectangular combination graph is considered in both the CMPLINE and CMP directions. The adjacent CMP gathers around the center point of the rectangle are borrowed and combined to form the super gather A8 of this gather. The size of the combined graph is represented by the number of CMPLINEs M and the number of CMPs N, respectively. Figure 3d The super set A2 of the present invention is the super set A8 after KL transformation to suppress linear noise, random noise suppression, and filtering equalization.

[0069] Example 2:

[0070] See Figure 1 This invention discloses an optimization method for super gathers used in seismic velocity analysis, comprising the following steps:

[0071] S1. The seismic data after conventional processing is sorted into gathers to select CMP gather A1, i.e., common midpoint gather, for velocity analysis, as follows:

[0072] The seismic data after conventional processing is sorted into gathers. For 3D seismic data, the first key for data sorting input is CMPLINE, the second key is CMP, and the third key is offset. The sorting output is CMP gather A1 used for velocity analysis.

[0073] S2. Calculate the interaction velocity spectrum for CMP gather A1, perform interaction velocity analysis, and pick the superposition velocity V1.

[0074] S3. Perform seismic head calculation on CMP gather A1, and extract super gather A2 for denoising using the newly added super gather head information.

[0075] Seismic head calculations are performed on CMP gather A1, and super gather A2 is selected using the newly added head information. For 3D seismic data, every M head words CMPLINE in CMP gather A1 are assigned to a new head word iuse1, i.e., iuse1 = int(CMPLINE / M) + 1; every N head words CMP in CMP gather A1 are assigned to a new head word iuse2, i.e., iuse2 = int(CMP / N) + 1.

[0076] The newly generated trace heads iuse1, iuse2, and offset from the above two formulas can be used to extract the super set A2 for noise reduction.

[0077] S4. Perform linear noise suppression on super set A2 to generate super set A3.

[0078] Linear noise suppression is performed on super gather A2 using KL transform intrinsic filtering. The approach is to determine the dominant frequency and bandwidth of the linear noise through spectral analysis; based on the minimum and maximum apparent velocity ranges of the linear noise and the apparent velocity range of the effective wave, the linear noise is predicted within a defined time window; and then the predicted noise is subtracted from super gather A2 to obtain super gather A3.

[0079] S5. Sorting and inputting the super gather A3 using CMPLINE, CMP, and offset head information, and performing dynamic correction processing using the superimposed velocity V1, to obtain the dynamically corrected CMP gather data A4, as follows:

[0080] The super gather A3 is sorted and input using CMPLINE, CMP, and offset head information. Dynamic correction is performed using the superimposed speed V1. In practice, the dynamic correction amount can be determined according to the following formula, and then the super gather A3 is subjected to specific dynamic correction processing based on the dynamic correction amount.

[0081]

[0082] In the above formula, x is the gun-receiver distance, v1 is the first effective wave velocity, t(x) is the travel time of the reflected wave at non-zero gun-receiver distance, t(0) is the travel time of the reflected wave at zero gun-receiver distance, and Δt(x) is the dynamic correction amount.

[0083] S6. Perform random noise suppression on the dynamic correction CMP gather A4 to generate the denoised dynamic correction CMP gather A5.

[0084] Four-dimensional pre-stack random noise attenuation is performed on the dynamically corrected CMP gather A4, treating the three-dimensional pre-stack seismic data as a four-dimensional data volume with four dimensions: CMPLINE number, CMP number, offset, and time. Based on the F-XYO domain prediction theory of three-dimensional frequency space, the three-dimensional prediction operator is obtained using the multi-channel complex least squares principle. This prediction operator is then used to perform predictive filtering on the four-dimensional seismic data volume of this frequency component to attenuate random noise, generating the dynamically corrected CMP gather A5.

[0085] S7. Perform reverse correction on the dynamic correction CMP gather A5 to generate CMP gather A6.

[0086] Perform reverse correction on the dynamic correction CMP gather A5 to generate CMP gather A6. Specifically, the reverse correction process can be to add back the dynamic correction amount that was subtracted in the original dynamic correction process.

[0087] S8. After frequency filtering and amplitude equalization of CMP gather A6, CMP gather A7 is obtained, which enhances the energy of the dominant frequency band of the reflected wave.

[0088] The CMP gather A6 is subjected to frequency filtering and amplitude equalization. Bandpass filtering is used to highlight the dominant frequency band of the super gather. Then, an amplitude equalization function is applied according to the desired amplitude level to eliminate energy differences and enhance the energy of the dominant frequency band of the reflected wave. Bandpass filtering and equalization are well-known techniques and will not be described in detail in this invention.

[0089] S9. Using CMP gather A7 as input for velocity spectrum generation, sort out the super gather A8. The first key for data sorting input is CMPLINE, the second key is CMP, and the third key is offset. The gather flag is set to CMP. For 3D seismic data, consider a rectangular combination of CMPLINE and CMP directions. Borrow data from adjacent CMP gathers around the CMP gather at the center of the rectangle, combine the gathers, and calculate the super gather A8. The size of the combined graph is represented by the number of CMPLINEs M and the number of CMPs N, respectively.

[0090] S10. Calculate the interaction velocity spectrum based on the super gather A8, perform interaction velocity analysis to obtain the superposition velocity V2, and combine the superposition velocity V2 with the CMP gather A1 for subsequent superposition processing.

[0091] This invention, without compromising the fidelity of subsequent data processing, adds seismic head information to low signal-to-noise ratio gather data used for seismic velocity analysis. This enables flexible extraction of super gathers and CMP gathers, suppressing linear and random noise in the super gathers. Furthermore, filtering and amplitude equalization are applied to enhance the energy of the dominant frequency band of reflected waves. This method can make the velocity spectrum energy clusters more focused, improve the signal-to-noise ratio of super gathers, and enhance the energy of the dominant frequency band of reflected waves, thereby significantly improving the accuracy and efficiency of velocity picking, obtaining more accurate velocities, and improving the signal-to-noise ratio and imaging accuracy of seismic data.

[0092] This invention employs KL transform intrinsic filtering to achieve linear noise denoising and signal enhancement on super gathers, eliminating the need for dynamic and reactive corrections on CMP gathers, thus simplifying the processing flow and improving efficiency. Because the residual linear noise on CMP gathers lacks strong regularity, it is difficult to accurately identify the linear noise range on the FK spectrum, easily causing FK filtering denoising artifacts. This invention, based on the strong regularity of linear noise on extracted super common center gathers (hereinafter referred to as super gathers), uses the KL transform method to subtract the predicted linear noise from the seismic record, exhibiting good noise tolerance.

[0093] This invention employs four-dimensional denoising to suppress random noise, supplemented by filtering and amplitude equalization to highlight the superior frequency band and energy of the super gather, further enhancing the signal-to-noise ratio of the super gather, greatly improving the identification accuracy of velocity analysis, and increasing the efficiency of velocity picking.

[0094] Example 3:

[0095] See Figure 1 This invention discloses an optimization method for super gathers used in seismic velocity analysis, comprising the following steps:

[0096] The eastern part of a basin is characterized by shallow gas reservoirs and numerous strata. The original data was significantly affected by surface lithology, environmental noise, and strong interference from shallow loess, resulting in a relatively low signal-to-noise ratio in the initial stacked profiles, making pre-stack denoising for fidelity preservation challenging. Since there are no obvious strong marker beds in the shallow layers of this area, conventional processing in the early stages needed to balance signal-to-noise ratio with the protection of weak signals. After employing multi-round, multi-domain, and multi-method stepwise denoising, the data fidelity was high. Analysis of single-shot and CMP gather data after conventional denoising showed that... Figure 2a , Figure 2b and Figure 2c As shown, apart from random noise, there is no obvious regular noise. However, in the velocity analysis stage, due to the supergather data extracted from adjacent gather combinations, such as... Figure 3a As shown, strong linear noise becomes prominent, masking the effective signal and significantly reducing the signal-to-noise ratio of the super gather. This results in low accuracy in manual velocity analysis and affects the precise acquisition of velocity. Therefore, optimizing the input data for velocity analysis to improve the signal-to-noise ratio of the velocity spectrum is crucial.

[0097] Figure 9 The flowchart of the velocity analysis supergather optimization method under this patent is an application example of 3D data of the Loess Mountains in the eastern part of a basin. As shown in Figure 2, although the gathers have been denoised, no linear or other regular interferences are observed on the gathers except for random noise, and the signal-to-noise ratio is still low, with only the Permian Tp8 target layer visible. Figure 3a To optimize the super gathers before processing, the effective signal is overwhelmed by linear and random noise, especially above 1000ms where the hyperbolic in-phase axis is difficult to see, resulting in an extremely low signal-to-noise ratio. After gather combination, performing velocity analysis on the extracted super gathers becomes extremely difficult, such as... Figure 4 As shown, especially in the shallow and middle layers above 1000ms, the energy of the velocity spectrum is weak, no in-phase axis is seen in the dynamic correction super gather, and it is also difficult to see the in-phase axis in the stacking section, resulting in low recognition of the velocity by manual picking. Figure 3b The super gather is extracted from CMP gather A1 after dynamic correction and FK filtering to suppress linear noise. Since the linear noise remaining in the CMP gather is not very regular, it is difficult to accurately identify the range of linear noise on the FK spectrum, resulting in the linear noise not being completely removed after FK filtering and the signal-to-noise ratio not being improved much. Figure 3c The super gather A3 is the super gather A2 after suppressing linear noise through KL transform. Figure 3d The super gather A2 is a super gather A8 obtained by suppressing linear noise, random noise, and filtering equalization through KL transform. This super gather has a high signal-to-noise ratio, and after denoising and signal enhancement processing, the effective wave hyperbola is obvious. Figure 4 The velocity spectrum before gather optimization. Figure 5 The velocity spectrum of the CMP gather after conventional processing is filtered by FK to suppress linear noise. Figure 6 The velocity spectrum of the super gather A2 sorted using the newly added track head information of this invention, after KL transform suppression of linear noise + random noise suppression + filtering equalization, is obtained from... Figure 4 , Figure 5 , Figure 6 By comparing the velocity spectra, Figure 6 The effective information from multiple sets of shallow noise-submerged layers is highlighted, the velocity spectrum energy clusters are enhanced and focused, the signal-to-noise ratio of the super gather is improved, the reflection phase axis is leveled, and the imaging of multiple sets of weak phase axes in the shallow layer on the stacking section is clear. This greatly improves the efficiency and accuracy of manual interactive interpretation of the velocity, and provides accurate velocity basis for subsequent stacking processing. Figure 7 shows the comparison of the velocities V1 and V2 picked up before and after super gather optimization on gather A1. Figure 7a The summation of the velocity V1 obtained before gather optimization. Figure 7bThis is a superposition of the velocity V2 obtained after gather optimization. It can be seen that the optimized velocity V2 is more accurate, the imaging of weak layers in shallow, low SNR regions is improved, and the lateral continuity is enhanced. Figure 8 shows a comparison of the SNR analysis of the superimposed data volume shown in Figure 7, illustrating the statistical analysis of the SNR from 600-1400ms. Figure 8b Compared to Figure 8a The area of ​​the yellow region with a signal-to-noise ratio greater than 2.5 has increased significantly, further demonstrating that the velocity accuracy obtained before and after the super gather optimization has been greatly improved, and the imaging of the superimposed data has been significantly improved, laying the foundation for subsequent processing of the target area of ​​shallow multilayer systems in the eastern part of the basin.

[0098] Practice has proven that this method is highly feasible and can improve the velocity acquisition accuracy of signal-to-noise ratio data in the Loess Plateau region, providing accurate velocity data for subsequent residual static correction, stacking, and migration. This method can be applied to seismic data processing in the oil and gas sector and has excellent prospects for widespread application.

[0099] See Figure 10 Based on the above method, this invention discloses an optimization system for seismic velocity analysis super-gathers, comprising:

[0100] The gather sorting module is used to sort the conventionally processed seismic data to obtain CMP gather A1.

[0101] The gather processing module is used to perform interactive velocity analysis on CMP gather A1 and pick up the stacking velocity V1; perform seismic head calculation on CMP gather A1, extract the super gather A2 for denoising, perform linear noise suppression on super gather A2, and generate super gather A3.

[0102] CMP gather generation module: used to perform dynamic correction processing on super gather A3 using superposition speed V1 to obtain dynamic correction CMP gather A4, and to perform noise reduction, reverse correction, frequency filtering and amplitude equalization processing on dynamic correction CMP gather A4 to obtain CMP gather A7.

[0103] Data overlay module: Used to create velocity spectrum using CMP gather A7 and sort out super gather A8, calculate the interaction velocity spectrum based on super gather A8, perform interaction velocity analysis to obtain overlay velocity V2, and combine overlay velocity V2 with CMP gather A1 for subsequent overlay processing.

[0104] See Figure 10In another feasible embodiment of the present invention, the following modifications are made as appropriate. It includes a gather sorting module, a gather processing module, a CMP gather generation module, and a data overlay module. The gather sorting module is used to sort the conventionally processed seismic data to obtain CMP gather A1. The gather processing module is used to perform interactive velocity analysis on CMP gather A1, picking up the stacking velocity V1; performing seismic head calculation on CMP gather A1, extracting a super gather A2 for denoising, and performing linear noise suppression on super gather A2 to generate super gather A3. The CMP gather generation module is used to perform dynamic correction processing on super gather A3 using the stacking velocity V1 to obtain dynamically corrected CMP gather A4; and performing denoising, reaction correction, frequency filtering, and amplitude equalization processing on dynamically corrected CMP gather A4 to obtain CMP gather A7. The data overlay module is used to generate a velocity spectrum from CMP gather A7 and sort out super gather A8. Based on super gather A8, an interactive velocity spectrum is calculated, and interactive velocity analysis is performed to obtain the overlay velocity V2. Overlay velocity V2 is then combined with CMP gather A1 for subsequent overlay processing. These modules work together to achieve flexible extraction of super gathers and CMP gathers, suppressing linear noise caused by gather combination and random noise on CMP gathers. Furthermore, filtering and equalization processing is used to enhance the energy of the dominant frequency band of reflected waves, thereby improving the signal-to-noise ratio of super gathers in the velocity analysis of oil and gas exploration data, mitigating the energy cluster divergence problem in the velocity spectrum, and achieving high-precision velocity analysis.

[0105] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. An optimization method for seismic velocity analysis super-gathers, characterized in that, Includes the following steps: CMP gather A1 is obtained by gather sorting the conventionally processed seismic data; Interactive velocity analysis is performed on CMP gather A1 to pick up stacking velocity V1. Seismic head calculation is performed on CMP gather A1 to extract super gather A2 for denoising. Linear noise suppression is performed on super gather A2 to generate super gather A3. Dynamic correction is applied to super gather A3 using the superposition velocity V1 to obtain dynamically corrected CMP gather A4. Noise reduction, inverse correction, frequency filtering, and amplitude equalization are then performed on dynamically corrected CMP gather A4 to obtain CMP gather A7, as detailed below: Dynamic correction is applied to super gather A3 using superposition velocity V1 to obtain dynamic correction CMP gather A4. Random noise suppression is applied to dynamic correction CMP gather A4 to generate denoised dynamic correction CMP gather A5. Reverse correction is applied to dynamic correction CMP gather A5 to generate CMP gather A6. Frequency filtering and amplitude equalization are applied to CMP gather A6 to obtain CMP gather A7. A velocity spectrum is generated using CMP gather A7 and a super gather A8 is selected. The interaction velocity spectrum is calculated based on the super gather A8, and the super velocity analysis is performed to obtain the superimposed velocity V2. The superimposed velocity V2 is then combined with CMP gather A1 for subsequent superimposed processing.

2. The optimization method for seismic velocity analysis super-gathers as described in claim 1, characterized in that, The first key for the channel set sorting input is CMPLINE, the second key is CMP, and the third key is offset.

3. The optimization method for seismic velocity analysis super-gathers as described in claim 1, characterized in that, The process of performing seismic head calculations on CMP gather A1 and extracting super gather A2 for denoising is as follows: Assign each M header word CMPLINE in CMP set A1 to a new header word iuse1, i.e., iuse1 = int(CMPLINE / M) + 1. Assign each N header word CMP in CMP set A1 to a new header word iuse2, i.e., iuse2 = int(CMP / N) + 1. The newly generated header words iuse1, iuse2 and offset can be extracted into the super set A2 for noise reduction.

4. The optimization method for seismic velocity analysis super-gathers as described in claim 1, characterized in that, The linear noise suppression of super gather A2 to generate super gather A3 is as follows: Linear noise suppression is performed on super gather A2 using KL transform intrinsic filtering. This involves determining the dominant frequency and bandwidth of the linear noise through spectral analysis, predicting the linear noise within a defined time window based on the minimum and maximum apparent velocity ranges of the linear noise and the apparent velocity range of the effective wave, and then subtracting the predicted noise from super gather A2 to obtain super gather A3.

5. The optimization method for seismic velocity analysis super-gathers as described in claim 1, characterized in that, The dynamic correction process performed on the super gather A3 using the superposition velocity V1 to obtain the dynamically corrected CMP gather A4 is as follows: The super gather A3 is sorted and input using CMPLINE, CMP, and offset lead information. Dynamic correction processing is then performed on super gather A3 using the stacking velocity V1 to obtain the dynamically corrected CMP gather A4. The formula for determining the dynamic correction amount for dynamic correction processing of super gather A3 is as follows: Where x is the shot-receiver distance. Let t(x) be the first effective wave velocity, t(x) be the travel time of the reflected wave at non-zero gun-receiver distance, and t(0) be the travel time of the reflected wave at zero gun-receiver distance. This is a dynamic correction value.

6. The optimization method for seismic velocity analysis super-gathers as described in claim 1, characterized in that, The random noise suppression of the dynamic correction CMP gather A4 to generate the denoised dynamic correction CMP gather A5 is as follows: Four-dimensional pre-stack random noise attenuation is performed on the dynamically corrected CMP gather A4. That is, the three-dimensional pre-stack seismic data is regarded as a four-dimensional data volume, with the four dimensions being the CMPLINE line number, CMP number, offset, and time. Based on the F-XYO domain prediction theory of three-dimensional frequency space, the three-dimensional prediction operator is obtained using the multi-channel complex least squares principle. The three-dimensional prediction operator is then used to perform prediction filtering on the four-dimensional seismic data volume of each frequency component to attenuate random noise and generate the dynamically corrected CMP gather A5.

7. The optimization method for seismic velocity analysis super-gathers as described in claim 1, characterized in that, The frequency filtering is a bandpass filter.

8. The optimization method for seismic velocity analysis super-gathers as described in claim 1, characterized in that, The specific steps for constructing the velocity spectrum using CMP gather A7 and sorting out the super gather A8 are as follows: The first key for data sorting input is CMPLINE, the second key is CMP, and the third key is offset. The gather flag is set to CMP. For 3D seismic data, consider the rectangular combination graph of CMPLINE and CMP. Borrow the adjacent CMP gather data around the CMP gather at the center of the rectangle, perform gather combination, and calculate to form the super gather A8 of this gather. The size of the combined graph is represented by the number of CMPLINEs M and the number of CMPs N, respectively.

9. An optimization system for seismic velocity analysis super-gathers, characterized in that, include: The gather sorting module is used to sort the conventionally processed seismic data to obtain CMP gather A1. The gather processing module is used to perform interactive velocity analysis on CMP gather A1 and pick up the stacking velocity V1; perform seismic head calculation on CMP gather A1, extract the super gather A2 for denoising, perform linear noise suppression on super gather A2, and generate super gather A3. CMP gather generation module: Used to perform dynamic correction processing on super gather A3 using super gather rate V1 to obtain dynamically corrected CMP gather A4. Then, denoising, inverse correction, frequency filtering, and amplitude equalization processing are performed on dynamically corrected CMP gather A4 to obtain CMP gather A7, as detailed below: Dynamic correction is applied to super gather A3 using superposition velocity V1 to obtain dynamic correction CMP gather A4. Random noise suppression is applied to dynamic correction CMP gather A4 to generate denoised dynamic correction CMP gather A5. Reverse correction is applied to dynamic correction CMP gather A5 to generate CMP gather A6. Frequency filtering and amplitude equalization are applied to CMP gather A6 to obtain CMP gather A7. Data overlay module: Used to create velocity spectrum using CMP gather A7 and sort out super gather A8, calculate the interaction velocity spectrum based on super gather A8, perform interaction velocity analysis to obtain overlay velocity V2, and combine overlay velocity V2 with CMP gather A1 for subsequent overlay processing.

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