A post-stack seismic data processing method and device, electronic equipment and medium

Abstract

Embodiments of the present application provide a post-stack seismic data processing method and device, electronic equipment and medium, the method comprises: obtaining data to be processed; the data to be processed includes post-stack seismic data corresponding to at least one stratum; determine the target stratum; along the target stratum, layer flattening processing is obtained, and the flattening body data is obtained; the flattening body data is continuously wavelet transformed to obtain the frequency expansion data; the frequency expansion data is subjected to inverse layer flattening processing and singular value decomposition denoising processing to obtain denoising data; for the denoising data, anisotropic laplace filtering processing is carried out to obtain optimization data. Embodiments of the present application can optimize the post-stack seismic data to improve the signal-to-noise ratio and resolution of the data.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method, apparatus, electronic device and medium for post-stack seismic data processing. Background Technology

[0002] Seismic data analysis of a region is beneficial for advancing the exploration of resources such as oil. However, the complex surface types in some work areas affect the overall signal-to-noise ratio and resolution of the seismic acquisition data. In addition, in slope areas, the strata dip is relatively steep, which can affect the imaging effect of the data.

[0003] In existing technologies, considering the unique surface environment and complex geological structure of the work area, the resolution and signal-to-noise ratio of seismic data are improved from the following two aspects: ① In acquisition, a high-density acquisition method combining well shot, air gun, and source is adopted; ② In processing, wavelet consistency processing is used to eliminate differences in frequency, waveform, and phase of wavelets, improving wavelet consistency and ensuring the resolution and signal-to-noise ratio of seismic data. Although the above methods improve the quality of seismic data after joint acquisition and processing, the overall signal-to-noise ratio and resolution of the original seismic data are still relatively low due to the influence of complex surface excitation and reception conditions and stratigraphic structure, which cannot meet the requirements for carrying out detailed structural interpretation and reservoir prediction.

[0004] Therefore, the above methods still have certain limitations and are not suitable for widespread use. Summary of the Invention

[0005] In view of the above problems, embodiments of the present invention are proposed to provide a post-stack seismic data processing method, apparatus, electronic device and medium that overcomes or at least partially solves the above problems.

[0006] To address the aforementioned problems, this invention discloses a post-stack seismic data processing method, comprising:

[0007] Acquire data to be processed; the data to be processed includes post-stack seismic data corresponding to at least one stratum;

[0008] Identify the target stratum;

[0009] The target stratum is flattened to obtain flattened body data.

[0010] The flattened body data is frequency-spread based on continuous wavelet transform to obtain frequency-spread data;

[0011] The frequency extension data is subjected to inverse flattening and singular value decomposition denoising to obtain denoised data;

[0012] Anisotropic Laplacian filtering is performed on the denoised data to obtain optimized data.

[0013] Optionally, it also includes:

[0014] Inversion is performed based on the optimized data to obtain inversion data;

[0015] By comparing the inversion data with the preset reference data, the conformity rate of the optimized data is determined.

[0016] Optionally, the step of performing frequency diffraction on the flattened body data to obtain frequency diffraction data includes:

[0017] The flattened body data is decomposed by continuous wavelet transform to obtain decomposed data; the decomposed data includes seismic signals corresponding to multiple frequencies.

[0018] Determine the frequency extension parameters; the frequency extension parameters include the frequency extension range and the reference frequency.

[0019] Based on the overlay parameters and the seismic signal, the overlay data is reconstructed to obtain the overlay data.

[0020] Optionally, the step of performing anti-flattening and denoising processing on the spread spectrum data to obtain denoised data includes:

[0021] The topology data is reverse-flattened to obtain reverse-flattened data.

[0022] The reverse-flattened data is subjected to singular value decomposition for denoising to obtain denoised data.

[0023] Optionally, the singular value decomposition denoising process performed on the flattened data to obtain denoised data includes:

[0024] Generate a Hankel matrix that matches the reverse flattening data;

[0025] Singular value decomposition is performed on the Hankel matrix to obtain one or more corresponding singular values ​​and singular value vectors;

[0026] Determine the target singular value according to preset rules;

[0027] Denoising data is generated using the target singular value and the singular value vector corresponding to the target singular value.

[0028] Optionally, the step of filtering the denoised data to obtain optimized data includes:

[0029] Based on the denoised data, the gradient structure tensor is calculated;

[0030] Based on the gradient structure tensor, the seismic profile confidence information is determined;

[0031] Based on the aforementioned gradient structure tensor, the dip angle of the formation;

[0032] Based on the confidence information and the formation dip angle, an anisotropic Laplace filter is constructed;

[0033] The anisotropic Laplace filter is used to process the denoised data to obtain optimized data;

[0034] The size and shape of the filter window in the anisotropic Laplace filter are determined by the confidence information, and the window orientation in the anisotropic Laplace filter is determined by the formation dip angle; the confidence information includes confidence in linear structure features and confidence in lateral discontinuity structure features.

[0035] This invention also discloses a post-stack seismic data processing apparatus, comprising:

[0036] The data acquisition module is used to acquire data to be processed; the data to be processed includes post-stack seismic data corresponding to at least one stratum.

[0037] The target formation determination module is used to determine the target formation.

[0038] The flattened body data generation module is used to perform layer flattening processing along the target stratum to obtain flattened body data;

[0039] The frequency extension data generation module is used to perform continuous wavelet transform on the flattened body data to obtain frequency extension data;

[0040] The denoised data generation module is used to perform anti-flattening processing and singular value decomposition denoising processing on the frequency extension data to obtain denoised data.

[0041] The filtering module is used to perform anisotropic Laplacian filtering on the denoised data to obtain optimized data.

[0042] Optionally, it also includes:

[0043] The inversion module is used to perform inversion based on the optimized data to obtain inversion data;

[0044] The comparison module is used to compare the inversion data with preset reference data to determine the compliance rate of the optimized data.

[0045] This invention also discloses an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the post-stack seismic data processing method as described above.

[0046] This invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the post-stack seismic data processing method described above.

[0047] The embodiments of the present invention have the following advantages:

[0048] Extended seismic data is obtained by flattening the post-stack seismic data along the target strata and then performing continuous wavelet transform to broaden the seismic data bandwidth. The extended data is then subjected to inverse flattening and singular value decomposition denoising to effectively remove random noise and obtain denoised data. Anisotropic Laplace filtering is then applied to the denoised data to effectively attenuate random noise, enhance the lateral consistency of the reflection phase axes, and preserve the stratigraphic edges and fine structures within the profile. The optimized data obtained after these processes has a better signal-to-noise ratio and resolution compared to the initial post-stack seismic data.

[0049] Furthermore, by performing inversion processing based on optimized data, more accurate interpretive data can be obtained, thereby improving the accuracy of stratigraphic prediction in the exploration area. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the steps of an embodiment of the post-stack seismic data processing method of the present invention;

[0051] Figure 2 This is a schematic diagram of a noise reduction process provided in an embodiment of the present invention;

[0052] Figure 3a This is a cross-sectional schematic diagram of a portion of the frequency bands before frequency band extension, provided in an embodiment of the present invention;

[0053] Figure 3b This is a cross-sectional schematic diagram of a portion of the frequency bands after frequency band extension provided in an embodiment of the present invention;

[0054] Figure 4a This is a cross-sectional schematic diagram of the noise reduction and filtering processes provided in the embodiments of the present invention;

[0055] Figure 4b This is a cross-sectional schematic diagram of the noise reduction and filtering processes provided in the embodiments of the present invention;

[0056] Figure 5a This is a schematic diagram of the inversion profile of the data to be processed provided in an embodiment of the present invention;

[0057] Figure 5b This is a schematic diagram of the inversion profile of the optimized data provided in an embodiment of the present invention;

[0058] Figure 6a This is a schematic diagram of data inversion before frequency spreading provided in an embodiment of the present invention;

[0059] Figure 6b This is a schematic diagram of data inversion after frequency extension provided in an embodiment of the present invention;

[0060] Figure 7 This is a structural block diagram of an embodiment of a post-stack seismic data processing device according to the present invention. Detailed Implementation

[0061] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] Reference Figure 1 The diagram illustrates a step flowchart of an embodiment of a post-stack seismic data processing method according to the present invention, which may specifically include the following steps:

[0063] Step 101: Obtain the data to be processed; the data to be processed includes post-stack seismic data corresponding to at least one stratum;

[0064] Post-stack seismic data consists of one or more earthquake-related time series information.

[0065] Initial seismic data can be obtained by collecting reflected signals from a designated location or area using detection equipment. The initial seismic data can then be superimposed and suppressed one or more times according to certain rules to obtain post-stack seismic data.

[0066] The embodiments of the present invention mainly focus on the processing of post-stack seismic data. The generation process of post-stack data is not considered a limitation of the embodiments of the present invention. Post-stack seismic data obtained by those skilled in the art in different ways do not affect the embodiments of the present invention.

[0067] Step 102: Determine the target stratum;

[0068] Post-stack seismic data may contain data corresponding to different strata. Since the analysis of post-stack seismic data is generally aimed at one or more specific strata, the target strata that need to be optimized can be determined according to actual needs or research objectives.

[0069] Step 103: Perform layer flattening along the target stratum to obtain flattened body data;

[0070] Stratigraphic flattening is primarily used for paleogeographic reconstruction in the interpretation and study of conventional seismic data. It is a method based on terrestrial sequence stratigraphy theory, using geological and seismic data as its material basis, and combining multiple factors. This method allows for isochronous correlation of strata, achieving high accuracy and thus better reflecting the original paleogeographic features.

[0071] Since post-stack data may contain strata with excessively steep dips, in order to avoid the problem of unsatisfactory processing results caused by vertical time variations in post-stack seismic data, layer flattening can be performed along the target strata after the target strata are determined to obtain flattened body data.

[0072] By utilizing the isochronism of flattening along the target strata, the longitudinal time window of the data is greatly shortened, reducing the computational error caused by time variation. The resulting flattened body data is beneficial for selecting reasonable seismic wavelets to carry out subsequent frequency extension work.

[0073] Step 104: Perform continuous wavelet transform on the flattened body data to obtain the frequency-spreading data;

[0074] The principle of continuous wavelet transform frequency extension technology is based on the assumption of approximate energy level of the amplitude spectrum of broadband high-resolution seismic signals. It compensates for the high-frequency energy lost due to propagation in the continuous wavelet domain to restore the high-resolution characteristics of seismic data. This method can simultaneously consider the relationship between time and frequency resolution, and can also extend both high and low frequencies, both by two octaves, and can reasonably adjust its frequency band structure.

[0075] By performing continuous wavelet transform on the obtained flattened body data, wider bandwidth data can be obtained compared to the flattened body data, resulting in more high-resolution content in the extended data.

[0076] Step 105: Perform anti-flattening processing and singular value decomposition denoising processing on the frequency extension data to obtain denoised data;

[0077] Reverse flattening is the reverse of layer flattening. By performing reverse flattening on the spread spectrum data obtained after spread spectrum, the post-stack data state is restored. Since the data obtained after reverse flattening generally retains a lot of random noise, singular value decomposition denoising can effectively reduce the random noise in the data and obtain denoised data. The denoised data has a higher signal-to-noise ratio than the data to be processed.

[0078] Step 106: Perform anisotropic Laplace filtering on the denoised data to obtain optimized data.

[0079] Anisotropic Laplace filters were used to filter the denoised data. The resulting optimized data not only effectively attenuated random noise and enhanced the lateral consistency of the reflection phase axis, but also preserved the stratigraphic edges and fine structures within the profile, which helped improve the reliability of subsequent seismic data interpretation.

[0080] In this embodiment of the invention, extended frequency data is obtained by flattening the post-stack seismic data along the target strata and then performing continuous wavelet transform extension processing to broaden the frequency band of the seismic data. The extended frequency data is then subjected to inverse flattening and singular value decomposition denoising processing to effectively remove random noise and obtain denoised data. Anisotropic Laplace filtering processing is then applied to the denoised data to effectively attenuate random noise, enhance the lateral consistency of the reflection phase axis, and maintain the stratigraphic edges and fine structures within the profile. The optimized data obtained after the above processing has a better signal-to-noise ratio and resolution compared to the initial post-stack seismic data, which is beneficial for the inversion processing of seismic data and stratigraphic correlation prediction.

[0081] In an optional embodiment of the present invention, the method further includes:

[0082] Inversion is performed based on the optimized data to obtain inversion data;

[0083] By comparing the inversion data with the preset reference data, the conformity rate of the optimized data is determined.

[0084] Inversion processing is performed based on optimized data, including but not limited to statistical inversion and deterministic inversion, to generate inversion data. By comparing the inversion data with reference data, the conformity rate of the optimized data with the reference data can be determined.

[0085] The embodiments of the present invention do not limit the content, form, or generation method of the reference data, as long as the conformity rate of the inversion data can be determined through the reference data.

[0086] In an optional embodiment of the present invention, step 104 includes:

[0087] Sub-step S11 involves performing continuous wavelet transform decomposition on the flattened body data to obtain decomposed data; the decomposed data includes seismic signals corresponding to multiple frequencies.

[0088] The decomposed data can be divided into multiple frequency band intervals for subsequent processing. For example, it can be divided into five frequency bands: below 10Hz (Hertz), 10–20Hz, 20–40Hz, 40–60Hz, and 60–70Hz. It is understood that it can also be divided into other numbers of frequency bands in other ways, and this embodiment of the invention does not limit this.

[0089] Sub-step S12: Determine the frequency extension parameters; the frequency extension parameters include the frequency extension range and the reference frequency;

[0090] Determine the reference frequency and extension range for the frequency band. Different reference frequencies may be obtained due to the data to be processed and different data optimization objectives. Analyze the data from the reference frequency upwards and downwards to determine the frequency range containing valid data. Based on the frequency range containing valid information, determine the extension range.

[0091] Sub-step S13 involves reconstructing the seismic signal based on the overlay parameters to obtain overlay data.

[0092] By using a reference frequency and a widened frequency range, and while maintaining the phase information in the seismic signal, the seismic signal is reconstructed using the broadened spectrum to obtain widened frequency data. This widened frequency data has a wider bandwidth than the flattened data. The widened frequency data is essentially a seismic trace time series.

[0093] In an optional embodiment of the present invention, step 105 may include:

[0094] Sub-step S21: Perform reverse flattening processing on the frequency extension data to obtain reverse flattening data;

[0095] Sub-step S22: Perform singular value decomposition denoising on the reverse flattened data to obtain denoised data.

[0096] The topology data is reverse flattened to obtain reverse flattened data. Reverse flattening is the reverse process of layer flattening, which will not be elaborated here.

[0097] By performing reverse flattening, the stratigraphic attitude of the data is restored. Since the reverse flattened data still retains random noise from the data to be processed, singular value decomposition (SVD) can be used to remove the random noise from the reverse flattened data, resulting in denoised data.

[0098] In an optional embodiment of the present invention, sub-step S22 may include:

[0099] Sub-step S221: Generate a Hankel matrix that matches the reverse flattening data;

[0100] Sub-step S222: Perform singular value decomposition on the Hankel matrix to obtain one or more corresponding singular values ​​and singular value vectors;

[0101] The above singular values ​​are non-zero singular values. Substep S222 yields the left singular matrix, the diagonal matrix with singular values ​​as diagonal elements, and the right singular matrix.

[0102] Singular value vectors can include left singular value vectors obtained from the column vectors of the left singular matrix and right singular value vectors obtained from the column vectors of the right singular matrix.

[0103] Sub-step S223: Determine the target singular value according to preset rules;

[0104] Specific singular values ​​are selected as target singular values ​​by using preset rules.

[0105] Sub-step S224: Using the target singular value and the singular value vector corresponding to the target singular value, generate denoised data.

[0106] Denoising data is generated using the target singular values ​​and their corresponding singular value vectors. By removing the parts of the Hankel matrix that do not correspond to the target singular values, random noise in the reverse-flattened data can be effectively reduced.

[0107] Reference Figure 2 The diagram illustrates a process provided by an embodiment of the present invention. In one example, the data before singular value denoising processing is as follows: Figure 2 As shown in (a), this is a black and white image containing 15*25 pixels, as... Figure 2 The black and white image shown in (b) contains three types of columns, such as Figure 2 The black and white image shown in (c) can be represented as a 15*25 matrix M with a total of 375 elements. Singular value decomposition of matrix M yields three non-zero singular values:

[0108] σ1=14.72, σ2=5.22, σ3=3.31,

[0109] Therefore, matrix M can be represented as:

[0110]

[0111] The above data represents ideal conditions; actual data may vary. Figure 2 As shown in (d), there is a lot of noise mixed in with the effective signal. When the above singular value decomposition is used, the corresponding matrix M′ and multiple non-zero singular values ​​σ are obtained. i The matrix M' can be represented as:

[0112]

[0113] Among them, u i For left singular value vectors, It is a right singular vector.

[0114] Non-zero singular values ​​can include (sorted from largest to smallest): σ1 = 14.15, σ2 = 4.67, σ3 = 3.00, σ4 = 0.21, σ5 = 0.19, ..., σ 15 =0.05.

[0115] Clearly, the first three singular values ​​are much larger than the others, indicating that they contain the vast majority of the information, namely:

[0116]

[0117] The first three singular values ​​can be taken as the target singular value, and the singular value vector corresponding to the target singular value can be used to generate denoised data.

[0118] In an optional embodiment of the present invention, step 106 includes: calculating a gradient structure tensor based on the denoised data; determining seismic profile confidence information based on the gradient structure tensor; determining the dip angle of the strata based on the gradient structure tensor; constructing an anisotropic Laplace filter based on the confidence information and the dip angle of the strata; processing the denoised data using the anisotropic Laplace filter to obtain optimized data; wherein the size and shape of the filter window in the anisotropic Laplace filter are determined by the confidence information, and the window direction in the anisotropic Laplace filter is determined by the dip angle of the strata; the confidence information includes confidence in linear structure features and confidence in lateral discontinuity structure features.

[0119] By employing the aforementioned filtering method, gradient structure tensor is used to estimate stratigraphic dip and analyze the regularity of stratigraphic structure. Based on this, confidence measures for two structural features in seismic profiles—linearity and lateral discontinuity—are introduced. These two confidence measures are used to adjust the scale and shape of the filtering window of the adaptive median filter. The direction of the filter window function is adjusted using the stratigraphic dip angle, thereby enabling the filtering window to optimally match and process the stratigraphic structural features in the neighborhood. This effectively solves the problems of random noise attenuation and effective signal fidelity in seismic profiles. It not only effectively attenuates random noise and enhances the lateral consistency of reflection phase axes but also preserves stratigraphic edges and fine structures within the profile, contributing to improved reliability of subsequent seismic data interpretation.

[0120] By processing post-stack seismic data through embodiments of the present invention, the following effects are achieved, including but not limited to:

[0121] 1) Imaging effect of seismic data:

[0122] Reference Figure 3a This shows a cross-sectional schematic diagram of a portion of the frequency band before frequency extension provided in an embodiment of the present invention; refer to Figure 3b The diagram shows a cross-sectional view of a portion of the frequency bands after frequency extension provided in an embodiment of the present invention.

[0123] Depend on Figure 3a and Figure 3b It can be seen that the main frequency band has been significantly improved, and the effective information in the low and high frequencies is richer than that in the original data.

[0124] Reference Figure 4a This shows a cross-sectional schematic diagram of the noise reduction and filtering processes provided in an embodiment of the present invention; refer to Figure 4b The diagram shows a cross-sectional view after noise reduction and filtering processing provided in an embodiment of the present invention.

[0125] Further singular value decomposition denoising and anisotropic Laplace filtering are applied to the spread spectrum data. Figure 4a and Figure 4b The comparison shows that the signal-to-noise ratio and resolution of the seismic data after singular value decomposition denoising and anisotropic Laplace filtering are improved, the correlation of the synthetic record calibration results of the wells is also better, the sand body superposition relationship characteristics are clear and obvious, and the correspondence with the wells is accurate.

[0126] 2) Reservoir prediction results:

[0127] Reference Figure 5a A schematic diagram of the inversion profile of the data to be processed provided in an embodiment of the present invention is shown; refer to Figure 5b The diagram shows an inversion profile of the optimized data provided in an embodiment of the present invention.

[0128] By comparison Figure 5a and Figure 5b It can be seen that using high-resolution seismic data after frequency extension to carry out specific reservoir prediction research in the study area, the inversion results show that the resolution of the seismic data impedance inversion results after processing by the embodiment of the present invention is significantly improved, and the sand body distribution characteristics are clear.

[0129] 3) Promotion and application effects:

[0130] Reference Figure 6a This illustrates a schematic diagram of data inversion before frequency spreading provided in an embodiment of the present invention; refer to Figure 6b The diagram illustrates a data inversion diagram after frequency extension provided in an embodiment of the present invention.

[0131] Applying the above methods to areas outside the study area, and comparing... Figure 6a and Figure 6b As can be seen, the signal-to-noise ratio and resolution of the seismic data processed by the method described in the embodiments of the present invention are significantly improved, the dominant frequency is increased, and the stacking and lateral distribution characteristics of the vertical sand bodies are clear, laying a data foundation for subsequent reservoir prediction.

[0132] The embodiments of the present invention will be further explained and illustrated by the following examples.

[0133] The target detection area has a complex surface type, mainly consisting of Gobi Desert and wind-eroded hills, followed by water bodies, swamps and reeds, and farmland. This diverse surface type affects the overall signal-to-noise ratio and resolution of the seismic acquisition data. Furthermore, in slope areas, the strata dip steeply, which negatively impacts the imaging quality. The specific implementation method of this invention to improve the resolution and signal-to-noise ratio of seismic data in this area is as follows:

[0134] 1) Export the target layer data along the layer, and flatten this data volume along the target layer to generate a flattened body;

[0135] To ensure the timeliness of data processing, a 300ms time window is first applied along the target layer (target stratum) to export the target layer data. This data volume is then flattened to generate a flattened volume. The vertical time window of the data volume is reduced from the original 4300ms to 2000ms, which reduces the calculation error caused by unreasonable wavelet selection due to vertical time variation.

[0136] 2) Perform frequency extension processing on the flattened body using the continuous wavelet transform algorithm;

[0137] Frequent analysis of seismic data from the study area revealed that, using -20 dB as the bandwidth standard, the target layer's bandwidth is 5-60 Hz, with a dominant frequency of 30 Hz. However, in reality, seismic signals between -20 dB and -30 dB still possess a certain signal-to-noise ratio, providing a basis for frequency upscaling. To further determine the distribution range of the effective signal, a series of parameters, including LP10 Hz, BP(10-20) Hz, BP(20-40) Hz, BP(40-60) Hz, and BP(60-80) Hz, were used to perform frequency-division scanning of the seismic data. The scanning results showed that at 60 Hz, some effective information was still faintly visible, and random noise was not significant. Therefore, the high-frequency extension end was set to 80 Hz, and the low-frequency end was optimized to maximize the utilization of effective low-frequency information. The data before and after frequency upscaling showed a significant increase in the dominant frequency band, with richer effective low- and high-frequency information compared to the original data. After determining the frequency upscaling parameters, calculations were performed to generate a flattened upscaling data volume.

[0138] 3) The flattened body after the frequency extension process is flattened back, and then singular value decomposition and noise reduction processing is performed;

[0139] Then, the flattened data after frequency upscaling is reversed. At this point, due to the increased frequency of the seismic data, some random interference is generated. Next, singular value decomposition (SVD) is used to select effective components, reconstruct a new signal, and select appropriate denoising parameters (including but not limited to non-zero target singular values) to remove random interference. This method is very effective for random noise in post-stack data.

[0140] 4) Perform anisotropic Laplace filtering on the denoised data.

[0141] Even after removing random noise, the data volume still suffers from poor continuity of phase axes inherited from the original post-stack data. To address this issue, we further employ anisotropic Laplace filtering to improve the continuity of the phase axes. Anisotropic Laplace filtering, also known as edge-preserving filtering, effectively attenuates random noise, enhances the lateral consistency of reflection phase axes, and preserves stratigraphic edges and fine structures within the profile, thus contributing to improved reliability in subsequent seismic data interpretation.

[0142] 5) Conduct post-stack inversion work to verify the reliability of the data and finally obtain high-resolution, high signal-to-noise ratio post-stack seismic data of the target detection area.

[0143] Finally, post-stack inversion work was conducted for different target stratigraphic systems, and reservoir prediction was studied using both post-stack statistical inversion and deterministic inversion methods, both of which achieved good results. We statistically analyzed the oil-producing reservoirs at different locations in the target detection area, and the accuracy of the inversion results using seismic data with improved signal-to-noise ratio and resolution was significantly improved, increasing from 70% to 93%.

[0144] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0145] Reference Figure 7 The diagram shows a structural block diagram of an embodiment of a post-stack seismic data processing device according to the present invention, which may specifically include the following modules:

[0146] The data acquisition module 701 is used to acquire data to be processed; the data to be processed includes post-stack seismic data corresponding to at least one stratum.

[0147] Target formation determination module 702 is used to determine the target formation;

[0148] The flattened body data generation module 703 is used to perform layer flattening processing along the target stratum to obtain flattened body data;

[0149] The frequency extension data generation module 704 is used to perform continuous wavelet transform on the flattened body data to obtain frequency extension data;

[0150] The denoising data generation module 705 is used to perform anti-flattening processing and singular value decomposition denoising processing on the frequency extension data to obtain denoised data.

[0151] The filtering module 706 is used to perform anisotropic Laplacian filtering on the denoised data to obtain optimized data.

[0152] In an optional embodiment of the present invention, the device further includes:

[0153] The inversion module is used to perform inversion based on the optimized data to obtain inversion data;

[0154] The comparison module is used to compare the inversion data with preset reference data to determine the compliance rate of the optimized data.

[0155] In an optional embodiment of the present invention, the frequency extension data generation module 704 includes:

[0156] The decomposition submodule is used to perform continuous wavelet transform decomposition on the flattened body data to obtain decomposed data; the decomposed data includes seismic signals corresponding to multiple frequencies.

[0157] The frequency extension parameter determination submodule is used to determine the frequency extension parameters, which include the frequency extension range and the reference frequency.

[0158] The signal reconstruction submodule is used to reconstruct the signal based on the overlay parameters and the seismic signal to obtain overlay data.

[0159] In an optional embodiment of the present invention, the denoised data generation module 705 includes:

[0160] The anti-flattening processing submodule is used to perform anti-flattening processing on the topology data to obtain anti-flattened data;

[0161] The singular value decomposition denoising processing submodule is used to perform singular value decomposition denoising processing on the reverse flattened data to obtain denoised data.

[0162] In an optional embodiment of the present invention, the singular value decomposition denoising processing submodule includes:

[0163] A matrix construction unit is used to generate a Hankel matrix that matches the reverse-flattening data;

[0164] A singular value decomposition unit is used to perform singular value decomposition on the Hankel matrix to obtain one or more corresponding singular values ​​and singular value vectors.

[0165] The target singular value determination unit is used to determine the target singular value according to a preset rule.

[0166] The denoised data generation unit is used to generate denoised data using the target singular value and the singular value vector corresponding to the target singular value.

[0167] In an optional embodiment of the present invention, the filtering module 706 includes:

[0168] The gradient structure tensor calculation submodule is used to calculate the gradient structure tensor based on the denoised data;

[0169] The confidence information determination submodule is used to determine the seismic profile confidence information based on the gradient structure tensor.

[0170] The formation dip angle determination submodule is used to determine the formation dip angle based on the gradient structure tensor.

[0171] An anisotropic Laplace filter construction submodule is used to construct an anisotropic Laplace filter based on the confidence information and the formation dip angle.

[0172] The filtering submodule is used to process the denoised data using the anisotropic Laplace filter to obtain optimized data;

[0173] The size and shape of the filter window in the anisotropic Laplace filter are determined by the confidence information, and the window orientation in the anisotropic Laplace filter is determined by the formation dip angle; the confidence information includes confidence in linear structure features and confidence in lateral discontinuity structure features.

[0174] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0175] This invention also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the various processes of the above-described post-stack seismic data processing method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here.

[0176] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described post-stack seismic data processing method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0177] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0178] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0179] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0180] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0183] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0184] The foregoing has provided a detailed description of the post-stack seismic data processing method, apparatus, electronic device, and medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for processing post-stack seismic data, characterized in that, include: Obtain the data to be processed; The data to be processed includes post-stack seismic data corresponding to at least one stratum; Identify the target stratum; The target stratum is flattened to obtain flattened body data. The flattened body data is subjected to continuous wavelet transform for frequency extension to obtain the frequency extension data. The frequency extension data is subjected to inverse flattening and singular value decomposition denoising to obtain denoised data; Anisotropic Laplacian filtering is performed on the denoised data to obtain optimized data; Inversion is performed based on the optimized data to obtain inversion data; By comparing the inversion data with preset reference data, the conformity rate of the optimized data is determined; The process of performing anisotropic Laplacian filtering on the denoised data to obtain optimized data includes: Based on the denoised data, the gradient structure tensor is calculated; Based on the gradient structure tensor, the seismic profile confidence information is determined; Based on the aforementioned gradient structure tensor, the dip angle of the formation; Based on the confidence information and the formation dip angle, an anisotropic Laplace filter is constructed; The anisotropic Laplace filter is used to process the denoised data to obtain optimized data; The size and shape of the filter window in the anisotropic Laplace filter are determined by the confidence information, and the window orientation in the anisotropic Laplace filter is determined by the formation dip angle; the confidence information includes confidence in linear structure features and confidence in lateral discontinuity structure features.

2. The method according to claim 1, characterized in that, The step of performing continuous wavelet transform on the flattened body data to obtain the frequency-spreading data includes: The flattened body data is decomposed by continuous wavelet transform to obtain decomposed data; the decomposed data includes seismic signals corresponding to multiple frequencies. Determine the frequency extension parameters; the frequency extension parameters include the frequency extension range and the reference frequency. Based on the overlay parameters and the seismic signal, the overlay data is reconstructed to obtain the overlay data.

3. The method according to claim 1, characterized in that, The reverse flattening and singular value decomposition denoising processes performed on the frequency extension data yield denoised data including: The topology data is reverse-flattened to obtain reverse-flattened data. The reverse-flattened data is subjected to singular value decomposition for denoising to obtain denoised data.

4. The method according to claim 3, characterized in that, The singular value decomposition denoising process performed on the flattened data to obtain denoised data includes: Generate a Hankel matrix that matches the reverse flattening data; Singular value decomposition is performed on the Hankel matrix to obtain one or more corresponding singular values ​​and singular value vectors; Determine the target singular value according to preset rules; Denoising data is generated using the target singular value and the singular value vector corresponding to the target singular value.

5. A post-stack seismic data processing device, characterized in that, include: The data acquisition module is used to acquire data to be processed. The data to be processed includes post-stack seismic data corresponding to at least one stratum; The target formation determination module is used to determine the target formation. The flattened body data generation module is used to perform layer flattening processing along the target stratum to obtain flattened body data; The frequency extension data generation module is used to perform continuous wavelet transform on the flattened body data to obtain frequency extension data; The denoised data generation module is used to perform anti-flattening processing and singular value decomposition denoising processing on the frequency extension data to obtain denoised data. The filtering module is used to perform anisotropic Laplacian filtering on the denoised data to obtain optimized data; The device further includes: The inversion module is used to perform inversion based on the optimized data to obtain inversion data; The comparison module is used to compare the inversion data with preset reference data to determine the compliance rate of the optimized data; The filtering module includes: The gradient structure tensor calculation submodule is used to calculate the gradient structure tensor based on the denoised data; The confidence information determination submodule is used to determine the seismic profile confidence information based on the gradient structure tensor. The formation dip angle determination submodule is used to determine the formation dip angle based on the gradient structure tensor. An anisotropic Laplace filter construction submodule is used to construct an anisotropic Laplace filter based on the confidence information and the formation dip angle. The filtering submodule is used to process the denoised data using the anisotropic Laplace filter to obtain optimized data; The size and shape of the filter window in the anisotropic Laplace filter are determined by the confidence information, and the window orientation in the anisotropic Laplace filter is determined by the formation dip angle; the confidence information includes confidence in linear structure features and confidence in lateral discontinuity structure features.

6. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the post-stack seismic data processing method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the post-stack seismic data processing method as described in any one of claims 1-4.

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