A micro-component analysis-based method and device for extracting weak signals from seismic data

Abstract

The application provides a weak signal extraction method and device of seismic data based on micro-component analysis, and belongs to the field of oil and gas seismic exploration. The method comprises the following steps: obtaining well-seismic data and geological information of a study area; pre-processing the obtained well-seismic data; calibrating horizons by comprehensively utilizing the well-seismic data and the geological information; adaptively decomposing seismic data of a target layer section; selecting appropriate intrinsic mode function components; respectively performing singular value decomposition on the selected components; selecting appropriate singular values for each component; reconstructing component signals according to the selected singular values; reconstructing seismic signals according to the obtained component signals; and repeatedly performing the above steps by using the micro-component analysis method, so as to obtain weak seismic data signals reflecting the slight changes of the geological medium properties. The application can extract the weak seismic response characteristics of the reservoir from the complex seismic wave field change characteristics, and provide a reliable basis for developing a reservoir gas-bearing property detection method with higher resolution.

Description

Technical Field

[0001] This invention relates to the field of oil and gas seismic exploration technology, specifically a method and apparatus for extracting weak signals from seismic data based on micro-component analysis. Background Technology

[0002] In recent years, with the continuous deepening of oil and gas exploration and development, the exploration targets have gradually shifted from conventional oil and gas reservoirs to complex unconventional oil and gas reservoirs. Some unconventional oil and gas reservoirs are buried deep and small in scale. During seismic oil and gas exploration data acquisition, the seismic waves excited at the surface are absorbed and attenuated by the intermediate strata. After reaching the target layer, they are reflected and undergo another absorption and attenuation process. This results in the weak energy of the reflected signal from the target layer received by the surface geophone and makes it susceptible to interference from other noise.

[0003] The seismic response of pore fluids in deeply buried reservoirs is weak, and if it can be observed, it can only be manifested as micro-events in the seismic record, reflected in the weak signal of seismic data. However, in actual exploration, although the weak signal of seismic data containing the fine structure and minute property changes of the geological medium objectively exists in the received seismic wave field signal, it is easily masked by the reflected signal of strong reflective layers or removed along with noise.

[0004] If we could find a way to extract weak signals from seismic data that contain information about the fine structure and subtle changes in the properties of geological media, we could provide a reliable basis for studying minute variations in the properties of geological media, such as changes in the properties and saturation of rock pore fluids. However, currently, there are very few methods for extracting weak signals from deep seismic data. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention aims to provide a method and apparatus for weak signal extraction from seismic data based on micro-component analysis. This invention, based on micro-component analysis and integrating the advantages of adaptive mode decomposition and singular value decomposition, can extract weak seismic response features reflecting subtle changes in geological medium properties from complex seismic wavefield variations while suppressing some seismic data noise. This allows for the analysis and acquisition of detailed structural and subtle differences in medium properties within the target layer, thereby improving the accuracy and reliability of reservoir gas content detection.

[0006] To achieve the above technical objectives, the present invention provides the following technical solutions.

[0007] In a first aspect, embodiments of this disclosure provide a method for extracting weak signals from seismic data based on micro-component analysis, the method comprising:

[0008] (1) Obtain well logging data, seismic data and geological information for the study area;

[0009] (2) Preprocess the acquired well logging data and seismic data;

[0010] (3) Based on the preprocessed well logging data and seismic data obtained, comprehensive geological information is used to perform seismic geological comprehensive stratigraphic calibration to accurately calibrate the stratigraphic position;

[0011] (4) Based on the calibrated layer, perform adaptive mode decomposition on the seismic data of the target layer to obtain a series of intrinsic mode function components;

[0012] (5) Based on the intrinsic mode function components obtained through adaptive mode decomposition, select appropriate intrinsic mode function components by combining well logging and geological information;

[0013] (6) Perform singular value decomposition on each of the selected intrinsic mode function components;

[0014] (7) Based on the singular values ​​of each intrinsic mode function component obtained by singular value decomposition, select appropriate singular values ​​for each intrinsic mode function component by combining well logging and geological information;

[0015] (8) Reconstruct each intrinsic mode function component signal according to the selected singular values ​​to obtain each intrinsic mode function component signal reconstructed by singular value decomposition;

[0016] (9) Reconstruct the seismic signal based on the obtained reconstructed intrinsic mode function component signals;

[0017] (10) Based on the micro-component analysis method, repeat steps (4) to (9) as needed to obtain weak seismic data signals that reflect the slight changes in the properties of the geological medium.

[0018] Preferably, the preprocessing of the acquired seismic data and well logging data in step (2) includes:

[0019] Seismic data undergoes trace editing, amplitude compensation, and pre-stack migration; well logging data undergoes outlier removal, correction, and lateral standardization.

[0020] Preferably, the core work of the comprehensive geological information for seismic geological stratigraphic calibration in step (3) is to use well logging data and seismic wavelet simulation of well-side seismic records to comprehensively calibrate and map well logging stratigraphic levels to seismic stratigraphic levels, including:

[0021] Seismic wavelets are extracted from seismic data; reflection coefficients are calculated using well logging data and convolved with the seismic wavelets to obtain a synthetic seismic record; and the correspondence between seismic reflections and geological strata is found by integrating geological information to accurately pinpoint seismic strata.

[0022] Preferably, the adaptive mode decomposition method described in step (4) may include empirical mode decomposition methods (such as set empirical mode decomposition, complementary set empirical mode decomposition) and variational mode decomposition methods. Each type of method has its own advantages and should be selected according to actual needs.

[0023] The Empirical Mode Decomposition (EMD) method, proposed by Huang E et al. in 1998, is a non-stationary signal processing method highly suitable for processing nonlinear and non-stationary time series. It adaptively decomposes a signal into a superposition of components called intrinsic mode functions (EMFs). However, EMD is prone to mode aliasing in its decomposition results. To address this issue, ensemble EMD has been proposed. Ensemble EMD mitigates the mode aliasing problem to some extent by introducing auxiliary noise. Complementary ensemble EMD replaces the direct introduction of Gaussian white noise into the original signal with the simultaneous introduction of a pair of Gaussian white noises with opposite values, allowing the introduced Gaussian white noise to cancel each other out during the decomposition process.

[0024] The variational mode decomposition (VMD) method, proposed by Dragomiretskiy and Zosso in 2014, is an adaptive and completely non-recursive decomposition method. As one of the most widely used signal decomposition methods currently available, VMD differs significantly from empirical mode decomposition (EMD). EMD methods decompose signals recursively, while VMD constructs a variational model of the signal to find the optimal solution non-recursively. This method can pre-determine the number of mode decompositions based on the actual signal characteristics, and then adaptively calculate the optimal center frequency and finite bandwidth of each mode, achieving effective separation of intrinsic mode functions. Essentially, VMD is the process of finding the optimal solution to a variational problem, possessing a more solid mathematical foundation than Empirical Mode Decomposition (EMD).

[0025] Preferably, in the process of selecting appropriate intrinsic mode function components based on the integrated well logging and geological information in step (5), some higher-order intrinsic mode function components can be discarded as needed to eliminate noise in the seismic data.

[0026] Preferably, the singular value decomposition method described in step (6) can be a common singular value decomposition method or a higher-order singular value decomposition method, which should be selected according to actual needs.

[0027] The singular value decomposition (SVD) method, first proposed by the Italian mathematician Beltrami in 1873, is an effective mathematical method for extracting data features. After more than a century of refinement and development, SVD has a sound theoretical foundation and practical algorithmic schemes, finding important applications in fields such as bioinformatics, statistics, and signal processing. Higher-order SVD is an effective tensor decomposition method, which can be seen as a generalization of two-dimensional matrix SVD. It is completely data-driven, requiring no user-defined parameters, and can handle tensors of arbitrary order.

[0028] Preferably, in the process of selecting appropriate singular values ​​for each intrinsic mode function component based on the integrated well logging and geological information in step (7), some low-order singular values ​​can be discarded as needed to remove some residual noise in the seismic data and better highlight the weak seismic response characteristics of the reservoir.

[0029] Preferably, step (10) involves repeating steps (4) to (9) based on the micro-component analysis method according to actual needs to obtain weak seismic data signals reflecting minor changes in the properties of the geological medium. Specifically, during the repetition of steps (4) to (9), the results of adaptive mode decomposition and singular value decomposition are processed based on the idea of ​​discarding large components and keeping small components in micro-component analysis. This process discards some of the main component signals reflecting the properties of the geological medium and then reconstructs and obtains weak seismic data signals reflecting minor changes in the properties of the geological medium.

[0030] As one specific implementation of this disclosure,

[0031] Secondly, embodiments of this disclosure provide a seismic data weak signal extraction device based on micro-component analysis, characterized in that the device may include:

[0032] The data acquisition module is used to acquire well logging data, seismic data, and geological information for the study area;

[0033] The data processing module preprocesses the acquired well logging data and seismic data;

[0034] The stratigraphic calibration module is used to perform seismic geological stratigraphic calibration based on the preprocessed well logging data and seismic data obtained, and by integrating geological information.

[0035] The adaptive mode decomposition module is used to perform adaptive mode decomposition on the seismic data of the target segment according to the calibrated layer to obtain a series of intrinsic mode function components.

[0036] The intrinsic mode function component selection module is used to select appropriate intrinsic mode function components based on the intrinsic mode function components obtained through adaptive mode decomposition, and by integrating well logging and geological information.

[0037] The singular value decomposition module is used to perform singular value decomposition on each of the selected intrinsic mode function components.

[0038] The singular value selection module is used to select appropriate singular values ​​for each intrinsic mode function component based on the singular values ​​of each intrinsic mode function component obtained through singular value decomposition, and by combining well logging and geological information.

[0039] The component signal reconstruction module is used to reconstruct each intrinsic mode function component signal according to the selected singular values ​​to obtain each intrinsic mode function component signal reconstructed by singular value decomposition.

[0040] The seismic signal reconstruction module is used to reconstruct seismic signals based on the obtained reconstructed intrinsic mode function component signals.

[0041] The weak signal extraction module for seismic data is used to obtain weak seismic signals that reflect subtle changes in the properties of the geological medium based on the reconstructed seismic signals.

[0042] The beneficial effects of this invention are as follows: This invention breaks through the traditional research approach and introduces micro-component analysis into seismic data processing for the first time. It integrates the advantages of adaptive mode decomposition and singular value decomposition methods, and achieves the extraction of weak seismic signals reflecting subtle changes in geological medium properties from the complex seismic wavefield variation characteristics while suppressing some seismic data noise. This allows for the analysis and acquisition of fine structure and subtle differences in medium properties of the target layer, thereby improving the accuracy and reliability of reservoir gas content detection. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments in this specification, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. In the drawings:

[0044] Figure 1 A flowchart illustrating a method for extracting weak signals from seismic data based on micro-component analysis, provided in an embodiment of the present invention;

[0045] Figure 2 This is a seismic amplitude image of a target layer in an exploration area provided in an embodiment of the present invention;

[0046] Figure 3 This is a seismic amplitude image of a weak seismic signal extracted from a target layer in an exploration area, provided in an embodiment of the present invention.

[0047] Figure 4 This is a schematic diagram of a seismic data weak signal extraction device based on micro-component analysis, provided as an embodiment of the present invention. Detailed Implementation

[0048] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the specification, and not all embodiments.

[0049] This invention provides a method for extracting weak signals from seismic data based on micro-component analysis. (Reference) Figure 1 As shown, in some embodiments, the seismic data weak signal extraction method based on micro-component analysis may include the following steps:

[0050] Step 101: Obtain well logging data, seismic data, and geological information for the study area;

[0051] Step 102: Preprocess the acquired well logging data and seismic data;

[0052] Step 103: Based on the preprocessed well logging data and seismic data obtained, perform comprehensive seismic geological stratigraphic calibration by integrating geological information to accurately determine the stratigraphic position;

[0053] Step 104: Based on the calibrated stratigraphic level, perform adaptive mode decomposition on the seismic data of the target stratigraphic segment to obtain a series of intrinsic mode function components;

[0054] Step 105: Based on the intrinsic mode function components obtained through adaptive mode decomposition, select appropriate intrinsic mode function components by combining well logging and geological information;

[0055] Step 106: Perform singular value decomposition on each of the selected intrinsic mode function components;

[0056] Step 107: Based on the singular values ​​of each intrinsic mode function component obtained through singular value decomposition, select appropriate singular values ​​for each intrinsic mode function component by combining well logging and geological information.

[0057] Step 108: Reconstruct each intrinsic mode function component signal according to the selected singular values ​​to obtain each intrinsic mode function component signal reconstructed by singular value decomposition;

[0058] Step 109: Reconstruct the seismic signal based on the obtained reconstructed intrinsic mode function component signals;

[0059] Step 110: Based on the micro-component analysis method, repeat steps (4) to (9) as needed to obtain weak seismic data signals that reflect the slight changes in the properties of the geological medium.

[0060] In one example, the preprocessing of seismic and well logging data includes:

[0061] Seismic data undergoes trace editing, amplitude compensation, and pre-stack migration; well logging data undergoes outlier removal, correction, and lateral standardization.

[0062] In one example, the core work of the integrated geological information for seismic geological horizon calibration is to use well logging data and seismic wavelet simulation of well-side seismic records to integrate geological information to calibrate and map well logging horizons to seismic horizons, including:

[0063] Seismic wavelets are extracted from seismic data; reflection coefficients are calculated using well logging data and convolved with the seismic wavelets to obtain a synthetic seismic record; and the correspondence between seismic reflections and geological strata is found by integrating geological information to accurately pinpoint the strata.

[0064] In one example, the adaptive mode decomposition method may include empirical mode decomposition methods (such as set empirical mode decomposition, complementary set empirical mode decomposition) and variational mode decomposition methods. Each type of method has its own advantages and should be selected according to actual needs.

[0065] In one example, during the process of selecting appropriate intrinsic mode function components by integrating well logging and geological information, some higher-order intrinsic mode function components can be discarded as needed to eliminate noise in the seismic data.

[0066] In one example, the singular value decomposition method can be a conventional singular value decomposition method or a higher-order singular value decomposition method, which should be selected according to actual needs.

[0067] In one example, during the process of selecting appropriate singular values ​​for each intrinsic mode function component based on integrated well logging and geological information, some low-order singular value components can be discarded as needed to remove some residual noise in the seismic data and better highlight the weak seismic response characteristics of the reservoir.

[0068] In one example, the micro-component analysis-based method repeats steps 104 to 109 as needed to obtain weak seismic signals reflecting minute changes in the properties of the geological medium, specifically:

[0069] During the repetition of steps 104 to 109, based on the idea of ​​discarding large components and retaining small components in micro-component analysis, the results of adaptive mode decomposition and singular value decomposition are processed by discarding the main component signals that reflect the properties of the geological medium, thereby reconstructing weak seismic data signals that reflect the slight changes in the properties of the geological medium.

[0070] In summary, the method provided by this invention involves: acquiring well logging data, seismic data, and geological information of the study area; preprocessing the acquired well logging data and seismic data; comprehensively utilizing the well logging data, seismic data, and geological information for stratigraphic calibration; performing adaptive mode decomposition on the seismic data of the target stratigraphic segment; selecting appropriate intrinsic mode function components; performing singular value decomposition on each selected component; selecting appropriate singular values ​​for each component; reconstructing the component signals based on the selected singular values; reconstructing the seismic signals based on the obtained component signals; and using the principle of discarding large components and retaining small components, discarding some of the main component signals reflecting the geological medium properties during the repetition of the above steps, thereby reconstructing weak seismic signals reflecting minor changes in the geological medium properties.

[0071] The method of this invention was used to extract weak signals from seismic data of a certain exploration area based on micro-component analysis. The comparison results are as follows: Figure 2 and Figure 3 As shown, where, Figure 2 Seismic amplitude images of the target layer in the exploration area. Figure 3 This image shows the seismic amplitude of a weak seismic signal extracted using a fusion of empirical mode decomposition and higher-order singular value decomposition. As can be seen from the image, this method can extract useful weak seismic signals reflecting subtle changes in geological medium properties from the complex and varied characteristics of the seismic wavefield. This allows for the analysis and acquisition of detailed information on the fine structure and subtle differences in medium properties of the target layer, improving the accuracy and reliability of reservoir gas-bearing detection.

[0072] Corresponding to the aforementioned method for extracting weak signals from seismic data based on differential component analysis, this invention provides a device for extracting weak signals from seismic data based on differential component analysis. (Reference) Figure 4 As shown, in some embodiments, the device may include: a data acquisition module 201, a data processing module 202, a layer calibration module 203, an adaptive mode decomposition module 204, an intrinsic mode function component selection module 205, a singular value decomposition module 206, a singular value selection module 207, a component signal reconstruction module 208, a seismic signal reconstruction module 209, and a seismic data weak signal extraction module 210.

[0073] Data acquisition module 201 can be used to acquire well logging data, seismic data and geological information of the study area;

[0074] The data processing module 202 can be used to preprocess the acquired well logging data and seismic data;

[0075] The stratigraphic calibration module 203 can be used to perform seismic geological stratigraphic calibration based on the preprocessed well logging data and seismic data obtained and integrated geological information.

[0076] The adaptive mode decomposition module 204 can be used to perform adaptive mode decomposition on the seismic data of the target segment according to the calibrated layer to obtain a series of intrinsic mode function components;

[0077] The intrinsic mode function component selection module 205 can be used to select appropriate intrinsic mode function components based on the intrinsic mode function components obtained through adaptive mode decomposition, and by integrating well logging and geological information.

[0078] The singular value decomposition module 206 can be used to perform singular value decomposition on each of the selected intrinsic mode function components;

[0079] The singular value selection module 207 can be used to select appropriate singular values ​​for each intrinsic mode function component based on the singular values ​​of each intrinsic mode function component obtained through singular value decomposition, and by combining well logging and geological information.

[0080] The component signal reconstruction module 209 can be used to reconstruct each intrinsic mode function component signal according to the selected singular values ​​to obtain each intrinsic mode function component signal reconstructed by singular value decomposition.

[0081] The seismic signal reconstruction module 209 can be used to reconstruct seismic signals based on the obtained reconstructed intrinsic mode function component signals;

[0082] The weak signal extraction module 210 for seismic data can be used to obtain weak seismic data signals that reflect minute changes in the properties of the geological medium based on the reconstructed seismic signals.

[0083] The seismic data weak signal extraction device based on differential component analysis provided in this embodiment of the invention can perform the above-described functions. Figure 1 The specific implementation process of the method embodiment shown is detailed in the method embodiment and will not be repeated here.

[0084] Those skilled in the art should understand that the above embodiments are merely illustrative of the beneficial effects of the present invention and are not exhaustive. Any modifications, equivalent substitutions, improvements, etc., made without departing from the scope and spirit of the described embodiments should not be excluded from the protection scope of the present invention.

Claims

1. A method for extracting weak signals from seismic data based on differential component analysis, characterized in that... The following steps are adopted: Acquire well logging data, seismic data, and geological information for the target area; The acquired well logging data and seismic data are preprocessed; Comprehensive seismic and geological stratigraphic determination is carried out by integrating well logging data, seismic data, and geological information. Based on the calibrated horizons, adaptive mode decomposition is performed on the seismic data to obtain a series of intrinsic mode function components; Select appropriate intrinsic mode function components by combining well logging and geological information; Higher-order singular value decomposition is performed on each selected intrinsic mode function component; By combining well logging and geological information, appropriate singular values ​​are selected for each intrinsic mode function component; The intrinsic mode function component signals are reconstructed based on the selected singular values ​​to obtain the intrinsic mode function component signals reconstructed by higher-order singular value decomposition. Seismic signal reconstruction is performed based on the obtained reconstructed intrinsic mode function component signals; The above steps were repeated using the micro-component analysis method. The results of adaptive mode decomposition and higher-order singular value decomposition were processed by removing the larger components and discarding the main component signals that reflect the properties of the geological medium. In this way, weak seismic data signals reflecting the subtle changes in the properties of the geological medium were reconstructed.

2. The method for extracting weak signals from seismic data based on differential component analysis according to claim 1, characterized in that: The adaptive mode decomposition method includes empirical mode decomposition methods and variational mode decomposition methods, which should be selected according to actual needs.

3. The method for extracting weak signals from seismic data based on differential component analysis according to claim 1, characterized in that: In the process of selecting appropriate intrinsic mode function components by integrating well logging and geological information, some low-order intrinsic mode function components are discarded according to actual needs in order to eliminate noise in seismic data.

4. The method for extracting weak signals from seismic data based on differential component analysis according to claim 1, characterized in that: In the process of selecting appropriate singular values ​​for each intrinsic mode function component based on integrated well logging and geological information, some low-order singular values ​​are discarded according to actual needs to remove some residual noise in the seismic data and better highlight the weak seismic response of the reservoir.

5. A device for extracting weak signals from seismic data based on micro-component analysis, characterized in that, include: The data acquisition module is used to acquire well logging data, seismic data, and geological information of the target area; The data processing module is used to preprocess the acquired well logging data and seismic data; The stratigraphic calibration module is used to perform comprehensive seismic and geological stratigraphic calibration based on preprocessed well logging data and seismic data, combined with geological information. The adaptive mode decomposition module is used to perform adaptive mode decomposition on seismic data based on the calibrated horizons to obtain a series of intrinsic mode function components. The intrinsic mode function component selection module is used to select appropriate intrinsic mode function components based on the intrinsic mode function components obtained through adaptive mode decomposition, and by integrating well logging and geological information. The singular value decomposition module is used to perform high-order singular value decomposition on each selected intrinsic mode function component. The singular value selection module is used to select appropriate singular values ​​for each intrinsic mode function component based on the singular values ​​obtained from higher-order singular value decomposition and by combining well logging and geological information. The component signal reconstruction module is used to reconstruct each intrinsic mode function component signal based on the selected singular values, so as to obtain each intrinsic mode function component signal reconstructed by singular value decomposition. The seismic signal reconstruction module is used to reconstruct seismic signals based on the obtained reconstructed intrinsic mode function component signals. The weak signal extraction module for seismic data is used to obtain weak seismic signals that reflect subtle changes in the properties of the geological medium based on the reconstructed seismic signals.

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