Seismic velocity analysis super gather optimization method and system

Abstract

The invention belongs to the field of oil-gas exploration seismic processing, and relates to a seismic velocity analysis super-gather optimization method and system. According to the method, on the premise that the fidelity of subsequent processing data is not affected, flexible extraction of the super gather and the CMP gather is achieved by adding seismic trace header information to low signal-to-noise ratio gather data used for seismic velocity analysis, the purpose of suppressing linear noise and random noise on the super gather is achieved, and the accuracy of seismic velocity analysis is improved. And filtering and amplitude equalization processing are assisted to enhance the dominant frequency band energy of reflected waves, so that the signal-to-noise ratio of a super gather in oil-gas exploration data velocity analysis is improved, the problem of energy group divergence on a velocity spectrum is improved, and the purpose of high-precision velocity analysis is achieved. According to the method, the velocity spectrum energy cluster is more focused, the signal-to-noise ratio of the super gather is improved, and the dominant frequency band energy of the reflected wave is enhanced, so that the precision and efficiency of velocity pickup are greatly improved, the more accurate velocity is obtained, and the signal-to-noise ratio and imaging precision of seismic data are improved.

Description

Technical Field

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

[0002] Velocity analysis is a crucial part of seismic data processing. Its purpose is to obtain accurate velocity parameters to improve the accuracy of seismic imaging. Velocity analysis is the basic guarantee for stacking and migration imaging, and the basis for reservoir prediction and inversion. With the continuous deepening of exploration, the demand for velocity accuracy in seismic imaging is also increasing. The main factors restricting the accuracy of velocity analysis are the energy cluster focusing degree of the velocity spectrum and the signal-to-noise ratio of the super gather.

[0003] When picking velocity spectrum interactively, we should consider whether the energy cluster of velocity spectrum is focused, whether the event axis of super gather is leveled, and the signal-to-noise ratio of stacking section. The three factors are combined to judge the velocity picking accuracy. Usually, stacking velocity analysis needs to combine adjacent CMP gathers of seismic data to form super gathers and then calculate velocity spectrum to improve the signal-to-noise ratio of velocity analysis. Therefore, the quality of super gather is the key factor restricting the picking accuracy of velocity analysis.

[0004] In the prior art, the idea of ​​performing three-dimensional filtering on the generated velocity spectrum has been proposed. FK filtering is applied to the CMP gather data after dynamic correction, and then dynamic correction is performed to achieve super gather sorting and produce velocity spectrum. Due to the use of FK filtering, while improving the signal-to-noise ratio of the input data of the interactive velocity spectrum, some artificial illusions or unclean elimination are inevitably generated. In addition, FK filtering is global and also eliminates effective waves that overlap with the linear noise band. Therefore, the characteristics and continuity of the effective waves after processing deteriorate, which seriously reduces the lateral resolution of the seismic record. The prior art also proposes a multi-link optimization velocity analysis method for gather data noise attenuation, phase time difference correction within the gather, and picking constraints, but there is no idea of ​​further denoising the linear noise of the super gather.

[0005] In the process of seismic data processing before velocity analysis, it is necessary to take into account the signal-to-noise ratio of the data while emphasizing the fidelity of the signal for subsequent processing. The characteristics of the weak linear noise remaining on the CMP gather are not obvious, but after the CMP gather combination, it will stand out on the super gather, drowning out the effective signal and reducing the recognition of the manually picked velocity. Summary of the invention

[0006] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a method and system for optimizing super gathers for seismic velocity analysis. Before calculating the velocity spectrum, the present invention adds seismic trace header information to achieve flexible extraction of super gathers and CMP gathers, thereby suppressing the linear noise generated by the gather combination and the random noise on the CMP gathers, and supplemented by filtering and equalization processing to enhance the energy of the dominant frequency band of the reflected wave, thereby improving the signal-to-noise ratio of the super gather in the velocity analysis of oil and gas exploration data, improving the problem of energy group divergence on the velocity spectrum, and achieving the purpose of high-precision velocity analysis.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The invention discloses a method for optimizing a seismic velocity analysis super gather, comprising the following steps:

[0009] The seismic data after conventional processing is sorted to obtain CMP gather A1;

[0010] Perform interactive velocity analysis on CMP gather A1, pick up stacking velocity V1, perform seismic trace header calculation on CMP gather A1, extract super gather A2 for denoising, perform linear noise suppression on super gather A2, and generate super gather A3;

[0011] The super gather A3 is subjected to a dynamic correction process using the stacking velocity V1 to obtain a dynamic correction CMP gather A4. The dynamic correction CMP gather A4 is subjected to denoising, back-correction, frequency filtering and amplitude equalization to obtain a CMP gather A7.

[0012] The velocity spectrum is made by using CMP gather A7 and super gather A8 is selected. The interactive velocity spectrum is calculated based on super gather A8, and the interactive velocity analysis is performed to obtain the stacking velocity V2. The stacking velocity V2 is combined with CMP gather A1 for subsequent stacking processing.

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

[0014] Gather sorting module: used to sort the seismic data after conventional processing to obtain CMP gather A1;

[0015] Gather processing module: used to perform interactive velocity analysis on CMP gather A1 and pick up stacking velocity V1; perform seismic trace header calculation on CMP gather A1, extract 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 stacking velocity V1 to obtain dynamic correction CMP gather A4, perform denoising, anti-dynamic correction, frequency filtering and amplitude equalization processing on dynamic correction CMP gather A4 to obtain CMP gather A7;

[0017] Data stacking module: used to make velocity spectrum using CMP gather A7 and select super gather A8, calculate interactive velocity spectrum based on super gather A8, perform interactive velocity analysis to obtain stacking velocity V2, and stacking velocity V2 is combined with CMP gather A1 for subsequent stacking processing.

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

[0019] The method of the present invention is based on the premise of not affecting the fidelity of the subsequent processing data. The low signal-to-noise ratio gather data used for seismic velocity analysis is used to add seismic header information to realize the flexible extraction of super gathers and CMP gathers, so as to suppress the linear noise and random noise on the super gathers, and is supplemented by filtering and amplitude equalization processing to enhance the energy of the dominant frequency band of the reflection wave, thereby improving the signal-to-noise ratio of the super gather in the velocity analysis of oil and gas exploration data, improving the energy group divergence problem on the velocity spectrum, and achieving the purpose of high-precision velocity analysis. The method of the present invention can make the velocity spectrum energy group more focused, improve the signal-to-noise ratio of the super gather, and enhance the energy of the dominant frequency band of the reflection wave, thereby greatly improving the accuracy and efficiency of velocity picking, obtaining more accurate velocity, and improving the signal-to-noise ratio and imaging accuracy of seismic data.

[0020] The system of the present invention includes a gather sorting module, a gather processing module, a CMP gather generation module and a data stacking module. The gather sorting module is used to perform gather sorting on seismic data after conventional processing to obtain CMP gather A1. The gather processing module is used to perform interactive velocity analysis on CMP gather A1 and pick up stacking velocity V1; perform seismic trace 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. The CMP gather generation module is used to perform dynamic correction processing on super gather A3 using stacking velocity V1 to obtain dynamic correction CMP gather A4, perform denoising, counter-correction, frequency filtering and amplitude equalization processing on dynamic correction CMP gather A4, and obtain CMP gather A7. The data stacking module is used to make velocity spectrum using CMP gather A7 and select super gather A8, calculate the interactive velocity spectrum according to super gather A8, perform interactive velocity analysis to obtain stacking velocity V2, and perform subsequent stacking processing on stacking velocity V2 in combination with CMP gather A1. The modules cooperate with each other to realize the flexible extraction of super gathers and CMP gathers, so as to suppress the linear noise generated by gather combination and the random noise on CMP gathers, and supplemented by filtering and equalization processing to enhance the energy of the dominant frequency band of the reflected wave, thereby improving the signal-to-noise ratio of super gathers in velocity analysis of oil and gas exploration data, improving the problem of energy group divergence on the velocity spectrum, and achieving the purpose of high-precision velocity analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the method of the present invention;

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

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

[0024] Figure 2c A1 diagram of a CMP gather used for velocity analysis according to the present invention;

[0025] Figure 3a It is a super gather A2 image sorted before the CMP gather A1 optimization treatment of the present invention;

[0026] Figure 3b It is a super gather map extracted after the CMP gather A1 of the present invention is corrected by moving average, linear noise is suppressed by FK filtering, and then the moving average is corrected;

[0027] Figure 3c It is a super gather A3 diagram of the present invention after the linear noise of super gather A2 is suppressed by KL transformation;

[0028] Figure 3dIt is the super gather A8 of the present invention after the linear noise is suppressed + random noise is suppressed + filtering equalization by KL transformation;

[0029] Figure 4 A velocity spectrum generated for the CMP gather A1 of the present invention;

[0030] Figure 5 It is the velocity spectrum of the CMP gather A1 of the present invention after the linear noise is suppressed by FK filtering;

[0031] Figure 6 It is the velocity spectrum of the super gather A2 of the present invention after linear noise suppression + random noise suppression + filtering equalization through KL transformation;

[0032] Figure 7a A superimposed diagram of the velocity V1 obtained before the CMP gather A1 optimization process of the present invention;

[0033] Figure 7b It is a superimposed diagram of the velocity V2 obtained after the CMP gather A1 is optimized in the present invention;

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

[0035] Figure 8b The velocity V2 map generated after the optimization of the signal-to-noise ratio analysis super gather of the stacked data volume of the present invention;

[0036] Fig. 9 is a flow chart of an embodiment method of the present invention;

[0037] Fig.10 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0040] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0041] See also Figure 1 The present invention discloses a method for optimizing a super gather for seismic velocity analysis, comprising the following steps:

[0042] S1. Perform gather sorting on the seismic data after conventional processing to obtain CMP gather A1;

[0043] S2. Perform interactive velocity analysis on CMP gather A1, pick up stacking velocity V1, perform seismic trace header 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. Performing NMO correction processing on the super gather A3 using the stacking velocity V1 to obtain the NMO correction CMP gather A4, performing denoising, anti-motion correction, frequency filtering and amplitude equalization processing on the NMO correction CMP gather A4 to obtain the CMP gather A7;

[0045] S4. Use CMP gather A7 to make velocity spectrum and select super gather A8. Calculate interactive velocity spectrum based on super gather A8, perform interactive velocity analysis to obtain stacking velocity V2, and combine stacking velocity V2 with CMP gather A1 for subsequent stacking processing.

[0046] See also Figure 1In another feasible embodiment of the present invention, the following is adaptively modified according to the situation. The seismic data after conventional processing is sorted to obtain CMP gather A1. Interactive velocity analysis is performed on CMP gather A1, and stacking velocity V1 is picked up. Seismic header operation is performed on CMP gather A1, and super gather A2 for denoising is extracted. Linear noise suppression is performed on super gather A2 to generate super gather A3. Super gather A3 is subjected to dynamic correction processing using stacking velocity V1 to obtain dynamic correction CMP gather A4. Dynamic correction CMP gather A4 is subjected to denoising, counter-correction, frequency filtering and amplitude equalization processing to obtain CMP gather A7. CMP gather A7 is used to make a velocity spectrum and sort out super gather A8. The interactive velocity spectrum is calculated based on super gather A8. Interactive velocity analysis is performed to obtain stacking velocity V2. Stacking velocity V2 is combined with CMP gather A1 for subsequent stacking processing. The present invention is based on the premise of not affecting the fidelity of the subsequent processing data. The low signal-to-noise ratio gather data used for seismic velocity analysis is added with seismic header information to realize flexible extraction of super gathers and CMP gathers, so as to suppress linear noise and random noise on the super gathers, and is supplemented by filtering and amplitude equalization processing to enhance the energy of the dominant frequency band of the reflection wave, thereby improving the signal-to-noise ratio of the super gather in the velocity analysis of oil and gas exploration data, improving the energy group divergence problem on the velocity spectrum, and achieving the purpose of high-precision velocity analysis. The method can make the energy group of the velocity spectrum more focused, improve the signal-to-noise ratio of the super gather, and enhance the energy of the dominant frequency band of the reflection wave, thereby greatly improving the accuracy and efficiency of velocity picking, obtaining more accurate velocity, and improving the signal-to-noise ratio and imaging accuracy of seismic data.

[0047] Embodiment 1:

[0048] See also Figure 1 The present invention discloses a method for optimizing a super gather for seismic velocity analysis, comprising the following steps:

[0049] S1. Perform gather sorting on the seismic data after conventional processing to obtain CMP gather A1;

[0050] The first keyword of the gather sorting input is CMPLINE, the second keyword is CMP, and the third keyword is offset.

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

[0052] The seismic trace header operation is performed on the CMP gather A1 to extract the super gather A2 for denoising as follows:

[0053] Assign every M trace header words CMPLINE in the CMP trace gather A1 to a new trace header word iuse1, that is, iuse1=int(CMPLINE / M)+1, and assign every N trace header words CMP in the CMP trace gather A1 to a new trace header word iuse2, that is, iuse2=int(CMP / N)+1. The newly added trace headers iuse1, iuse2 and offset generated by the above two formulas can be extracted to form the super trace gather A2 for denoising.

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

[0055] Linear noise is suppressed on super gather A2 by using KL transform intrinsic filtering, that is, the main frequency and frequency band range of linear noise are determined through spectrum analysis, and the linear noise is predicted within the defined time window according to the minimum and maximum apparent velocity range of linear noise and the apparent velocity range of effective waves. Then the predicted noise is subtracted from super gather A2 to obtain super gather A3.

[0056] S3. Performing NMO correction processing on super gather A3 using stacking velocity V1 to obtain NMO correction CMP gather A4, performing denoising, anti-motion correction, frequency filtering and amplitude equalization processing on NMO correction CMP gather A4 to obtain CMP gather A7, as follows:

[0057] Performing NMO correction on super gather A3 using stacking velocity V1 to obtain NMO correction CMP gather A4, performing random noise suppression on NMO correction CMP gather A4 to generate denoised NMO correction CMP gather A5, performing counter-correction on NMO correction CMP gather A5 to generate CMP gather A6, performing frequency filtering and amplitude equalization on CMP gather A6 to obtain CMP gather A7;

[0058] The super gather A3 is processed with the stacking velocity V1 to obtain the dynamic correction CMP gather A4 as follows:

[0059] The CMPLINE, CMP, and offset header information are selected and input into the super gather A3. The super gather A3 is subjected to dynamic correction processing using the stacking velocity V1 to obtain the dynamic correction CMP gather A4.

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

[0061]

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

[0063] The random noise of the NMO CMP gather A4 is suppressed to generate the denoised NMO CMP gather A5 as follows:

[0064] The four-dimensional pre-stack random noise attenuation is performed on the NMO CMP gather A4, that is, the three-dimensional pre-stack seismic data is regarded as a four-dimensional data body, and the four dimensions are 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 by using the multi-channel complex least squares principle, and the three-dimensional prediction operator is used to perform prediction filtering on the four-dimensional seismic data body of each frequency component to attenuate random noise and generate the NMO CMP gather A5.

[0065] The frequency filtering is bandpass filtering.

[0066] S4. Use CMP gather A7 to make velocity spectrum and select super gather A8. Calculate interactive velocity spectrum based on super gather A8, perform interactive velocity analysis to obtain stacking velocity V2, and combine stacking velocity V2 with CMP gather A1 for subsequent stacking processing.

[0067] The velocity spectrum is prepared using CMP gather A7 and super gather A8 is selected as follows:

[0068] The first keyword of data sorting input is CMPLINE, the second keyword is CMP, the third keyword is offset, and the gather flag is set to CMP. For 3D seismic data, the rectangular combination figures in the two directions of CMPLINE and CMP are considered, and the adjacent CMP gather data around the CMP gather at the center of the rectangle are borrowed. After the gathers are combined, the super gather A8 of this gather is calculated. The size of the combined figure is represented by the number of CMPLINEs M and the number of CMPs N, respectively. Figure 3d It is the super gather A8 of the present invention after the linear noise is suppressed + random noise is suppressed + filtering equalization is performed on the super gather A2 through KL transformation.

[0069] Embodiment 2:

[0070] See also Figure 1 The present invention discloses a method for optimizing a super gather for seismic velocity analysis, comprising the following steps:

[0071] S1. The seismic data after conventional processing are sorted to obtain CMP gather A1 for velocity analysis, i.e., common center point gather, as follows:

[0072] The seismic data after the previous routine processing is sorted. For three-dimensional seismic data, the first keyword of data sorting input is CMPLINE, the second keyword is CMP, and the third keyword is offset. The sorting output is CMP gather A1 for velocity analysis.

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

[0074] S3. Perform seismic trace header calculation on CMP gather A1, and use the newly added super gather header information to extract super gather A2 for denoising.

[0075] Perform seismic trace header calculation on CMP gather A1, and use the newly added trace header information to select super gather A2. For 3D seismic data, assign every M trace header words CMPLINE in CMP gather A1 to a new trace header word iuse1, i.e., iuse1=int(CMPLINE / M)+1; assign every N trace header words CMP in CMP gather A1 to a new trace header word iuse2, i.e., iuse2=int(CMP / N)+1;

[0076] The newly added trace headers iuse1, iuse2 and offset generated by the above two equations can be used to extract the super-trace gather A2 for denoising.

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

[0078] Linear noise is suppressed on super gather A2 by using KL transform intrinsic filtering. The idea is to determine the main frequency and frequency band range of linear noise through spectrum analysis; predict linear noise within the defined time window based on the minimum and maximum apparent velocity range of linear noise and the apparent velocity range of effective waves, and then subtract the predicted noise from super gather A2 to obtain super gather A3.

[0079] S5. Use CMPLINE, CMP, and offset header information to sort and input the super gather A3, and use the stacking velocity V1 to perform dynamic correction processing to obtain the dynamic correction CMP gather data A4, as follows:

[0080] The CMPLINE, CMP, and offset header information are sorted and input for super gather A3, and the stacking velocity V1 is used for dynamic correction processing. During specific implementation, the dynamic correction amount can be determined according to the following formula, and then specific dynamic correction processing is performed on the super gather A3 based on the dynamic correction amount.

[0081]

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

[0083] S6. Suppress random noise on the NMO CMP gather A4 to generate a denoised NMO CMP gather A5.

[0084] The four-dimensional prestack random noise attenuation is performed on the NMO CMP gather A4, that is, the three-dimensional prestack seismic data is regarded as a four-dimensional data body, and the four dimensions are 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 by using the multi-channel complex least square principle, and the prediction operator is used to perform prediction filtering on the four-dimensional seismic data body of the frequency component to attenuate random noise and generate the NMO CMP gather A5.

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

[0086] A back-correction is performed on the dynamic correction CMP gather A5 to generate a CMP gather A6. The back-correction process may specifically be to add back the dynamic correction amount subtracted in the original dynamic correction process.

[0087] S8. After performing frequency filtering and amplitude equalization processing on CMP gather A6, CMP gather A7 is obtained, so that the energy of the dominant frequency band of the reflected wave is enhanced.

[0088] The CMP gather A6 is subjected to frequency filtering and amplitude equalization processing. The frequency filtering is bandpass filtering to highlight the dominant frequency band of the super gather. Then, the amplitude equalization function is applied according to the desired amplitude level to eliminate the energy difference and enhance the energy of the dominant frequency band of the reflected wave. Bandpass filtering and equalization processing are well-known technologies and will not be elaborated in detail in the present invention.

[0089] S9. Take CMP gather A7 as input for velocity spectrum production and select super gather A8. The first keyword of data sorting input is CMPLINE, the second keyword is CMP, the third keyword is offset, and the gather flag is set to CMP. For three-dimensional seismic data, consider the rectangular combination figures in the two directions of CMPLINE and CMP, borrow the adjacent CMP gather data around the CMP gather at the center of the rectangle, and calculate and form the super gather A8 of this gather after gather combination. The size of the combination figure is represented by the number of CMPLINE M and the number of CMP N respectively.

[0090] S10. Calculate the interaction velocity spectrum according to the super gather A8, perform interaction velocity analysis to obtain the stacking velocity V2, and perform subsequent stacking processing on the stacking velocity V2 in combination with the CMP gather A1.

[0091] The present invention is based on the premise of not affecting the fidelity of subsequent processed data. By adding seismic trace header information to the low signal-to-noise ratio gather data used for seismic velocity analysis, the flexible extraction of super gathers and CMP gathers is achieved, so as to suppress the linear noise and random noise on the super gathers, and is supplemented by filtering and amplitude equalization processing to enhance the energy of the dominant frequency band of the reflected wave. This method can make the velocity spectrum energy group more focused, improve the signal-to-noise ratio of the super gather, and enhance the energy of the dominant frequency band of the reflected wave, thereby greatly improving the accuracy and efficiency of velocity picking, obtaining more accurate velocity, and improving the signal-to-noise ratio and imaging accuracy of seismic data.

[0092] The present invention uses KL transform intrinsic filtering to realize linear noise denoising and signal enhancement processing on super gathers, without the need for dynamic correction and anti-dynamic correction processing on CMP gathers, thus simplifying the processing flow and improving work efficiency. Since the linear noise remaining on the CMP gathers is not regular, it is difficult to accurately identify the linear noise range on the FK spectrum, which can easily cause the FK filtering denoising illusion. Based on the strong regularity of linear noise on the extracted super common center point gathers (abbreviated as: super gathers), the present invention uses the KL transform method to subtract the predicted linear noise from the seismic record, which has good noise tolerance.

[0093] The present invention adopts four-dimensional denoising to suppress random noise, supplemented by filtering and amplitude equalization processing to highlight the advantageous frequency band and energy of the super gather, further enhancing the signal-to-noise ratio of the super gather, greatly improving the recognition of velocity analysis, and improving the efficiency of velocity picking.

[0094] Embodiment three:

[0095] See also Figure 1 The present invention discloses a method for optimizing a super gather for seismic velocity analysis, comprising the following steps:

[0096] The eastern part of a basin has the characteristics of shallow gas reservoirs and multiple layers. The original data is affected by the strong interference of surface lithology, environmental noise and shallow loess. The signal-to-noise ratio of the initial stacked profile is relatively low, and pre-stack fidelity denoising is difficult. There is no obvious strong marker layer in the shallow layer of this area. In the early conventional processing, it is necessary to take into account the protection of signal-to-noise ratio and weak signals. After multi-round multi-domain multi-method step-by-step denoising, the data fidelity is high. After conventional denoising, analysis of single shot and CMP gather data, such as Figure 2a , Figure 2b and Figure 2c As shown in Figure 2, except for random noise, there is no obvious regular noise. However, in the velocity analysis, due to the super gather data extracted by combining adjacent gathers, such as Figure 3a As shown in the figure, strong linear noise is formed, which covers up the effective signal and greatly reduces the signal-to-noise ratio of the super gather. The recognition of manual interactive velocity analysis is low, which affects the accurate picking of velocity. Therefore, it is particularly important to optimize the input data of velocity analysis and improve the signal-to-noise ratio of the velocity spectrum.

[0097] Fig. 9 The flow chart of the velocity analysis super gather optimization method of this claim is an application example of 3D data of loess mountain in the eastern part of a basin. As shown in Figure 2, although the several gathers displayed have been denoised, except for the random noise that has not been suppressed, no linear or other regular interference is seen on the gathers, and the signal-to-noise ratio is still low, and only the Permian Tp8 target layer is visible. Figure 3a In order to optimize the super gathers before processing, the linear noise and random noise overwhelmed the effective signal, especially the hyperbolic phase axis was difficult to see above 1000ms, and the signal-to-noise ratio was extremely low. After the gathers were combined, it was extremely difficult to perform velocity analysis on the extracted super gathers, such as Figure 4 As shown, the energy of the velocity spectrum in the shallow and middle layers above 1000ms is weak, no event axis is seen in the dynamic correction super gather, and it is difficult to see the event axis in the stacking section, and the recognition degree of the artificially picked velocity is low. Figure 3b This is a super gather extracted from CMP gather A1 after conventional processing in the early stage, after dynamic correction and FK filtering to suppress linear noise. Since the residual linear noise on the CMP gather is not regular, it is difficult to accurately identify the linear noise range on the FK spectrum, resulting in the linear noise not being completely removed after FK filtering, and the signal-to-noise ratio is not greatly improved. Figure 3c The super gather A3 is the super gather A2 after the linear noise is suppressed by KL transformation. Figure 3d Super gather A8 is the result of super gather A2 undergoing KL transformation to suppress linear noise + random noise + filtering equalization. This super gather has a high signal-to-noise ratio. After denoising and signal enhancement processing in the shallow layer, the effective wave hyperbola is obvious. Figure 4 is the velocity spectrum before gather optimization processing, Figure 5 This is the velocity spectrum of the CMP gather after conventional processing in the early stage and the linear noise is suppressed by FK filtering. Figure 6 The velocity spectrum after linear noise suppression + random noise suppression + filtering equalization of the super gather A2 selected by the newly added header information of the present invention is obtained by KL transformation. Figure 4 , Figure 5 , Figure 6 Comparing the velocity spectrum, Figure 6 The effective information of multiple shallow layers that were submerged by noise is highlighted, the energy cluster of the velocity spectrum is enhanced and focused, the signal-to-noise ratio of the super gather is enhanced, the reflection event is leveled, and the shallow multiple weak event images on the stacking section are clear, which greatly improves the efficiency and interpretation accuracy of the manual interactive interpretation speed and provides an accurate velocity basis for subsequent stacking processing. Figure 7 shows the stacking comparison of the velocity V1 and V2 picked up before and after the super gather optimization on the gather A1. Figure 7a This is the superposition of the velocity V1 obtained before the gather optimization process. Figure 7bThis is the superposition of the velocity V2 obtained after the gather optimization processing. It can be seen that the velocity V2 picked up after optimization is more accurate, the imaging of weak layers in the shallow low signal-to-noise ratio area is improved, and the lateral continuity is enhanced. Figure 8 is the SNR analysis comparison of the stacked data volume shown in Figure 7. The statistical analysis of the SNR from 600-1400ms shows that 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 indicating that the velocity accuracy obtained before and after the super gather optimization has been greatly improved, and the stacking data imaging has been significantly improved, laying the foundation for the subsequent processing of the shallow multi-layer target area in the eastern part of the basin.

[0098] Practice has proved that this method is highly feasible and can improve the velocity picking accuracy of the signal-to-noise ratio data in the loess plateau area, providing accurate velocity for subsequent residual static correction, stacking, and migration. This method can be applied to seismic data processing in the oil and gas field and has a good promotion prospect.

[0099] See also Fig.10 Based on the above method, the present invention discloses an optimization system for seismic velocity analysis super gathers, comprising:

[0100] Gather sorting module: used to sort the seismic data after conventional processing to obtain CMP gather A1;

[0101] Gather processing module: used to perform interactive velocity analysis on CMP gather A1 and pick up stacking velocity V1; perform seismic trace header calculation on CMP gather A1, extract 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 stacking velocity V1 to obtain dynamic correction CMP gather A4, perform denoising, anti-dynamic correction, frequency filtering and amplitude equalization processing on dynamic correction CMP gather A4 to obtain CMP gather A7;

[0103] Data stacking module: used to make velocity spectrum using CMP gather A7 and select super gather A8, calculate interactive velocity spectrum based on super gather A8, perform interactive velocity analysis to obtain stacking velocity V2, and stacking velocity V2 is combined with CMP gather A1 for subsequent stacking processing.

[0104] See also Fig.10In another feasible embodiment of the present invention, the following is adaptively modified according to the situation. It includes a gather sorting module, a gather processing module, a CMP gather generation module and a data stacking module. The gather sorting module is used to perform gather sorting on the seismic data after conventional processing to obtain CMP gather A1. The gather processing module is used to perform interactive velocity analysis on the CMP gather A1 and pick up the stacking velocity V1; perform seismic trace head calculation on the CMP gather A1, extract the super gather A2 for denoising, perform linear noise suppression on the super gather A2, and generate a super gather A3. The CMP gather generation module is used to perform dynamic correction processing on the super gather A3 using the stacking velocity V1 to obtain a dynamic correction CMP gather A4, perform denoising, counter-correction, frequency filtering and amplitude equalization processing on the dynamic correction CMP gather A4 to obtain a CMP gather A7. The data stacking module is used to make velocity spectrum using CMP gather A7 and select super gather A8, calculate the interactive velocity spectrum according to super gather A8, perform interactive velocity analysis to obtain stacking velocity V2, and perform subsequent stacking processing on stacking velocity V2 in combination with CMP gather A1. The modules cooperate with each other to realize the flexible extraction of super gathers and CMP gathers, so as to suppress the linear noise generated by gather combination and the random noise on CMP gathers, and supplemented by filtering and equalization processing to enhance the energy of the dominant frequency band of the reflected wave, thereby improving the signal-to-noise ratio of super gathers in velocity analysis of oil and gas exploration data, improving the problem of energy group divergence on the velocity spectrum, and achieving the purpose of high-precision velocity analysis.

[0105] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for optimizing supergathers for seismic velocity analysis, characterized in that: The following steps are involved: The seismic data after conventional processing is sorted to obtain CMP gather A1; Perform interactive velocity analysis on CMP gather A1, pick up stacking velocity V1, perform seismic trace header calculation on CMP gather A1, extract super gather A2 for denoising, perform linear noise suppression on super gather A2, and generate super gather A3; The super gather A3 is subjected to a dynamic correction process using the stacking velocity V1 to obtain a dynamic correction CMP gather A4, and the dynamic correction CMP gather A4 is subjected to denoising, anti-dynamic correction, frequency filtering and amplitude equalization to obtain a CMP gather A7; The velocity spectrum is made by using CMP gather A7 and super gather A8 is selected. The interactive velocity spectrum is calculated based on super gather A8, and the interactive velocity analysis is performed to obtain the stacking velocity V2. The stacking velocity V2 is combined with CMP gather A1 for subsequent stacking processing.

2. The method for optimizing a seismic velocity analysis supergather according to claim 1, characterized in that: The first keyword of the gather sorting input is CMPLINE, the second keyword is CMP, and the third keyword is offset.

3. The method for optimizing a seismic velocity analysis supergather according to claim 1, characterized in that: The seismic trace header operation is performed on the CMP gather A1 to extract the super gather A2 for denoising as follows: Assign every M trace header words CMPLINE in the CMP trace gather A1 to a new trace header word iuse1, that is, iuse1=int(CMPLINE / M)+1, and assign every N trace header words CMP in the CMP trace gather A1 to a new trace header word iuse2, that is, iuse2=int(CMP / N)+1. The newly added trace headers iuse1, iuse2 and offset generated by the above two formulas can be extracted to form the super trace gather A2 for denoising.

4. The method for optimizing a seismic velocity analysis supergather according to claim 1, characterized in that: The linear noise suppression of super gather A2 to generate super gather A3 is specifically as follows: Linear noise is suppressed on super gather A2 by using KL transform intrinsic filtering, that is, the main frequency and frequency band range of linear noise are determined through spectrum analysis, and the linear noise is predicted within the defined time window according to the minimum and maximum apparent velocity range of linear noise and the apparent velocity range of effective waves. Then the predicted noise is subtracted from super gather A2 to obtain super gather A3.

5. The method for optimizing a seismic velocity analysis supergather according to claim 1, characterized in that: The super gather A3 is subjected to a dynamic correction process by using the stacking velocity V1 to obtain a dynamic correction CMP gather A4, and the dynamic correction CMP gather A4 is subjected to denoising, counter-correction, frequency filtering and amplitude equalization to obtain a CMP gather A7 as follows: The super gather A3 is subjected to dynamic correction processing by using the stacking velocity V1 to obtain the dynamic correction CMP gather A4. Random noise is suppressed on the dynamic correction CMP gather A4 to generate the denoised dynamic correction CMP gather A5. The dynamic correction CMP gather A5 is subjected to counter-dynamic correction to generate CMP gather A6. The CMP gather A6 is subjected to frequency filtering and amplitude equalization to obtain CMP gather A7.

6. The method for optimizing a seismic velocity analysis supergather according to claim 5, characterized in that: The method of performing dynamic correction processing on the super gather A3 by using the stacking velocity V1 to obtain the dynamic correction CMP gather A4 is as follows: The CMPLINE, CMP, and offset header information are selected and input into the super gather A3. The super gather A3 is subjected to dynamic correction processing using the stacking velocity V1 to obtain the dynamic correction CMP gather A4. The formula for determining the dynamic correction amount for super gather A3 is as follows: Where x is the offset, v1 is the first effective wave velocity, t(x) is the travel time of the reflected wave at non-zero offset, t(0) is the travel time of the reflected wave at zero offset, and Δt(x) is the dynamic correction.

7. The method for optimizing a seismic velocity analysis supergather according to claim 5, characterized in that: The random noise suppression of the NMO CMP gather A4 to generate the denoised NMO CMP gather A5 is specifically as follows: The four-dimensional pre-stack random noise attenuation is performed on the NMO CMP gather A4, that is, the three-dimensional pre-stack seismic data is regarded as a four-dimensional data body, and the four dimensions are 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 by using the multi-channel complex least squares principle, and the three-dimensional prediction operator is used to perform prediction filtering on the four-dimensional seismic data body of each frequency component to attenuate random noise and generate the NMO CMP gather A5.

8. The method for optimizing a seismic velocity analysis supergather according to claim 5, characterized in that: The frequency filtering is bandpass filtering.

9. The method for optimizing a seismic velocity analysis supergather according to claim 1, characterized in that: The method of using CMP gather A7 to prepare velocity spectrum and select super gather A8 is as follows: The first keyword of data sorting input is CMPLINE, the second keyword is CMP, the third keyword is offset, and the gather flag is set to CMP. For three-dimensional seismic data, the rectangular combination figures in the two directions of CMPLINE and CMP are considered, and the adjacent CMP gather data around the CMP gather at the center point of the rectangle are borrowed. After the gathers are combined, the super gather A8 of this gather is calculated. The size of the combined figure is represented by the number of CMPLINE M and the number of CMP N, respectively.

10. An optimization system for seismic velocity analysis supergathers according to claim 1, characterized in that: include: Gather sorting module: used to sort the seismic data after conventional processing to obtain CMP gather A1; Gather processing module: used to perform interactive velocity analysis on CMP gather A1 and pick up stacking velocity V1; perform seismic trace header calculation on CMP gather A1, extract 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 stacking velocity V1 to obtain dynamic correction CMP gather A4, perform denoising, anti-dynamic correction, frequency filtering and amplitude equalization processing on dynamic correction CMP gather A4 to obtain CMP gather A7; Data stacking module: used to make velocity spectrum using CMP gather A7 and select super gather A8, calculate interactive velocity spectrum based on super gather A8, perform interactive velocity analysis to obtain stacking velocity V2, and stacking velocity V2 is combined with CMP gather A1 for subsequent stacking processing.

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