An iterative optimization method for improving the signal-to-noise ratio of seismic data in extremely shallow layers of sandstone-type uranium deposits

Abstract

The present invention belongs to the field of geophysical exploration and development of sandstone-type uranium resources, and discloses an iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data of sandstone-type uranium deposits. The present invention firmly grasps the essence of the problem of extremely shallow seismic data containing sandstone-type uranium reservoirs, adopts an iterative method for improving the signal-to-noise ratio, and on this basis, targets the problems of low coverage times and irregular data caused by extremely shallow seismic data affected by surface factors and acquisition factors, and also performs data regularization based on Fourier reconstruction. The method has great theoretical and technical progress and fully solves the problems of low signal-to-noise ratio, low coverage times and irregular data of extremely shallow seismic data of sandstone-type uranium deposits.

Description

Technical Field

[0001] The present invention belongs to the field of geophysical exploration and development of sandstone-type uranium ore resources, and relates to a targeted processing method for seismic data of extremely shallow layers containing sandstone-type uranium deposits, in particular to an iterative optimization method for improving the signal-to-noise ratio of seismic data of extremely shallow layers containing sandstone-type uranium deposits. Background Art

[0002] Although similar to oil and gas resources, sandstone-type uranium deposits are sedimentary deposits formed in sandstones at very shallow depths relative to oil and gas reservoirs (approximately 500 milliseconds in the southern Songliao Basin). Three-dimensional seismic data used for oil and gas exploration are significantly affected by surface factors in these intervals and suffer from severe under-representation. This results in a significantly low signal-to-noise ratio near the target horizon, thus limiting the ability to use 3D seismic exploration data to locate sandstone-type uranium deposits. The key to addressing this issue lies in scientifically and effectively suppressing noise in extremely shallow seismic data using relatively fidelity-preserving amplitude methods and leveraging existing effective information to moderately improve the regularity of seismic data in these shallow target horizons. In shallow seismic data, surface waves, strong energy, and shallow refraction interference (especially shallow refraction) significantly reduce the signal-to-noise ratio of reflection signals, significantly negatively impacting stacking and migration imaging. Therefore, noise suppression or filtering is essential.

[0003] Data regularization offers unique advantages in regularizing shot and checkpoint locations and improving CMP bin density. From a processing application perspective, regularization does not add additional geological information; it merely improves the spatial sampling properties of the observation system to better meet subsequent processing requirements. Therefore, denoising and data regularization techniques used in oil and gas exploration seismic data processing are specifically designed and optimized for processing extremely shallow seismic data from sandstone-type uranium deposits. This approach specifically suppresses surface waves, outliers, and shallow refractions in extremely shallow seismic data, employing highly targeted processes and parameters. Data regularization fully considers the contribution of near-offset data to extremely shallow seismic data, employing highly targeted parameters to regularize seismic traces within an offset of 2500 meters. Summary of the Invention

[0004] In order to make up for the shortcomings of the existing technology, the present invention proposes an iteratively optimized and highly targeted data processing method for improving the signal-to-noise ratio and data regularization of extremely shallow seismic data for sandstone-type uranium deposits.

[0005] Aiming at the special needs of data characteristics and data processing of extremely shallow layers of sandstone-type uranium deposits, the present invention proposes an iterative optimization method for improving the signal-to-noise ratio of seismic data of extremely shallow layers of sandstone-type uranium deposits. The method is used to solve the problems of low signal-to-noise ratio, low coverage times and poor data regularity caused by surface factors and acquisition factors in seismic data of extremely shallow layers of sandstone-type uranium reservoirs. Through the method of the present invention, the data signal-to-noise ratio is significantly improved, the coverage times and data regularity are better, which is conducive to subsequent stacking and migration imaging.

[0006] The purpose of the present invention is achieved through the following technical solutions and steps:

[0007] a. Read in 3D seismic data with a pre-defined observation system and field static correction, extract seismic traces with an offset less than or equal to 2500 meters, and truncate the trace length to 1500ms;

[0008] b. Analyze the distribution of shallow refraction in the data and its seismic characteristics, subdivide the seismic data into regions based on their distribution characteristics, select typical single shots in different regions, and perform denoising on these typical single shots;

[0009] c. Analyze the typical single-shot denoising effect, iteratively optimize and adjust the typical single-shot denoising scheme, set the threshold for manual judgment of denoising effect, and complete the optimization of sample denoising parameters when the expected effect is achieved;

[0010] d. De-noise the entire area with a single shot. Additionally, select a single shot for quality control and fine-tune the denoising parameters based on the quality control results to ultimately achieve suppression and removal of surface waves, outliers, and shallow refractions in the entire area.

[0011] e. Sorting the denoised seismic data into the CMP domain and performing dynamic correction processing;

[0012] f. Set data regularization parameters and transform the seismic data into the Fourier domain using the anti-leakage two-dimensional Fourier transform method;

[0013] g. According to the data characteristics and actual data processing needs, the expected output parameters are set, and the data is transformed back to the time-space domain using inverse Fourier transform. After a forward-inverse transform, the seismic data is transformed from the original irregular input data to regular output data;

[0014] h. Perform reaction correction on the regularized seismic data, suppress random noise on the gather, and finally output CMP gather data with improved signal-to-noise ratio.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] The present invention addresses the problems of low signal-to-noise ratio, low coverage, and data irregularity in seismic data from extremely shallow layers containing sandstone-type uranium reservoirs. It proposes a method for improving the signal-to-noise ratio and data regularity through progressive iterative optimization, thereby improving the quality of seismic data in this layer. The present invention firmly grasps the essence of the problems existing in seismic data from extremely shallow layers containing sandstone-type uranium reservoirs, adopts an iterative method for improving the signal-to-noise ratio, and on this basis, addresses the low coverage and data irregularity issues of extremely shallow seismic data caused by surface and acquisition factors, and also performs data regularization based on Fourier reconstruction. This method represents significant theoretical and technological advancement, fully resolving the problems of low signal-to-noise ratio, low coverage, and data irregularity in extremely shallow seismic data from sandstone-type uranium reservoirs.

[0017] Compared with other existing seismic data processing methods for oil and gas exploration, the method of the present invention has the following advantages:

[0018] ① Based on the characteristics of sandstone-type uranium deposits being located in extremely shallow layers, the method of the present invention significantly sorts and intercepts the original data, retaining only the effective seismic data for sandstone-type uranium deposit prediction. This not only ensures a more targeted seismic data processing link, but also greatly reduces the amount of seismic data to be processed, thereby greatly reducing the data processing cost and shortening the seismic data processing cycle while meeting the processing target requirements;

[0019] ② Compared with conventional denoising, the method of the present invention fully considers the relatively low signal-to-noise ratio of seismic data from extremely shallow sandstone-type uranium deposits during the iterative optimization denoising process. It adopts a meticulous and cautious denoising method that includes regional parameter selection, quality control fine-tuning, and full-area secondary denoising, thereby ensuring that effective signals are substantially unaffected while noise is removed as much as possible.

[0020] ③ The regularization method for extremely shallow data differs from the regularization of conventional oil and gas exploration seismic data in that the data must be screened before regularization. This is especially true in complex surface areas with severe surface variation. Based on the targeted zoning denoising in the early stages, the anti-leakage two-dimensional Fourier transform method is used for forward and inverse transformation to obtain regularized offset gather data.

[0021] ④ The targeted denoising method and data regularization method adopted by the method of the present invention have better applicability, so that the denoising effect after the final partition iterative optimization is significantly better than the conventional seismic data denoising effect;

[0022] ⑤ After comparing the stacked sections before and after denoising, and before and after regularization, the targeted processing of the present invention significantly improves the signal-to-noise ratio and event continuity of the extremely shallow seismic data;

[0023] ⑥ The extremely shallow seismic data of sandstone-type uranium deposits finally obtained by the present invention provide high signal-to-noise ratio gather data for subsequent resolution-enhancing processing and migration imaging, which has important practical significance and economic benefits for reducing the exploration risk of sandstone-type uranium deposits and improving the development efficiency of sandstone-type uranium deposits. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the technical solutions of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. The drawings described below are some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0025] Figure 1 It is a flow chart for realizing the method of the present invention;

[0026] Figure 2 It is a detailed diagram of the specific implementation process of the method of the present invention;

[0027] Figure 3 This is a seismic single shot profile before iterative optimization and denoising according to an embodiment of the present invention;

[0028] Figure 4 This is a seismic single shot profile after iterative optimization and denoising according to an embodiment of the present invention;

[0029] Figure 5 It is an embodiment of the present invention that iteratively optimizes the stacked profile before denoising;

[0030] Figure 6 It is a superimposed profile after iterative optimization and denoising according to an embodiment of the present invention;

[0031] Figure 7 This is one of the stacked sections before (left) and after (right) regularization of shallow data in an embodiment of the present invention;

[0032] Figure 8 This is the second of the superimposed sections before (left) and after (right) the regularization of shallow data in an embodiment of the present invention. Specific implementation methods

[0033] In order to make the technical means adopted by the present invention and the objectives achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0034] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0035] Example 1

[0036] The method of the present invention specifically comprises the following steps:

[0037] a. As attached Figure 2As shown in Figure a, 3D seismic data is read in. 3D seismic data requires pre-processing, observation system definition, trace header placement, and field static correction of shot gather data. The processing method of the present invention is highly targeted, processing only seismic data samples that contribute to the imaging of sandstone-type uranium reservoirs. Therefore, the read-in shot gather data is screened to retain only seismic traces with a shot offset of less than or equal to 2500 meters, and the trace length is truncated to 1500ms.

[0038] b. As attached Figure 2 As shown in Figure b, the noise distribution characteristics of the input 3D seismic data are analyzed in detail. Using elevation maps, surface lithology distribution maps, and other auxiliary methods, the data is partitioned according to the main noise types. In order to ensure the denoising effect, the partitioning should be as accurate as possible, so as to facilitate the setting of different characteristic denoising parameters. Typical single shots in different partitions are selected, and a characteristic parameter library is established based on their interference frequency, apparent velocity, amplitude, and other characteristics such as surface wave, wild value, and shallow refraction. Then, methods such as frequency-constrained apparent velocity filtering and frequency-constrained energy threshold are used to denoise the typical single shots.

[0039] c. As attached Figure 2 As shown in Figure c, the denoising effect of a typical single shot is analyzed. In particular, the denoising effect of shallow refraction interference must be quality-controlled shot by shot to ensure that while removing interference, the effective shallow reflection signal is not damaged as much as possible. Based on the quality control results, the denoising parameters of the area with unsatisfactory results are adjusted, and the typical single-shot denoising scheme is iteratively optimized. The threshold for manual judgment of denoising effect is set. The optimization of sample denoising parameters is completed when the expected effect is achieved.

[0040] d. As attached Figure 2 As shown in Figure d, all single shots in each partition are denoised using the denoising parameters after sample iterative optimization. Representative single shots are selected (the selection rule can be based on actual needs, such as selecting at fixed intervals) for quality control of the denoising effect. The quality control results are analyzed. If there are problems with the denoising effect of individual single shots, the denoising parameters are fine-tuned according to the actual situation and denoised again. Ultimately, the suppression and removal of surface waves, outliers, and shallow refractions in the entire area are achieved.

[0041] e. As attached Figure 2 As shown in Figure e, in order to perform subsequent data regularization processing, the denoised shot gather seismic data are sorted into the CMP domain, and the CMP gathers are subjected to dynamic correction processing; the dynamic correction velocity used here is the stacking velocity carefully picked on the denoised data;

[0042] f. As attached Figure 2As shown in Figure 5, the distribution characteristics of the bin density, shot and receiver position, coverage times, and azimuth angle of the CMP gather after dynamic correction in the time-space domain are analyzed. Then, the time window, space window, and frequency-wavenumber domain parameters of the Fourier transform are defined, and the anti-leakage two-dimensional Fourier transform method is used to transform the seismic data from the time-space domain to the Fourier domain.

[0043] g、As attached Figure 2 As shown in Figure g, according to the data characteristics and actual data processing needs, data regularization parameters are set (the specific parameters need to be determined according to the seismic data characteristics and target processing requirements), and the data is transformed back to the time-space domain using the anti-leakage two-dimensional Fourier inverse transform. After a forward-inverse transformation, the seismic data is transformed from the original irregular input data to regular output data;

[0044] h. As attached Figure 2 As shown in Figure h, the regularized seismic data is subjected to back-movement correction. Since regularization will produce a certain amount of random noise, the back-movement corrected data set is subjected to appropriate noise suppression, and the denoised and regularized seismic data for the extremely shallow layer without back-movement correction is obtained; finally, the CMP data set with improved signal-to-noise ratio and regularization is output.

[0045] Attachment Figure 3 , Attachment Figure 4 Provided with Figure 2 The processing effect diagram after iterative optimization denoising and shallow data regularization is shown in the figure. Figure 3 To iteratively optimize the single shot data before denoising, Figure 4 Comparing the two, we can see that the signal-to-noise ratio of the denoised single-shot data is significantly improved, and typical surface waves, outliers, and shallow refraction are all well suppressed.

[0046] Attachment Figure 5 To iteratively optimize the stacked profile before denoising, Figure 6 Comparing the two sections, we can see that after iterative optimization and denoising, the surface wave interference, outliers, and shallow refraction on the sections have been largely suppressed, and the signal-to-noise ratio of the stacked sections has been greatly improved.

[0047] Attachment Figure 7 and attached Figure 8 Comparisons of shallow data stacking sections before and after regularization are shown. Analysis of the stacked sections reveals a significant improvement in the signal-to-noise ratio (SNR) after denoising, a significant increase in the continuity of shallow target events after regularization, and the significant improvement in many shallow data gaps, including gaps in the original data.

[0048] In general, the embodiments fully illustrate that the present invention has a highly targeted method, which has a significant effect on improving the signal-to-noise ratio of extremely shallow seismic data for sandstone-type uranium deposits, and has important practical significance for improving the interpretation results of seismic data for sandstone-type uranium deposits in the next step, thereby further improving the exploration effect of sandstone-type uranium deposits.

[0049] Although some embodiments of the present invention have been described herein, those skilled in the art will appreciate that modifications may be made to the embodiments herein without departing from the spirit of the present invention. The above embodiments are merely exemplary and should not be used as limitations on the scope of the present invention.

Claims

1. An iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data in sandstone-type uranium deposits, characterized by: The following steps are involved: a. Read in the 3D seismic data with the observation system defined in advance and after field static correction; b. Analyze the distribution of shallow refraction in the data and its seismic characteristics, subdivide the seismic data into regions based on their distribution characteristics, select typical single shots in different regions, and perform denoising on these typical single shots; c. Analyze the typical single-shot denoising effect, iteratively optimize and adjust the typical single-shot denoising scheme, set the threshold for manual judgment of denoising effect, and complete the optimization of sample denoising parameters when the expected effect is achieved; d. De-noise the entire area with a single shot. Additionally, select a single shot for quality control and fine-tune the denoising parameters based on the quality control results to ultimately achieve suppression and removal of surface waves, outliers, and shallow refractions in the entire area. e. Sorting the denoised seismic data into the CMP domain and performing dynamic correction processing; f. Set data regularization parameters and transform the seismic data into the Fourier domain using the anti-leakage two-dimensional Fourier transform method; g. According to the data characteristics and actual data processing needs, the expected output parameters are set, and the data is transformed back to the time-space domain using inverse Fourier transform. After a forward-inverse transform, the seismic data is transformed from the original irregular input data to regular output data; h. Perform back-movement correction on the regularized seismic data to obtain denoised and regularized seismic data for the extremely shallow layer without back-movement correction, and finally output CMP gather data with improved signal-to-noise ratio.

2. The iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data in sandstone-type uranium deposits according to claim 1, characterized in that: The operation of step a is specifically as follows: extracting seismic traces with an offset less than or equal to 2500 meters, and truncating the seismic trace length to 1500 ms.

3. The iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data in sandstone-type uranium deposits according to claim 1, characterized in that: The specific operation of step b is as follows: typical single shots from different partitions are selected, and a feature parameter library is established based on their surface waves, outliers, interference frequency of shallow refraction, apparent velocity, and amplitude. Then, the typical single shots are denoised using the frequency-constrained apparent velocity filtering and frequency-constrained energy threshold methods.

4. The iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data in sandstone-type uranium deposits according to claim 1, characterized in that: The specific operations of step c are as follows: analyze the denoising effect of a typical single shot, especially to quality control the denoising effect of shallow refraction interference shot by shot, to ensure that while removing the interference, the shallow effective reflection signal is not damaged as much as possible; according to the quality control results, adjust the denoising parameters of the area with unsatisfactory effect, iteratively optimize the typical single shot denoising scheme, set the threshold for manual judgment of denoising effect, and complete the optimization of sample denoising parameters when the expected effect is achieved.

5. The iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data in sandstone-type uranium deposits according to claim 1, characterized in that: When selecting single shots in step d, select them at fixed intervals.

6. The iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data in sandstone-type uranium deposits according to claim 1, characterized in that: The speed of the dynamic correction described in step e is the superposition speed of fine picking on the denoised data.

7. The iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data in sandstone-type uranium deposits according to claim 1, characterized in that: The regularization parameters in step f include: the time window, the space window and the frequency-wavenumber domain parameters of the Fourier transform.

8. The iterative optimization method for improving the signal-to-noise ratio of extremely shallow seismic data in sandstone-type uranium deposits according to claim 1, characterized in that: The technical means used in step h to obtain the denoised and regularized seismic data for the extremely shallow layer without dynamic correction is to suppress random noise on the data set.

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