Multi-scale crack identification method, electronic device and medium

Abstract

This application discloses a multi-scale fracture identification method, electronic device, and medium. The method may include: performing Fourier spectrum analysis on seismic data to determine the dominant frequency; determining a time window based on the dominant frequency and calculating a seismic coherence volume; extracting slices along the target layer from the seismic coherence volume; determining the optimal number of modal decompositions, and performing decomposition on the slices along the layers to identify fractures of different scales and directions. The present invention determines the optimal number of variational mode decompositions by factoring them, decomposing the seismic coherence slices into modes of different frequency bands, which can reflect fractures of different scales and directions, and has considerable practical application value.

Description

Technical Field

[0001] The present invention relates to the field of crack identification, and more specifically, to a multi-scale crack identification method, electronic equipment, and medium. Background Art

[0002] Most existing multi-scale fracture identification methods first use wavelet transform and other methods to decompose seismic data into narrowband data of different frequency bands, and then perform coherent processing on these narrowband data. The coherence bodies of different frequency bands reflect fractures of different scales.

[0003] Coherent data volumes in different frequency bands can reveal fractures of varying scales. High-frequency coherence volumes can reveal small-scale fractures, while low-frequency coherence volumes can reveal large-scale fractures. Multi-scale fracture detection using frequency-splitting coherence technology can identify small faults and fracture zones that are difficult to detect using full-band data, but this method still has certain limitations.

[0004] (1) Wavelet frequency division is used to divide seismic data into narrowband data of different frequency bands. The frequency division result is affected by the type of wavelet and is limited by the Heisenberg uncertainty principle. The time domain resolution and frequency domain resolution restrict each other, and it is impossible to achieve high time-frequency analysis accuracy at the same time.

[0005] (2) The wavelet transform is used to perform frequency division processing on seismic trace data, which lacks an overall understanding of the seismic data.

[0006] (3) Since coherence processing will also change the spectral characteristics of seismic data, wavelet transform frequency division is performed first, and then coherence calculation is performed. In essence, it undergoes two frequency transformations, which reduces the reliability of the final result.

[0007] Therefore, it is necessary to develop a multi-scale fracture identification method, electronic equipment and medium to obtain richer information on fault and fracture development zones, and to identify some small faults and fracture development zones that are difficult to detect with conventional data.

[0008] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0009] The present invention proposes a multi-scale crack identification method, electronic equipment and medium, which can determine the optimal number of decompositions of variational mode decomposition through decomposition factors, decompose seismic coherent slices into modes of different frequency bands, and reflect cracks of different scales and directions, and has good practical application value.

[0010] In a first aspect, an embodiment of the present disclosure provides a multi-scale crack identification method, comprising:

[0011] Perform Fourier spectrum analysis on seismic data to determine the main frequency of the earthquake;

[0012] Determine a time window according to the earthquake main frequency and calculate an earthquake coherence volume;

[0013] Extracting target layer slices from the seismic coherence volume;

[0014] Determine the optimal number of modal decompositions, perform decomposition along the layer slices, and then identify cracks of different scales and directions.

[0015] Preferably, determining the optimal number of modal decompositions includes:

[0016] Setting multiple modal decomposition numbers, and performing VMD decomposition on the slice along the layer for each modal decomposition number;

[0017] For each decomposed layer slice, perform the following steps:

[0018] Decomposing the decomposed signal data along the slice into amplitude-frequency modulation signals to obtain decomposition residuals;

[0019] Calculating the correlation between the signal data and the decomposition residual;

[0020] Calculate energy entropy;

[0021] According to the correlation degree and energy entropy corresponding to the number of multiple modal decompositions, normalization calculation is performed to obtain the normalized correlation degree and energy entropy corresponding to each modal decomposition number;

[0022] The decomposition factors are calculated, and the modal decomposition number corresponding to the largest decomposition factor is taken as the optimal modal decomposition number.

[0023] Preferably, the decomposed signal data along the slice is decomposed into amplitude-frequency modulation signals, and the decomposition residual is obtained as:

[0024]

[0025] Among them, f(x) is the signal data, u k (x) is the AM / FM signal, k is the number of modal decompositions, and Rf(x) is the decomposition residual.

[0026] Preferably, the correlation between the signal data and the decomposition residual is calculated by formula (2):

[0027]

[0028] Where MI is the degree of correlation, p(x,y) is the joint probability distribution function of the decomposition residual X and the signal data Y, and p(x) and p(y) are the marginal probability distribution functions of the decomposition residual and signal data, respectively.

[0029] Preferably, the energy entropy is calculated by formula (3):

[0030]

[0031] Where EE is energy entropy, q k =E k / E total , E k is the energy of a single mode, E total is the total signal energy.

[0032] Preferably, the normalized correlation degree is calculated by formula (4):

[0033]

[0034] Among them, MI′ is the normalized correlation degree.

[0035] Preferably, the normalized energy entropy is calculated by formula (5):

[0036]

[0037] Where EE′ is the normalized energy entropy.

[0038] Preferably, the decomposition factor is calculated by formula (6):

[0039] MF=(EE'-MI') / 2 (6)

[0040] Among them, MF is the decomposition factor.

[0041] As a specific implementation of the embodiment of the present disclosure,

[0042] In a second aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0043] a memory storing executable instructions;

[0044] A processor runs the executable instructions in the memory to implement the multi-scale crack identification method.

[0045] In a third aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the multi-scale crack identification method is implemented.

[0046] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0048] Figure 1 A flow chart showing the steps of a multi-scale crack identification method according to an embodiment of the present invention.

[0049] Figure 2a and Figure 2b Schematic diagrams of slices along the layer of the top surface of the Paleozoic obtained by the prior art and the present method according to an embodiment of the present invention are respectively shown.

[0050] Figure 3a and Figure 3b Schematic diagrams of slices along the Paleozoic-Archean obtained by the prior art and the present method according to an embodiment of the present invention are respectively shown.

[0051] Figure 4 A schematic diagram of a slice along the Paleozoic-Archean layer according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0053] The present invention provides a multi-scale crack identification method, comprising:

[0054] Perform Fourier spectrum analysis on seismic data to determine the main frequency of the earthquake;

[0055] Determine the time window according to the main frequency of the earthquake and calculate the earthquake coherence volume;

[0056] Extract target layer slices from the seismic coherence volume;

[0057] Determine the optimal number of modal decompositions, perform decomposition along the layer slices, and then identify cracks of different scales and directions.

[0058] In one example, determining an optimal number of modal decompositions includes:

[0059] Set multiple modal decomposition numbers, and perform VMD decomposition along the slices for each modal decomposition number;

[0060] For each decomposed layer slice, perform the following steps:

[0061] Decomposing the decomposed signal data along the slice into amplitude-frequency modulation signals to obtain decomposition residuals;

[0062] Calculate the correlation between signal data and decomposition residuals;

[0063] Calculate energy entropy;

[0064] According to the correlation degree and energy entropy corresponding to the number of multiple modal decompositions, normalization calculation is performed to obtain the normalized correlation degree and energy entropy corresponding to each modal decomposition number;

[0065] Calculate the decomposition factor, and take the modal decomposition number corresponding to the largest decomposition factor as the optimal modal decomposition number.

[0066] In one example, the decomposed signal data along the slices are decomposed into amplitude-frequency modulated signals, and the decomposition residual is obtained as follows:

[0067]

[0068] Among them, f(x) is the signal data, u k (x) is the AM / FM signal, k is the number of modal decompositions, and Rf(x) is the decomposition residual.

[0069] In one example, the correlation between the signal data and the decomposition residual is calculated using formula (2):

[0070]

[0071] Where MI is the degree of correlation, p(x,y) is the joint probability distribution function of the decomposition residual X and the signal data Y, and p(x) and p(y) are the marginal probability distribution functions of the decomposition residual and signal data, respectively.

[0072] In one example, the energy entropy is calculated by formula (3):

[0073]

[0074] Where EE is energy entropy, q k =E k / E total , E k is the energy of a single mode, E total is the total signal energy.

[0075] In one example, the normalized correlation degree is calculated using formula (4):

[0076]

[0077] Among them, MI′ is the normalized correlation degree.

[0078] In one example, the normalized energy entropy is calculated using formula (5):

[0079]

[0080] Where EE′ is the normalized energy entropy.

[0081] In one example, the decomposition factor is calculated by formula (6):

[0082] MF=(EE'-MI') / 2 (6)

[0083] Among them, MF is the decomposition factor.

[0084] Specifically, the earthquake data is subjected to Fourier spectrum analysis to determine the main frequency of the earthquake; the time window is determined according to the main frequency of the earthquake, and the earthquake coherence volume is calculated.

[0085] The third-generation coherence volume technology is obtained by calculating the eigenvalues of the seismic data volume. First, the appropriate time window and number of channels are selected according to the main frequency of the earthquake to form the matrix D:

[0086]

[0087] The covariance matrix of this matrix is:

[0088]

[0089] The covariance matrix is a symmetric, semi-positive matrix with all eigenvalues greater than or equal to 0. Then the third-generation coherence based on the eigenstructure is:

[0090]

[0091] Extract the target layer slices from the seismic coherence volume; set multiple modal decomposition numbers, and perform VMD decomposition on the slices for each modal decomposition number; perform the following steps for each decomposed slice:

[0092] The decomposed signal data along the slice is decomposed into a series of amplitude-frequency modulated signals through the two-dimensional variational mode, i.e., formula (1), to obtain the decomposition residual. The essence of the two-dimensional variational mode decomposition is to solve the following optimization problem:

[0093]

[0094] Among them, ω k is the reference direction vector of the kth mode.

[0095] Introducing the Lagrangian operator, the solution to the above problem is:

[0096]

[0097] Then formula (1) becomes:

[0098]

[0099] Among them, λ is the Lagrangian operator, α k is the balance factor, {u k} is the decomposed mode set, {ω k} corresponds to {u k} is a set of center frequencies. In the frequency domain, {u k}The iterative form is:

[0100]

[0101] in, and are the Fourier transforms of f(t), u(t) and λ(t), respectively.

[0102] Center frequency {ω k}The iterative form is:

[0103]

[0104] The iterative form of the Lagrangian operator is:

[0105]

[0106] Through formulas (13)-(15), the two-dimensional variational mode decomposition method can decompose a two-dimensional signal (image) into components of different scales and directions.

[0107] The disadvantage of variational mode decomposition is that the number of decompositions K must be given in advance, and the choice of K affects the accuracy and reliability of the decomposition.

[0108] The degree of correlation between the decomposition residual Rf(x) and the signal data f(x) is measured using mutual information, i.e., formula (2), to determine the degree of signal decomposition. When MI is 0, it indicates that the two signals are uncorrelated.

[0109] The energy entropy calculated by formula (3) measures the degree of chaos in the system and thus determines the degree of aliasing between modes. The larger the EE, the more chaotic the system is and the smaller the degree of aliasing between modes.

[0110] Calculate the MI and EE for the decomposition results separately. Based on the correlation and energy entropy corresponding to multiple modal decomposition numbers K, perform normalization calculations using formulas (4) and (5) to obtain the normalized correlation and energy entropy corresponding to each modal decomposition number K. For example, if K = 3, 4, or 5 is specified, then each K corresponds to an MI and an EE. Finally, normalize these three MIs and EEs to obtain MI' and EE'. The smaller the MI and the larger the EE, the better the decomposition effect. Therefore, the decomposition factor is defined as formula (6). A larger MF indicates a better decomposition effect. That is, the modal decomposition number corresponding to the largest decomposition factor is the optimal modal decomposition number.

[0111] Determine the optimal number of modal decompositions, perform decomposition along the layer slices, and then identify cracks of different scales and directions based on the moduli of different decomposition scales.

[0112] The present invention also provides an electronic device, which includes: a memory storing executable instructions; and a processor running the executable instructions in the memory to implement the above-mentioned multi-scale crack identification method.

[0113] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the multi-scale crack identification method is implemented.

[0114] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that these examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0115] Example 1

[0116] Figure 1 A flow chart showing the steps of a multi-scale crack identification method according to an embodiment of the present invention.

[0117] like Figure 1 As shown, the multi-scale crack identification method includes: step 101, performing Fourier spectrum analysis on seismic data to determine the main frequency of the earthquake; step 102, determining the time window according to the main frequency of the earthquake, and calculating the seismic coherence body; step 103, extracting the target layer along the layer slice from the seismic coherence body; step 104, determining the optimal number of modal decompositions, decomposing the along-layer slices, and then identifying cracks of different scales and directions.

[0118] Fractures in a certain region have a significant controlling effect on carbonate oil and gas reservoirs. However, micro-fractures appear as distorted event axes on seismic sections without obvious faulting. Technologies such as coherence body, seismic attributes, and edge detection are difficult to accurately detect and identify.

[0119] Figure 2a and Figure 2b Schematic diagrams of slices along the Paleozoic top surface obtained by the prior art and the present method according to an embodiment of the present invention are shown. Compared with the prior art, the fault details obtained by the present method are clearer than the coherence volume, and the fault line is refined.

[0120] Figure 3a and Figure 3b Schematic diagrams of slices along the Paleozoic-Archean layer obtained using conventional techniques and this method, respectively, according to an embodiment of the present invention. Both methods can detect fault and fracture zones, but this method produces clearer details than the coherence volume, refining fault lines and revealing fracture details.

[0121] Figure 4 A schematic diagram of a slice along the Paleozoic-Archaean layer according to one embodiment of the present invention is shown. Analysis of known oil testing results reveals that the fracture detection results are consistent with the oil testing results: the dashed lines indicate high oil production, while the solid lines indicate oil-free areas, demonstrating that fractures have a strong control effect on oil and gas.

[0122] A factor, M, is proposed to measure the effectiveness of the variational mode decomposition, thereby determining the optimal solution. The layer-by-layer slices calculated using the optimal solution better reflect small-scale fractures, improving the visibility of weak signals and enabling detailed detection of low-order faults.

[0123] Fractures in this area have a strong control over oil and gas production, as confirmed by fractures revealed by slices along the bed, and consistent with existing oil testing. Finally, combining fracture information from slices along the bed with known geological and structural findings, seven new wells were predicted.

[0124] Example 2

[0125] The present disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the multi-scale crack identification method.

[0126] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0127] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0128] The processor may be a central processing unit (CPU) or other form of processing unit having data processing capability and / or instruction execution capability, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to execute the computer-readable instructions stored in the memory.

[0129] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of this disclosure.

[0130] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0131] Example 3

[0132] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the multi-scale crack identification method is implemented.

[0133] According to an embodiment of the present disclosure, a computer-readable storage medium stores non-transitory computer-readable instructions, which, when executed by a processor, execute all or part of the steps of the aforementioned methods of the embodiments of the present disclosure.

[0134] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0135] Those skilled in the art should understand that the above description of the embodiments of the present invention is only for the purpose of illustrative purposes only to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0136] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A multi-scale crack identification method, characterized in that: include: Perform Fourier spectrum analysis on seismic data to determine the main frequency of the earthquake; Determine a time window according to the earthquake main frequency and calculate an earthquake coherence volume; Extracting target layer slices from the seismic coherence volume; Determine the optimal number of modal decompositions, perform decomposition along the layer slices, and then identify cracks of different scales and directions; Determining the optimal number of modal decompositions includes: Setting multiple modal decomposition numbers, and performing VMD decomposition on the slice along the layer for each modal decomposition number; For each decomposed layer slice, perform the following steps: Decomposing the decomposed signal data along the slice into amplitude-frequency modulation signals to obtain decomposition residuals; Calculating the correlation between the signal data and the decomposition residual; Calculate energy entropy; According to the correlation degree and energy entropy corresponding to the number of multiple modal decompositions, normalization calculation is performed to obtain the normalized correlation degree and energy entropy corresponding to each modal decomposition number; Calculating the decomposition factor, and taking the modal decomposition number corresponding to the largest decomposition factor as the optimal modal decomposition number; The energy entropy is calculated by formula (3): Where EE is energy entropy, q k =E k / E total , E k is the energy of a single mode, E total is the total signal energy; The normalized correlation degree is calculated by formula (4): Among them, MI′ is the normalized correlation degree, and MI is the correlation degree; The normalized energy entropy is calculated by formula (5): Where EE′ is the normalized energy entropy; The decomposition factor is calculated by formula (6): MF=(EE'-MI') / 2 (6) Among them, MF is the decomposition factor.

2. The multi-scale crack identification method according to claim 1, wherein: Decompose the signal data along the slice into AM / FM signals, and obtain the decomposition residual as follows: Among them, f(x) is the signal data, u k (x) is the AM / FM signal, k is the number of modal decompositions, and Rf(x) is the decomposition residual.

3. The multi-scale crack identification method according to claim 2, wherein: The correlation between the signal data and the decomposition residual is calculated by formula (2): Where p(x,y) is the joint probability distribution function of the decomposition residual X and the signal data Y, and p(x) and p(y) are the marginal probability distribution functions of the decomposition residual and signal data, respectively.

4. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the multi-scale crack identification method according to any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the multi-scale crack identification method according to any one of claims 1 to 3 is implemented.

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