A mud shale barrier layer stripping system, method, device and medium

Abstract

The application discloses a mudstone shielding layer stripping system, method, equipment and medium, and the method is realized based on the system and comprises the following steps: firstly, mudstone strong reflection wavelets are identified and removed by using multi-wavelet decomposition; then, reconstructed seismic traces are subjected to IMF decomposition by using CEEMD; and strong energy of the shielding layer is subjected to flattening processing in a target time window constrained by well-seismic calibration; finally, the reconstructed seismic data volume after the shielding layer stripping is obtained, so that the weak response of the reservoir shielded by the mudstone is enhanced. The application can firstly weaken the dominant mudstone wavelet, and then finely process the strong energy of the residual shielding layer, so that a better weak response highlighting effect is obtained compared with a single method, and the problem that the reservoir response is shielded due to the mutual interference of the strong reflection of the mudstone boundary and the weak reflection of the adjacent reservoir is solved without significantly changing the main phase axis and the original seismic structural characteristics.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, specifically to a mudstone shielding layer stripping system, method, equipment, and storage medium based on CEEMD combined with multi-wavelet decomposition technology. Background Technology

[0002] Seismic reservoir prediction is a core technology in the field of oil and gas exploration and development. The seismic data used in seismic reservoir prediction is essentially an observation signal formed by superimposing noise from multiple sets of seismic wavelets and reflection coefficients.

[0003] In stratigraphic systems such as carbonate rocks, coal-bearing strata, and mudstone formations, lithological interfaces are prone to forming strong reflection and interference seismic responses, constituting strong shielding layers (strong reflection layers). These strong shielding layers usually have significant differences in velocity and density properties with adjacent reservoirs, resulting in high-intensity reflection coefficients at the lithological interfaces. This causes the seismic responses at the shielding layer interface and the reservoir interface to interfere with each other, masking the originally effective seismic responses of the reservoir. This manifests as weak peaks, weak responses, or even no obvious responses, greatly increasing the difficulty of identifying and predicting hidden reservoirs and restricting the improvement of reservoir exploration accuracy.

[0004] Currently, conventional reservoir prediction methods are mostly based on seismic attributes, time-frequency attributes, or reflection characteristics for analysis. Reservoir characterization capabilities are improved by optimizing the prediction process and extracting pre-stack or post-stack seismic attributes. For example, Chinese Patent Publication CN107643539A proposes a carbonate reservoir prediction method based on pre-stack seismic attributes. By combining pre-stack seismic attributes with the reservoir prediction process, the accuracy of carbonate reservoir characterization is improved.

[0005] However, these methods focus on the extraction and interpretation of reservoir properties and belong to "post-processing" analysis. They do not address the shielding interference caused by the strong reflection of adjacent mudstone on the wavelet level response, and do not establish a step-by-step signal processing mechanism of "first stripping the strong response of the shielding layer, and then enhancing the masked weak response". Therefore, they cannot fundamentally eliminate the masking effect of the strong shielding layer on the weak response of the reservoir.

[0006] To address the difficulty in identifying weak reservoir responses caused by strong shielding layers, existing technologies have developed various methods for suppressing strong reflections and processing non-stationary signals, mainly falling into two categories: One type is a strong reflection suppression technique based on the matching pursuit algorithm and multi-wavelet decomposition reconstruction. The matching pursuit algorithm removes strong reflection axes by using the horizon and wavelet information as joint constraints, while multi-wavelet decomposition removes strong amplitude wavelets by splitting the seismic signal into a set of wavelets with different dominant frequencies and phases, and then filtering and eliminating the strong amplitude wavelets to achieve the stripping of the shielding layer. Another type is adaptive processing techniques based on Empirical Mode Decomposition (EMD) and its improved algorithms, such as Complementary Set Empirical Mode Decomposition (CEEMD). By adding paired positive and negative white noise and averaging multiple sets, it suppresses mode aliasing and residual white noise errors, and is suitable for the decomposition and weak response extraction of nonlinear and non-stationary seismic signals.

[0007] In summary, while existing mainstream technical solutions can alleviate the interference problem of strong shielding layers to some extent, they still have many unavoidable technical defects: (1) Conventional reservoir prediction and attribute extraction methods (such as the scheme disclosed in CN107643539A) only focus on attribute interpretation and cannot directly eliminate the masking of the strong response of the mudstone shielding layer on the weak response of the adjacent reservoir, and cannot solve the shielding interference problem from the source of the signal. (2) When using multi-wavelet decomposition and reconstruction technology alone, although it can identify and remove the strong reflection wavelets of the shielding layer, it can only remove a single strong reflection wavelet and cannot completely remove the remaining energy of the shielding layer in different frequency bands and different signal components. Furthermore, it does not set up a component-level energy correction mechanism for the complex interference relationship between the shielding layer interface and the reservoir interface, and can only slightly enhance some weak reservoir responses, and cannot stably achieve the response highlighting effect required for accurate reservoir identification. (3) When using complementary ensemble empirical mode decomposition technology alone, although it can accurately locate signal energy anomalies and optimize the decomposition effect of non-stationary seismic signals, this technology lacks the pre-positional dominant shielding layer wavelet removal step and does not combine the strong reflection wavelet identification advantage of multi-wavelet decomposition. Relying solely on a single energy weakening method can only locally suppress the strong response of the shielding layer, and the overall enhancement effect on the weak response of the reservoir is limited, resulting in a poor highlighting effect on the reservoir response in the target area. (4) The functions of various single technical solutions are relatively isolated. The existing technology has never formed a closed-loop integrated shielding layer stripping system that integrates shielding layer wavelet removal, signal component screening, abnormal energy location, time window energy correction, and signal reconstruction output with well-seismic calibration as a constraint. It cannot completely and accurately eliminate the interference and suppression effect of strong shielding layer on weak reservoir response, and it is difficult to meet the engineering application requirements of high-precision reservoir prediction.

[0008] Therefore, this application proposes a mudstone shielding layer stripping system, method, equipment, and medium to solve the above-mentioned technical problem of reservoir response being obscured due to the mutual interference between strong reflections at the mudstone interface and weak reflections from adjacent reservoirs, without significantly altering the main phase axis and the original seismic structural characteristics. Summary of the Invention

[0009] The main objective of this invention is to provide a mudstone shielding layer stripping system, method, equipment, and medium to solve the technical problems mentioned in the background art.

[0010] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: On one hand, this invention discloses a mudstone shielding layer stripping system, which performs the following steps via computer equipment: First, multi-wavelet decomposition is used to identify and remove strong reflection wavelets from mudstone, reducing the dominant influence of strong reflections from the shielding layer on subsequent decomposition. Second, CEEMD is used to perform IMF component-level decomposition on the reconstructed seismic trace signal, allowing the residual shielding layer energy to be located at a finer scale. Finally, using the maximum energy method and well-seismic calibration window constraints, only the strong energy within the characteristic range of the shielding layer is flattened, preserving as much as possible the weak reservoir response and the seismic signal structure outside the target range. Finally, the seismic data volume after the shielding layer is removed is obtained through reconstruction, which enhances the weak response of the reservoir shielded by mudstone.

[0011] Preferably, the system includes: (1) Seismic data and well logging data input module, used to input raw seismic signal data, well-side seismic records, well logging curves, well seismic calibration results and target layer interpretation results; (2) Well-seismic calibration and shielding layer time window determination module, used to determine the time location, response period and target processing range of mudstone shielding layer based on the well logging data, synthetic records and seismic profiles input by the system; The time position is used to constrain the processing center position of the mudstone shield layer in the seismic trace or seismic profile, and serves as a benchmark for subsequent IMF energy anomaly location, maximum energy point correction, and processing range determination. The response period is used to characterize the dominant oscillation scale of the strong reflection response of the mudstone shield layer in the time domain, and is used to determine the width of the subsequent energy processing window. The target processing range is used to limit the scope of subsequent IMF component screening, maximum energy point search, energy statistics, and energy leveling, so that the processing is concentrated within the strong energy response time window of the mudstone shield layer, and reduces changes to weak reflections, thin-layer responses, and original structural information outside the target layer. (3) Multi-wavelet decomposition and mudstone wavelet removal module, used to decompose the original seismic signal input by the system into multiple wavelets, identify the target wavelet with strong mudstone reflection characteristics, remove the target wavelet before reconstruction, and finally perform signal reconstruction to output the seismic trace after multi-wavelet reconstruction; (4) CEEMD decomposition module, used to perform CEEMD decomposition on the seismic trace after multi-wavelet reconstruction to obtain multiple IMF components and residual quantities; (5) Maximum energy location and range calculation module, used to calculate the maximum energy point in the IMF component with mudstone shielding layer characteristics, and determine the target energy processing range in combination with the well-seismic calibration results input by the system; The IMF component containing mudstone shielding features is obtained by performing empirical mode decomposition, ensemble empirical mode decomposition, complete ensemble empirical mode decomposition, or variational mode decomposition on the seismic trace after multi-wavelet reconstruction. The IMF component is selected based on energy, correlation, dominant frequency, or time-frequency energy concentration within the mudstone shielding time window determined by well-seismic calibration. The IMF component is used as the object for subsequent maximum energy location and energy leveling processing. (6) IMF energy leveling processing module, used to calculate the ratio index between the target range energy of the IMF component to be processed and the average energy of the whole channel within the target range, and when the ratio index meets the preset energy processing conditions, to apply controlled gain suppression to the excessive amplitude or excessive energy within the target processing range, so as to weaken the strong reflection response of the mudstone shielding layer and retain the weak response and original structural features outside the target processing range. (7) Reconstruction module, used to perform energy flattening or gain suppression on the selected IMF components containing mudstone shielding features, and to combine them with the retained IMF components that were not selected as containing mudstone shielding features or that did not meet the energy flattening processing conditions and remained unchanged, as well as the low-frequency residual terms or trend terms obtained during the decomposition process, to perform superposition and reconstruction in the reverse process of decomposition, so as to obtain the seismic trace or seismic profile after weakening the strong response of the mudstone shielding layer. (8) Data output module after stripping: used to output the seismic data volume after stripping the mudstone shielding layer, well profile, root mean square amplitude attribute slice or data basis for subsequent inversion after reconstruction.

[0012] On the other hand, the present invention also discloses a mudstone shielding layer stripping method, which performs corresponding operations based on the above-mentioned mudstone shielding layer stripping system to obtain seismic data volume after shielding layer stripping, including: Step S1. Input the raw seismic trace data and perform well-seismic calibration by combining the target well logging curves, synthetic records, and seismic profiles; Step S2. Determine the time location and target processing window of the mudstone shielding layer based on the well-seismic calibration results. The target processing window should cover the strong reflection response caused by the top and bottom interfaces of the mudstone and maintain a spatial correspondence with the weak response of the adjacent reservoir. Step S3. Perform multi-wavelet decomposition on the original seismic signal to represent the seismic signal as a set of multiple wavelets; Step S4. Based on the well-seismic calibration results, wavelet center position, phase and amplitude energy, identify and remove the target wavelet with strong mudstone reflection from multiple wavelets. Finally, reconstruct the remaining wavelets to obtain the seismic signal after the initial weakening of strong mudstone reflection. Step S5. Perform CEEMD decomposition on the reconstructed seismic signal to obtain multiple IMF components and residuals; Step S6. Filter out IMF components containing strong amplitude or strong energy characteristics of the shielding layer from the CEEMD decomposition results; Step S7. Calculate the location of the maximum energy point for each of the selected IMF components. And the main frequency of the corresponding IMF is obtained through time-frequency analysis. Finally, based on the well seismic calibration results, the processing range was determined to be... ,in, It is a constant, determined based on the seismic response characteristic period of the mudstone shielding layer or the well-seismic calibration results; Step S8. Calculate the energy within the target range. With the average energy of the whole track and construct proportional indicators After that As an energy processing standard, excessive energy within the target range is flattened to weaken the strong energy of the shielding layer, while avoiding excessive alteration of the weak response and original structural characteristics outside the target range. Step S9. Perform maximum energy location, range calculation and energy flattening processing on multiple IMF components with shielding layer characteristics in a loop. Reconstruct the flattened IMF components, unprocessed IMF components and residual quantities to obtain the single-channel shielding layer stripping result. Step S10. Repeat the above process for multiple seismic data in the target area to form a seismic data volume after the mudstone shielding layer is removed, and further output well profiles or root mean square amplitude attribute slices.

[0013] Preferably, the target processing time window in step S2 is determined by the well-seismic calibration results, covering the time range of the strong reflection response of the mudstone shielding layer, and maintaining a spatial correspondence with the weak response of adjacent reservoirs.

[0014] Preferably, in step S3, each seismic signal can be represented as follows:

[0015] in, Represents the original seismic signal. Indicates the first individual wave components Indicates noise or residual components.

[0016] Preferably, the specific calculation formula for CEEMD decomposition of the seismic signal in step S5 is as follows:

[0017] in, This represents the seismic signal after reconstruction by multiple wavelets. Indicates the first Each intrinsic mode component Indicates the residual amount.

[0018] Preferably, the selection criteria in step S4 include the wavelet center position, dominant frequency, initial phase, amplitude energy, and their correspondence with the response of the mudstone top and bottom interface.

[0019] Preferably, the CEEMD decomposition parameters in step S5 include the number of times positive and negative white noise are added in pairs, the amplitude of white noise, the EMD decomposition stopping criterion, the number of IMF components, and the residual amount judgment criterion.

[0020] Preferably, the implementation steps of the CEEMD decomposition algorithm in step S5 are as follows: First, a series of white noise sequences are added to the original sequence signal. The signal is then decomposed by EMD to obtain a series of intrinsic mode components and remainders arranged from high to low frequency. Then, a series of white noise sequences, the opposite of those in the previous step, are added to the original sequence signal. This signal is then decomposed using EMD to obtain a series of intrinsic mode components and remainder terms arranged from high to low frequency, using the following formula:

[0021]

[0022] in, For the final j-th IMF component obtained from CEEMD decomposition, The j-th IMF component in the n groups obtained after EMD decomposition of the signal; This represents the remaining amount after CEEMD decomposition. This is the i-th residual in the n groups obtained after the signal is decomposed by EMD. Then, repeat the above two steps to obtain n pairs of intrinsic mode component functions and n pairs of residual functions.

[0023] Finally, the multiple intrinsic mode component functions (IMFs) from the previous step are combined and averaged to obtain the averaged IMF, which is the final IMF.

[0024] Preferably, the screening criteria in step S6 include the energy concentration of the IMF component near the target layer, the dominant frequency characteristics, the correspondence with the well seismic calibration window, and the degree of phase or amplitude matching with the strong response of the mudstone.

[0025] Preferably, in the process of single-channel shielding layer stripping results in step S9, a shielding layer stripping method based on CEEMD is adopted. Specifically, the signal is decomposed into multiple IMF components by CEEMD, and the location of the strong amplitude (energy) of the shielding layer is marked by the maximum energy method of the signal using the characteristic. Then, the IMF component containing the characteristic is selected and processed. Finally, the data after stripping the shielding layer is obtained by reconstruction.

[0026] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0027] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0028] As can be seen from the above technical solution, the present invention provides a mudstone shielding layer stripping system, method, equipment, and medium. Compared with the prior art, the present invention has the following advantages: 1. This invention combines multi-wavelet decomposition and CEEMD decomposition in series, leveraging the advantages of multi-wavelet decomposition in identifying and eliminating strong reflection wavelets from mudstone and the adaptive component decomposition advantages of CEEMD decomposition for non-stationary seismic signals. This enables the hierarchical and progressive processing of strong mudstone reflection interference and residual shielding energy, effectively compensating for the shortcomings of single algorithms and significantly enhancing the prominence of weak seismic responses in strata under strong shielding backgrounds.

[0029] 2. This invention accurately locks the time range of the shielding layer by relying on well-seismic calibration constraints, abandons the processing method of uniform attenuation of the entire seismic signal, and only performs local fine processing on the wavelet and IMF components containing shielding layer characteristics. This can accurately suppress the interference signal of the shielding layer, while protecting the original seismic data information to the greatest extent, thereby completely preserving the main in-phase axis structure of the seismic profile and the weak effective response of the strata, ensuring the authenticity and reliability of the seismic data.

[0030] 3. This invention identifies strong energy points in the shielding layer within the IMF component by employing the maximum energy method, and accurately defines the processing interval by combining the signal main frequency and response period. It can establish a set of shielding layer stripping calculation procedures with clear parameters and standardized steps, thereby realizing the quantitative and procedural processing of shielding layer interference, eliminating human experience errors, and improving the stability, repeatability, and engineering applicability of the algorithm.

[0031] 4. This invention employs a series strategy of first using multi-wavelet decomposition to remove the dominant strong reflection wavelet of mudstone, and then using CEEMD to finely process the residual shielding strong energy. This strategy effectively avoids residual interference from individual multi-wavelet decomposition, solves the technical defect of individual CEEMD decomposition being susceptible to strong reflection interference in the prior art, and significantly improves the processing quality and weak signal extraction accuracy of complex seismic signals in areas with strong shielding layers.

[0032] 5. This invention effectively enhances the inter-layer reflection amplitude intensity and lateral continuity of strata by jointly suppressing mudstone shielding interference and strengthening weak effective reflection of strata through a dual-method approach. It also optimizes the contiguous display effect of root mean square amplitude attributes, thereby effectively enhancing the weak response at the top of the target area group, making the stratigraphic boundaries clearer, and providing high-precision seismic data support for reservoir identification and fine geological interpretation.

[0033] It should be understood that the descriptions in this section are not intended to identify key or essential features of embodiments of the invention, nor are they intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Of course, implementing any product of the invention does not necessarily require achieving all of the advantages described above simultaneously. Attached Figure Description

[0034] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the data processing process of the system of the present invention; Figure 2 This is a flowchart illustrating the operation of the method of the present invention; Figure 3 This is a typical single-well composite columnar section of an embodiment of the present invention; Figure 4 This is a diagram showing the well seismic calibration results of an embodiment of the present invention; Figure 5 This is a schematic diagram of discrete synthesis record analysis according to an embodiment of the present invention; Figure 6 This is a schematic diagram of actual data multi-wavelet decomposition and reconstruction analysis in an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the practical application effect of the multi-wavelet decomposition and reconstruction technology according to an embodiment of the present invention. The upper figure is the original seismic well-connected profile, and the lower figure is the multi-wavelet reconstructed seismic well-connected profile. Figure 8 This is a schematic diagram illustrating the actual application effect of the shielding layer stripping method based on CEEMD according to an embodiment of the present invention. The upper figure is the original seismic well profile, and the lower figure is the processed seismic well profile. Figure 9This is a schematic diagram illustrating the actual application effect of the shielding layer stripping method based on multi-wavelet decomposition and reconstruction technology combined with CEEMD in an embodiment of the present invention. The upper figure is the original seismic well profile, and the lower figure is the processed seismic well profile. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] For details in the embodiments, please refer to Figures 1 to 9 .

[0037] On the one hand, such as Figure 1 As shown, an embodiment of the present invention discloses a mudstone shielding layer stripping system, comprising: Seismic data and well logging data input module: used to input raw seismic data, well-side seismic records, well logging curves, well-seismic calibration results, and target layer interpretation results.

[0038] Well-seismic calibration and shielding layer time window determination module: used to determine the time location, response period and target processing range of the mudstone shielding layer based on well logging data, synthetic records and seismic profiles.

[0039] Multi-wavelet decomposition and mudstone wavelet removal module: used to decompose the original seismic signal into multiple wavelets, identify target wavelets with strong mudstone reflection characteristics, and remove the target wavelet before reconstruction.

[0040] CEEMD decomposition module: used to perform CEEMD decomposition on seismic traces after multi-wavelet reconstruction to obtain multiple IMF components and residuals.

[0041] Maximum Energy Location and Range Calculation Module: Used to calculate the maximum energy point in the IMF component containing mudstone shielding layer characteristics, and determine the target energy processing range in combination with well-seismic calibration results.

[0042] IMF Energy Leveling Module: Used to calculate the ratio between the energy within the target range and the average energy of the entire track, and accordingly level or reduce the energy within the target range.

[0043] Reconstruction module: Used to combine and reconstruct the processed IMF components, the retained IMF components, the residuals, and the multi-wavelet reconstruction results.

[0044] Data output module after stripping: Used to output seismic data volume, well profile, root mean square amplitude attribute slices, or data base for subsequent inversion after stripping of mudstone shielding layer.

[0045] Based on the above system, the following operations are performed: First, multi-wavelet decomposition is used to identify and remove the dominant wavelet of strong mudstone reflection, reducing the dominant influence of strong reflection from the shielding layer on subsequent decomposition. Second, CEEMD is used to perform IMF component-level decomposition on the reconstructed signal, enabling the residual shielding layer energy to be located at a finer scale. Finally, through the maximum energy method and well-seismic calibration window constraints, only the strong energy within the characteristic range of the shielding layer is flattened, preserving as much as possible the weak reservoir response and the seismic signal structure outside the target range.

[0046] On the other hand, such as Figure 2 As shown in the figure, this invention also discloses a method for stripping mudstone shielding layers, comprising the following steps: Input the raw seismic trace data and perform well-seismic calibration by combining the target well logging curves, synthetic records, and seismic profiles.

[0047] The temporal location of the mudstone shielding layer and the target processing time window are determined based on the well-seismic calibration results. The target processing time window should cover the strong reflection response caused by the top and bottom interfaces of the mudstone and maintain a spatial correspondence with the weak response of adjacent reservoirs. The target processing time window is determined by the well-seismic calibration results and is used to cover the time range of the strong reflection response of the mudstone shielding layer.

[0048] The original seismic signal is decomposed into a multi-wavelet decomposition, representing the seismic signal as a set of multiple wavelets. For each seismic signal trace, it can be represented as:

[0049] in, Represents the original seismic signal. Indicates the first individual wave components Indicates noise or residual components.

[0050] Based on the well-seismic calibration results, wavelet center position, phase, and amplitude energy, a target wavelet with strong mudstone reflection is identified from multiple wavelets. After removing this target wavelet, the remaining wavelets are reconstructed to obtain a seismic signal with preliminary weakening of mudstone reflection. The target wavelet selection parameters include the wavelet center position, dominant frequency, initial phase, amplitude energy, and their correspondence with the response of the mudstone top and bottom interfaces.

[0051] The reconstructed seismic signal was decomposed using CEEMD to obtain multiple IMF components and residuals:

[0052] in, This represents the seismic signal after reconstruction by multiple wavelets. Indicates the first Each intrinsic mode component Indicates the residual amount.

[0053] The Complementary Ensemble Empirical Mode Decomposition (CEEMD) method is an improved algorithm based on EMD. This method adds contrasting white noise during the decomposition process to cancel out noise signals. In practice, it effectively reduces mode aliasing in the EMD algorithm and minimizes reconstruction errors caused by the incomplete cancellation of white noise added in the CEEMD algorithm, while also improving computational efficiency.

[0054] At this time, the CEEMD decomposition parameters include the number of times positive and negative white noise are added in pairs, the amplitude of white noise, the EMD decomposition stopping criterion, the number of IMF components, and the residual quantity judgment criterion.

[0055] The implementation steps of the CEEMD decomposition algorithm are as follows: First, a series of white noise sequences are added to the original sequence signal. The signal is then decomposed by EMD to obtain a series of intrinsic mode components and remainders arranged from high to low frequency. Then, a series of white noise sequences, the opposite of those in the previous step, are added to the original sequence signal. This signal is then decomposed using EMD to obtain a series of intrinsic mode components and remainder terms arranged from high to low frequency, using the following formula:

[0056]

[0057] in, For the final j-th IMF component obtained from CEEMD decomposition, The j-th IMF component in the n groups obtained after EMD decomposition of the signal; This represents the remaining amount after CEEMD decomposition. This is the i-th residual in the n groups obtained after the signal is decomposed by EMD. Then, repeat the above two steps to obtain n pairs of intrinsic mode component functions and n pairs of residual functions.

[0058] Finally, the multiple intrinsic mode component functions (IMFs) from the previous step are combined and averaged to obtain the averaged IMF, which is the final IMF.

[0059] IMF components containing strong amplitude or energy characteristics of the shielding layer were screened from the CEEMD decomposition results. The screening criteria included the energy concentration of the IMF component near the target layer, its dominant frequency characteristics, its correspondence with the well-seismic calibration window, and its phase or amplitude matching with the strong response of the mudstone.

[0060] Therefore, the IMF screening parameters include the IMF dominant frequency, the location of the maximum energy point, the degree of energy concentration near the target layer, and the overlap with the shielding layer time window.

[0061] The location of the maximum energy point is calculated for each of the selected IMF components. And the main frequency of the corresponding IMF is obtained through time-frequency analysis. The scope of treatment was determined based on the well seismic calibration results.

[0062] in, It is a constant and can be determined based on the seismic response characteristic period of the mudstone shielding layer or the well-seismic calibration results.

[0063] Calculate energy within the target range With the average energy of the whole track And construct proportional indicators:

[0064] Will As an energy processing standard, it is used to flatten excessive energy within the target range, thereby weakening the strong energy of the shielding layer, while avoiding excessive alteration of the weak response and original structural characteristics outside the target range.

[0065] In summary, the maximum energy location, range calculation and energy leveling processes are performed iteratively on multiple IMF components with shielding layer characteristics. The processed IMF components, unprocessed IMF components and residual quantities are then reconstructed to obtain the single-channel shielding layer stripping results.

[0066] At this point, the shielding layer stripping process employs a shielding layer stripping method based on CEEMD. Specifically, CEEMD decomposes the signal into multiple IMF components. Combining the characteristics of the strong amplitude (energy) of the shielding layer, the location of the strong amplitude of the shielding layer is marked using the maximum energy method. Then, the IMF component containing this characteristic is selected and processed. Finally, the data after the shielding layer is stripped is obtained through reconstruction. This method organically combines the advantages of CEEMD signal processing with the maximum energy method, which can weaken the strong shielding characteristic signal while also protecting the characteristics of the original weak signal. As a result, the weak response is highlighted, which is helpful for seismic interpretation.

[0067] Furthermore, the specific implementation process of the CEEMD shielding stripping method is as follows: Assume there is an original seismic signal. ; The first step is to process the original seismic signal. After CEEMD decomposition, the following can be obtained: ; in, This represents the multiple IMF components that have been decomposed. This represents the residual amount; The second step is to combine seismic data and well logging data to perform fine well-seismic calibration, thereby determining the time and location of the mudstone shielding layer, and selecting the components containing shielding layer characteristics from the decomposed IMF components. The third step involves calculating the location of the maximum energy point for each selected IMF component using the maximum energy method, denoted as [the location of the maximum energy point]. The dominant frequency of the IMF is obtained through time-frequency analysis, denoted as . ,make ; The fourth step is to combine the results of fine-scale well seismic calibration to determine the time range of mudstone shielding characteristics, denoted as: ; in, Since it is a constant, the seismic response characteristic period of the mudstone shielding layer can be determined from the well seismic calibration results, and thus the seismic response characteristic period can be determined. ; The fifth step is to calculate the energy within the range of the mudstone shielding layer determined in the fourth step, and at the same time calculate the average energy of the channel. ; in, and These represent the energy within the target range and the average energy of the entire path, respectively. Finally and Divide, that is: ; Received The index is used as a standard indicator to flatten the energy values ​​at each point within the target range of the mudstone shielding layer. Finally, other relevant IMF components are processed in the same cyclical manner as described above. The processed IMF components are then reconstructed with other retained IMF components, thus completing one mudstone shielding layer feature stripping step. Performing the above processing on each seismic trace containing mudstone shielding features achieves the purpose of mudstone shielding layer stripping. Here, the strong energy point of the shielding layer in the IMF component is determined by the maximum energy method, and the processing range is determined by combining the dominant frequency and response period, which enables the shielding layer stripping to have a clear and feasible calculation path.

[0068] Furthermore, it should be emphasized that this method does not uniformly attenuate the entire seismic signal, but rather locates the shielding layer time range under the constraints of well-seismic calibration, and only performs local processing on the wavelet or IMF component containing shielding layer characteristics, which can better preserve the weak response and main in-phase axis structure in the original seismic signal.

[0069] The above process is repeated on multiple seismic data within the target area to form a seismic data volume after the mudstone shielding layer is removed, and further output well profiles or root mean square amplitude attribute slices.

[0070] In summary, this method combines multi-wavelet decomposition and CEEMD decomposition in series. It leverages the direct identification capability of multi-wavelet decomposition for strong mudstone reflection wavelets and the adaptive component decomposition capability of CEEMD for non-stationary seismic signals. It also combines the advantages of multi-wavelet decomposition in identifying and eliminating strong mudstone reflection wavelets with the advantages of CEEMD in adaptive component decomposition for non-stationary seismic signals. This enables hierarchical and progressive processing of strong mudstone reflection interference and residual shielding energy, effectively compensating for the shortcomings of single algorithms and significantly enhancing the prominence of weak seismic responses in strata under strong shielding backgrounds.

[0071] Furthermore, compared with existing single multi-wavelet decomposition methods, this method can further process the strong energy of the shielding layer remaining in the IMF components; compared with existing single CEEMD methods, this method first removes the dominant wavelet of strong mudstone reflection, reducing the interference of strong reflection on subsequent CEEMD component processing.

[0072] In one example, for zone shielding layer identification: In a specific embodiment, taking a certain exploration area as an example, after processing it using the above steps, the result is as follows: Figure 3-5 As shown, a lithological interface exists between the two groups of areas (bottom of Group A and top of Group B). Single-well logging curves indicate that this mudstone exhibits high natural gamma, low velocity, and low density characteristics. The overlying top of Group B is interpreted as a gas layer, with a lithology of bioclastic limestone, exhibiting relatively high velocity, high density, and well-developed porosity. Well-seismic calibration results indicate that the combined effect of strong negative and strong positive reflection coefficients at the mudstone interface causes the actual seismic data to show a weak peak response, masking the reservoir response.

[0073] Specifically Figure 3 The stratigraphic sequence at depths of 6890m to 6920m is vertically marked, which can clearly distinguish the target Plw mudstone shield layer from the upper and lower reservoir strata, and intuitively present the typical logging response characteristics of the mudstone section with high sonic, high density, and high gamma. The depth calibration clarifies the vertical distribution range of the top and bottom interfaces of the mudstone shield layer, providing logging geological basis for subsequent well-seismic calibration and shield layer time window selection, and realizing quantitative constraint of the spatial boundary of the underground mudstone shield layer.

[0074] And similarly, Figure 4 It includes TP2ch1 and TP2ch2 formation reflection wavelets, well-side synthetic seismic records, reflection coefficient sequences, wave impedance curves, and actual well-side seismic traces. The reflection coefficients are constructed using well logging wave impedance data, and synthetic records are generated by combining standard wavelet convolution in the work area. The phase axis of the synthetic records is matched layer by layer with the original well-side seismic traces, which can accurately calibrate the seismic time axis corresponding to the corresponding strata interface and lock the seismic time window interval corresponding to the mudstone shielding layer. Based on the verification of well-seismic matching accuracy, the drawbacks of uniform attenuation processing across the entire trace are avoided.

[0075] In addition, Figure 5 The data also includes four key stratigraphic boundaries: mudstone top boundary, mudstone bottom boundary, reservoir top boundary, and reservoir bottom boundary. These boundaries are used to overlay single-channel seismic waveforms from the well. It's important to note that in the original waveforms, the mudstone top and bottom boundaries form strong amplitude reflection phase axes, completely obscuring the weak stratigraphic response of the specified reservoir group. The reflection signals from the reservoir top and bottom boundaries are submerged by the mudstone shielding energy, making it impossible to directly identify the reservoir boundaries. By decomposing the discrete waveforms from the synthetic records, it becomes clear that the strong reflection wavelet from the mudstone is the core interference source causing the weak reservoir response shielding. Discrete synthetic record analysis revealed an energy difference and phase reversal between the mudstone top and bottom boundary wavelets and the reservoir top and bottom boundary wavelets. The mudstone wavelets and reservoir wavelets interfered and superimposed, forming a mudstone shielding layer, thus making the reservoir difficult to identify.

[0076] In one example, the effect of a single method processing: In a specific embodiment, multi-wavelet decomposition was performed on the seismic records from the Yb29 well bypass. After processing using the steps described above, the result is as follows: Figure 6-8 As shown, 26 wavelet channels were obtained through decomposition, with the strong amplitude of the mudstone mainly concentrated on wavelet signal 4. After removing this wavelet and reconstructing, the error between the reconstructed signal and the original signal was small, and the reservoir response was enhanced to some extent. After applying the multi-wavelet reconstruction method to the well profile, the main phase axes did not change significantly before and after processing, some weak signal responses were enhanced, and the phase axes became clearer and more continuous.

[0077] Figure 6 The data includes the original single-channel seismic record, wavelet components of each order obtained through multi-wavelet decomposition, the characteristic wavelet of strong reflection from mudstone, and the reconstructed waveform after removing the mudstone wavelet. In the original record, the energy amplitude of the mudstone wavelet is significantly higher than that of the reservoir's weak reflection wavelet. After separating wavelet components of different energy and frequency through multi-wavelet decomposition, the dominant strong reflection wavelet of mudstone can be accurately identified and removed individually. The reconstructed waveform significantly suppresses the main shielding energy of mudstone, but a small amount of diffuse residual shielding energy remains, and the weak reservoir response is only slightly highlighted. Therefore... Figure 6 This clearly demonstrates that relying solely on multi-wavelet decomposition can only remove the main strong reflections from mudstone; the residual shielding energy will still obscure stratigraphic details, necessitating secondary fine processing in conjunction with CEEMD adaptive decomposition.

[0078] For longitudinal depths of 2980m to 3110m Figure 7 Because the mudstone shielding layer in the conventional profile has a continuous strong amplitude in-phase axis, the reservoir reflection at the top of the formation in the test area is weak and laterally discontinuous, the stratigraphic boundaries are blurred, and the information between the layers is completely suppressed by strong shielding. Therefore, in this area... Figure 7 In this invention, only the reconstructed profile after removing mudstone wavelets by multi-wavelet decomposition is used. The amplitude of the main strong reflection of mudstone is significantly reduced, and the weak response of the reservoir is improved to a certain extent. However, there is still diffuse residual high energy around the shielding layer, and the thin layer and stratigraphic interface are not clearly characterized, so the improvement of the lateral continuity of the reservoir is limited.

[0079] against Figure 8 For the original seismic profile of well Yb_7-Vb_29, the strong reflection energy of the mudstone covers the entire target time window, making it difficult to distinguish stratigraphic layers and reservoir boundaries; the figure below shows the profile after only CEEMD decomposition and maximum energy method processing of IMF components. According to Figure 8 Image CEEMD adaptive decomposition can separate multi-scale components of non-stationary seismic signals and flatten the strong energy range of the shielding layer. However, the extremely strong reflection of mudstone in the original earthquake will interfere with the screening of IMF components, and amplitude distortion will occur around the shielding layer. The effective layer phase axis will be partially distorted, and the local original stratigraphic information will be destroyed, resulting in poor data fidelity.

[0080] When using the CEEMD shielding stripping method alone, the IMF components are separated by CEEMD, and the high-energy locations within the IMF components are located using the maximum energy method before flattening. Practical application results show that some weak responses appear on the processed profile, but the reservoir response near the target well is not sufficiently prominent, indicating that the effect of CEEMD alone is limited.

[0081] In one example, the effect of handling union methods: In a specific embodiment, when using the combined method mentioned in the above steps, the original data is first decomposed and reconstructed using multiple wavelet decomposition, and mudstone wavelets are removed from the decomposed wavelets. Then, the CEEMD shielding layer stripping method is used to determine the processing time window under well-seismic constraints, and the strong energy points in the IMF components are processed. In the processed well-connected profile, the interlayer reflection amplitude is significantly enhanced and laterally continuous, and the weak response at the top of the experimental area group is enhanced, achieving the purpose of mudstone shielding layer stripping and highlighting reservoir reflection characteristics.

[0082] Further analysis was conducted by extracting and comparing three segments of the root mean square amplitude attribute of the target experimental region before and after joint processing. The results are as follows: Figure 9 As shown, in Figure 9In the original well-connected seismic profiles, the processed attribute slices exhibit enhanced local energy, creating a continuous effect and clearer boundary delineation. This result demonstrates that the present invention can highlight weak responses that were originally shielded by mudstone, providing higher-quality seismic data for subsequent reservoir prediction and seismic inversion.

[0083] At this point, due to the continuous distribution of strong amplitude mudstone shielding layers, the weak reflection at the top of the test group is almost invisible, the lateral continuity of the strata is poor, and interlayer reflection information is missing; therefore, combined with the attached... Figure 9 The comparison shows that: the strong reflection of the mudstone shielding layer is precisely suppressed and the residual shielding energy is completely eliminated; the weak reflection of the top strata in the target experimental area is significantly enhanced, the inter-stratum reflection amplitude is uniform and the lateral continuity is greatly improved; the main strata phase axis of the original profile is completely preserved, without amplitude distortion or stratum structure distortion, and the stratum boundaries are clearly delineated, which can be directly used for reservoir identification and fine geological interpretation.

[0084] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0085] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0086] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform any of the mudstone shielding layer stripping methods described above.

[0087] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0088] This application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus. Memory, used to store computer programs; The processor, when executing the program stored in the memory, implements the above-mentioned mudstone shielding layer stripping method.

[0089] The communication bus mentioned in the above-mentioned electronic devices can be a standard bus for interconnecting peripheral components or an extended industrial standard structure bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0090] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0091] The memory may include random access memory or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0092] The processors mentioned above can be general-purpose processors, including central processing units, network processors, etc.; they can also be digital signal processors, application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0093] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0095] Furthermore, it should be noted that if any directional indication (such as up, down, left, right, front, back, etc.) is involved in the embodiments of the present invention, the directional indication is only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0096] Furthermore, those skilled in the art should understand that in the actual use of the embodiments of this application, there may be preset thresholds used as the basis for judging the corresponding technical solutions. These thresholds are conventional technical means commonly used in the field to implement functions such as state judgment, condition recognition, and control logic switching. The specific values, setting basis, value selection methods, determination methods, and adjustment rules of the thresholds involved in this technical solution are all conventional technical choices that can be reasonably determined by those skilled in the art based on conventional technical factors such as actual application scenarios, system working states, characteristics of the detection object, hardware performance parameters, and functional requirements, through conventional experiments, calibrations, and debugging. The specific setting and adjustment of the aforementioned thresholds will not cause this technical solution to be unimplementable as a whole, nor will it affect the realization of the core concept and the achievement of the technical effects of this technical solution.

[0097] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, in the embodiments of this invention, "multiple" refers to two or more. Moreover, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

Claims

1. A mudstone shielding layer stripping system, characterized in that, include: The multi-wavelet decomposition and mudstone wavelet removal module is used to decompose the original seismic signal input by the system into multiple wavelets, identify and remove target wavelets with strong mudstone reflection characteristics, and finally perform signal reconstruction to output the reconstructed seismic trace. The CEEMD decomposition module is used to perform CEEMD decomposition on the seismic traces after multi-wavelet reconstruction to obtain multiple IMF components and residuals. The maximum energy location and range calculation module is used to calculate the maximum energy point in a specified IMF component and determine the target energy processing range in combination with the well-seismic calibration results input by the system. The IMF energy leveling module is used to calculate the ratio between the energy within the target range and the average energy of the entire track, and to level or reduce the energy within the target range accordingly. The reconstruction module is used to combine and reconstruct the IMF components, residuals, and multi-wavelet reconstruction results before and after flattening or weakening processing. The stripped data volume output module is used to reconstruct and output the seismic data volume after the mudstone shielding layer has been stripped.

2. A method for removing a mudstone shielding layer, comprising performing corresponding operations based on the mudstone shielding layer removal system described in claim 1 to obtain a seismic data volume after the shielding layer has been removed, characterized in that, include: Step S1. Input the raw seismic trace data and perform well-seismic calibration by combining the target well logging curves, synthetic records, and seismic profiles; Step S2. Determine the time location of the mudstone shielding layer and the target processing time window based on the wellbore calibration results; Step S3. Perform multi-wavelet decomposition on the original seismic signal to represent the seismic signal as a set of multiple wavelets; Step S4. Based on the well-seismic calibration results, wavelet center position, phase and amplitude energy, identify and remove the target wavelet with strong mudstone reflection from multiple wavelets. Finally, reconstruct the remaining wavelets to obtain the seismic signal after the initial weakening of strong mudstone reflection. Step S5. Perform CEEMD decomposition on the reconstructed seismic signal to obtain multiple IMF components and residuals; Step S6. Filter out IMF components containing strong amplitude or strong energy characteristics of the shielding layer from the CEEMD decomposition results; Step S7. Calculate the location of the maximum energy point for each of the selected IMF components. And the main frequency of the corresponding IMF is obtained through time-frequency analysis. Finally, based on the well seismic calibration results, the processing range was determined to be... ,in, It is a constant, determined based on the seismic response characteristic period of the mudstone shielding layer or the well-seismic calibration results; Step S8. Calculate the energy within the target range. With the average energy of the whole track and construct proportional indicators After that As an energy processing standard, excessive energy within the target range is leveled out; Step S9. Perform maximum energy location, range calculation and energy flattening processing on multiple IMF components with shielding layer characteristics in a loop. Reconstruct the processed IMF components, unprocessed IMF components and residual quantities to obtain the single-channel shielding layer stripping result. Step S10. Repeat the above process for multiple seismic data in the target area to form a seismic data volume after the mudstone shielding layer is removed, and further output well profiles or root mean square amplitude attribute slices.

3. The mudstone shielding layer stripping method as described in claim 2, characterized in that, In step S2, the target processing time window is determined by the well-seismic calibration results, covering the time range of the strong reflection response of the mudstone shielding layer, and maintaining a spatial correspondence with the weak response of the adjacent reservoir.

4. The mudstone shielding layer stripping method as described in claim 2, characterized in that, The selection criteria in step S4 include the wavelet center position, dominant frequency, initial phase, amplitude energy, and their correspondence with the response of the mudstone top and bottom interface.

5. The mudstone shielding layer stripping method as described in claim 2, characterized in that, The CEEMD decomposition parameters in step S5 include the number of times positive and negative white noise are added in pairs, the amplitude of white noise, the EMD decomposition stopping criterion, the number of IMF components, and the residual quantity judgment criterion.

6. The mudstone shielding layer stripping method as described in claim 2, characterized in that, The screening criteria in step S6 include the energy concentration of the IMF component near the target layer, the dominant frequency characteristics, the correspondence with the well-seismic calibration window, and the degree of phase or amplitude matching with the strong response of mudstone.

7. A computer-readable storage medium, characterized in that, The system stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 6.

8. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 6.

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