Seismic data multi-level decomposition and superposition method for crack identification

Abstract

The invention provides a seismic data multi-level decomposition and superposition method used for crack identification. The method comprises the following steps of: 1, performing multi-level decomposition and superposition on seismic data; the method comprises the following steps: 1, decomposing original seismic data into multi-level seismic data, and establishing a seismic data alternative library; step 2, determining an identification demand, obtaining the dominant frequency of the original seismic data and the multi-level seismic data, and screening out superimposed data meeting the identification demand according to the identification demand and the dominant frequency; and step 3, extracting different seismic attributes from the screened superimposed data, and realizing crack identification according to the obtained seismic attributes. The seismic data multi-level decomposition and superposition method for crack identification has the advantages that the accuracy of seismic data can be improved, so that the crack identification precision is improved, and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a multi-level decomposition and superposition method of seismic data for crack identification. Background Art

[0002] A reservoir is a rock formation with interconnected pores that allows oil and gas to store and percolate. The greater the porosity and permeability of a reservoir, the more favorable it is for oil and gas accumulation. The effectiveness and success of oil and gas exploration and development hinges on whether understanding the reservoir is consistent with objective reality. Before each well is officially put into development and production, a comprehensive evaluation of its inherent oil and gas endowment is conducted, analyzing various aspects such as oil and gas production, potential, and profitability. Early-stage well logging and mud recording techniques can only provide basic reservoir data. Because the factors influencing reservoir characteristics are complex and multifaceted, only comprehensive reservoir measurement, classification, research, and evaluation can provide scientific and feasible guidance for subsequent reservoir modification and development.

[0003] Reservoirs are categorized by reservoir space into porous, fractured, and cavernous reservoirs. Fractured reservoirs primarily consist of structural fractures and interlayer fractures. Because the number, shape, and distribution of fractures can be extremely uneven, porosity and permeability can vary significantly. Fractures in fractured reservoirs serve as important oil and gas storage spaces and migration pathways. The development of fractures within reservoirs helps improve reservoir performance, making the study of fractures a hot topic both domestically and internationally. Seismic data contain a variety of seismic attributes, and the characteristics of different attributes can reflect fracture characteristics to a certain extent.

[0004] Yang Guoquan et al. used fitting quadratic surfaces to develop a curvature calculation method, which has shown promising results in large-scale fracture identification ("Research on Curvature Attribute Calculation Methods and Effectiveness Analysis," Progress in Geophysics, 2015). Qin Si et al. increased the stability of the variance attribute by increasing the calculation time window of the variance algorithm, achieving significant results in identifying mesoscale faults ("Research on the Mechanism of Coherence and Variance Fracture Detection Algorithms," Inner Mongolia Petrochemical, 2007). Xu Hongxia et al. used curvature attributes to describe the distribution of caves and gullies in carbonate rocks, also demonstrating good results in identifying small-scale fractures ("Application of Multi-Attribute Analysis Technology in Predicting Fault-Ksolution Bodies in Carbonate Rocks," Petroleum Geophysical Exploration, 2017). However, due to the high level of external interference from seismic data, such as source location, minimum offset, distance between geophones, and subsurface structural complexity, seismic attributes generally cannot accurately characterize fractures. Therefore, there is an urgent need to process acquired seismic data to improve the accuracy of seismic attributes.

[0005] Although wavelet transform frequency reconstruction, generalized S-transform, and inverse Q filtering methods can improve the accuracy of raw seismic data, their capabilities are limited and cannot meet the requirements of fracture identification. However, multi-level classification techniques can modify the scale of seismic data to obtain sub-data containing different types of effective information. Furthermore, by overlaying seismic data at different levels, different seismic details can be highlighted. To this end, this paper proposes a multi-level decomposition and overlay method for seismic data for fracture identification. Summary of the Invention

[0006] To solve the above problems, the present disclosure provides a multi-level decomposition and superposition method of seismic data for fracture identification, so as to improve the accuracy of fracture identification by improving the accuracy of seismic data.

[0007] A multi-level decomposition and superposition method of seismic data for crack identification according to the present invention comprises the following steps:

[0008] Step 1: Decompose the original seismic data into multi-level seismic data and establish a seismic data candidate library;

[0009] Step 2: Determine the recognition requirement, obtain the main frequency of the original seismic data and the multi-level seismic data, and select the superimposed data that meets the recognition requirement based on the recognition requirement and the magnitude of the main frequency;

[0010] Step 3: Extract different seismic attributes from the screened superimposed data, and identify cracks based on the obtained seismic attributes.

[0011] Furthermore, in step 1, the original seismic data is decomposed into multi-level seismic data using a data hierarchical structure decomposition technique.

[0012] Furthermore, by using data hierarchical structure decomposition technology, the original seismic data can be decomposed into up to 7 levels according to the size of the original seismic data.

[0013] Furthermore, data hierarchical structure decomposition technology is used to design different radial filters and directional filters according to geological characteristics to decompose the original seismic data.

[0014] Furthermore, each level of the hierarchical image is obtained by performing radial filtering on the level image of the previous level through a radial filter, and then performing directional filtering on the filtered result through a directional filter.

[0015] Furthermore, in step 1, the multi-level seismic data are superimposed in sequence, and a seismic data candidate library is established using the superposition results.

[0016] Furthermore, in step 2, the spectrum analysis method is used to obtain the main frequency of the original seismic data and the main frequency f of the seismic data in the candidate library. b .

[0017] Furthermore, the seismic attributes include curvature attributes, variance attributes and / or mean curvature attributes.

[0018] The present invention also discloses an electronic device, characterized in that it includes: at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the multi-level decomposition and superposition method of seismic data for crack identification.

[0019] The present invention also discloses a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the multi-level decomposition and superposition method of seismic data for crack identification is implemented.

[0020] Compared with the prior art, the present disclosure has the following advantages:

[0021] The present invention provides a multi-level decomposition and superposition method of seismic data for crack identification, comprising the following steps: Step 1: decomposing original seismic data into multi-level seismic data and establishing a seismic data candidate library; Step 2: determining identification requirements, obtaining the main frequencies of the original seismic data and the multi-level seismic data, and screening out superposed data that meets the identification requirements based on the identification requirements and the size of the main frequencies; Step 3: extracting different seismic attributes from the screened superposed data, and realizing crack identification based on the obtained seismic attributes.

[0022] The core of the multi-level decomposition and superposition method of seismic data for crack identification of the present invention is to identify crack characteristics based on the selection of multi-level optimal superposition seismic data. It can improve the accuracy of seismic data, thereby enhancing the accuracy of crack identification and solving the problem of difficulty in crack identification in existing methods.

[0023] The multi-level decomposition and superposition method of seismic data for crack identification of the present invention has the advantages of improving the accuracy of seismic data and thus enhancing the accuracy of crack identification.

[0024] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 The flowchart of the multi-level decomposition and superposition method of seismic data for fracture identification of the present invention is shown.

[0027] Figure 2 Comparison diagram of curvature attributes identified by the multi-level decomposition and superposition method of seismic data for fracture identification according to the present invention and actual fracture development.

[0028] Figure 3 A comparison diagram of the curvature attributes identified by the existing technology and the actual fracture development;

[0029] Figure 4 This is the curvature attribute result diagram of the entire work area identified using existing technology methods. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0031] like Figure 1 The present invention provides a multi-level decomposition and superposition method of seismic data for crack identification, comprising the following steps:

[0032] Step 1: Decompose the original seismic data into multi-level seismic data and establish a seismic data candidate library;

[0033] Step 2: Determine the recognition requirement, obtain the main frequency of the original seismic data and the multi-level seismic data, and select the superimposed data that meets the recognition requirement based on the recognition requirement and the magnitude of the main frequency;

[0034] Step 3: Extract different seismic attributes from the screened superimposed data, and identify cracks based on the obtained seismic attributes.

[0035] During specific implementation, in step 1, the data hierarchical structure decomposition technology is used to decompose the original seismic data into multi-level seismic data.

[0036] In the step 1, the original earthquake is decomposed into multi-level earthquake data, multi-level earthquake sub-data are extracted, and the multi-level earthquake data are superimposed in sequence to establish an earthquake data candidate library.

[0037] This step includes the following steps:

[0038] Step 11: Input the original seismic data and perform multi-level decomposition processing on it to obtain multiple sub-data. This method uses data hierarchical structure decomposition technology for decomposition, but other hierarchical methods can also be selected for different data, such as seismic frequency division processing.

[0039] Step 12: Overlay the multi-level seismic data in step 11 in sequence;

[0040] Step 13: Place the superposition results of step 12 into the candidate library to establish a seismic data candidate library, providing a complete seismic combination option for subsequent screening.

[0041] In practice, to obtain seismic data containing various frequency information, the results of seismic pyramid navigation processing must be superimposed. Unlike seismic frequency division processing, pyramid navigation processing cannot be decomposed into an infinite number of levels. Depending on the size of the seismic data being processed, data hierarchy decomposition technology can decompose the raw seismic data into up to seven levels, which are then sequentially superimposed to create a total of 28 possible combinations. Finally, these 28 superimposed results are stored in a candidate library for subsequent selection.

[0042] In specific implementation, data hierarchical structure decomposition technology is adopted to decompose the original seismic data into up to 7 levels according to the size of the original seismic data.

[0043] In specific implementation, data hierarchical structure decomposition technology is used to design different radial filters and directional filters according to geological characteristics to decompose the original seismic data.

[0044] To decompose raw seismic data into seismic data at different frequencies, it is first necessary to perform a multi-level decomposition process on the data. Data hierarchical structure decomposition technology achieves this multi-level decomposition by characterizing the raw seismic information at multiple scales. This method uses different radial and directional filters designed based on geological characteristics to perform a multi-level decomposition of the raw seismic data.

[0045] In a specific implementation, each level of the hierarchical image is obtained by performing radial filtering on the level image of the previous level through a radial filter, and then passing the filtering result through a directional filter for directional filtering.

[0046] In the original seismic data, each level of the hierarchical image undergoes a combination of radial filtering and downsampling. Each level of the hierarchical image is obtained by radially filtering the hierarchical image of the previous level with a radial filter, and then filtering the filtered result through a directional filter. The directional filter is defined according to different angles and is expressed by the following formulas (1) and (2).

[0047] G k =∑ m ∑ n W k G k-1 (2 +m,2y+n) (1)

[0048]

[0049] In formula (1) and formula (2), G k represents the kth layer of the pyramid, (x, y) represents the pixel arrays of different levels of images, and W k Represents a window function with low-pass characteristics. Where f θ (x, y) is the function of the controllable filter in the θ direction, which can be obtained by the interpolation function k in the θ direction. j Basis functions in the (θ) and θ directions The linear combination is obtained, where M is the logarithm of the basis function and the interpolation function, and j represents the jth pair of basis function and interpolation function. The directional filtered image is then multiplied by the corresponding interpolation function, and the components are summed to obtain the final hierarchical image. m and n represent the size of the window matrix function with low-pass characteristics, respectively. m means the matrix has m rows, and n means the matrix has n columns.

[0050] At the same time, for different raw seismic data, other frequency division methods can also be used, such as seismic frequency division processing. In other words, for raw seismic data, either data hierarchical structure decomposition technology or frequency division method can be selected for processing, but data hierarchical structure decomposition technology is more effective, reducing the loss of effective information and preventing the occurrence of false strong axis phenomena.

[0051] In specific implementation, in step 1, the multi-level seismic data are superimposed in sequence, and the superposition results are used to establish a seismic data candidate library.

[0052] In the specific implementation, in step 2, the main frequency of the original seismic data and the main frequency f of the seismic data in the candidate library are obtained by using the spectrum analysis method. b .

[0053] In specific implementation, the identification requirements are first determined based on the actual conditions of different regions, and then the dominant frequency of the seismic data is determined to finally select the superimposed data that meets the requirements. In the case of mining in different regions, the requirements for seismic data are different, and the processing of seismic data is also different. The specific situations include but are not limited to the following: (1) To identify cracks, high-frequency information is selected; (2) To identify faults, medium-frequency information is selected; (3) To identify stratigraphic trends, low-frequency information is selected.

[0054] During the initial mining phase of the study area, it was necessary to determine the underground structure and stratigraphic information. However, conventional seismic data, for various reasons, contains a lot of noise, which can lead to errors in stratigraphic tracing and structural characterization. Therefore, it is necessary to select low-frequency information, retain the trend information of the seismic data, and remove the excess high-frequency noise to better display the stratigraphic information.

[0055] In the middle stage of mining in the study area, it is necessary to determine the location of reservoir fault development and predict favorable reservoir areas. At this time, it is necessary to retain the medium-frequency information, highlight the discontinuous position of the seismic information, and better characterize the fault.

[0056] In the later stages of mining in the study area, it is necessary to identify reservoir fractures and further determine specific production capacity areas to lay the foundation for subsequent fracturing to increase production capacity. Fracture information is often reflected in high-frequency information, highlighting the subtle changes caused by fractures.

[0057] The spectrum analysis method is used to perform statistics on the original earthquake data and the earthquake data in the candidate library, and the corresponding main frequency f of the original earthquake data and the earthquake data in the candidate library is obtained. b , used for comparison and judgment, wherein the spectrum data of the optimal seismic data needs to meet the following setting conditions, which can be expressed by the following formula (3).

[0058]

[0059] In formula (3), f b (L) is the main frequency corresponding to the Lth type of superimposed seismic data, f b is the main frequency corresponding to the original seismic data image.

[0060] Finally, the best superimposed seismic data under the corresponding conditions can be screened out through different main frequency information.

[0061] During specific implementation, the seismic attributes include curvature attributes, variance attributes and / or mean curvature attributes.

[0062] During specific implementation, the causes and scales of the cracks in the study area are determined based on the actual geological conditions of different regions; based on the actual geological conditions, the corresponding seismic attributes with high recognition accuracy are extracted, and ultimately high-precision identification of crack development based on seismic attributes is achieved.

[0063] Because different seismic attributes have different fracture identification ranges and accuracies, they are suitable for identifying different fracture characteristics. The curvature attribute characterizes the degree of fracture development and is most sensitive to fractures characterized by flexure and folds, making it suitable for identifying large-scale faults. The variance attribute describes fractures by measuring the similarity of adjacent seismic signals. It is more sensitive to fractures caused by faults with strong discontinuities and is suitable for identifying medium-scale faults. The mean curvature attribute represents amplitude anomalies of seismic attributes and is more sensitive to micro-fractures, making it suitable for identifying small-scale fractures.

[0064] According to the actual situation of the study area, the following formula (4) is used to extract single curvature attribute, variance attribute, average curvature attribute and other attributes.

[0065]

[0066] In formula (4), K Cur represents the curvature property; p represents the horizontal coordinate of the curve; q represents the vertical coordinate of the curve; K Var represents the earthquake variance attribute; j ′ represents the variance calculation time; i represents the number of seismic traces, and its value is a real number; I represents the number of layers and fault traces selected when calculating the variance; L represents the time window length under the variance calculation time; w represents the triangular weighted function of the seismic variance volume, and its interval range is [0,1]; z represents the average amplitude under the variance calculation time; K Amp represents the average curvature attribute; A represents the amplitude value on the seismic slice.

[0067] The present invention also discloses an electronic device, characterized in that it includes: at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the multi-level decomposition and superposition method of seismic data for crack identification.

[0068] The present invention also discloses a computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the multi-level decomposition and superposition method of seismic data for crack identification is implemented.

[0069] The study area in this actual case is mainly composed of structural fractures (seismic sensitive parameters: curvature) caused by fault activity in sandstone fractures, most of which are oblique fractures and high-angle fractures. Figure 2 、 Figure 3 It is verified that the fracture development characteristics are different under different geological conditions. In this work area, the curvature attribute can better reflect the structural and fracture characteristics. Select the curvature attribute for extraction, such as Figure 4shown.

[0070] Finally, the development of fractures of different scales can be determined through seismic attributes. Based on the above information, the following identification criteria are established: For curvature, a value greater than 0.007 indicates that tectonic fractures caused by fault activity are highly likely to develop; between 0.004 and 0.007, it is considered possible to develop such fractures; and below 0.004, it is considered that such fractures are rarely developed.

[0071] The multi-level decomposition and superposition method of seismic data for crack identification of the present invention improves the accuracy of seismic data by decomposing the original earthquake into multi-level seismic data, and selects appropriate seismic attributes for extraction, thereby improving the accuracy of crack identification and solving the problem of difficulty in crack identification existing in the existing background technology methods.

[0072] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0073] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A multi-level decomposition and superposition method of seismic data for fracture identification, characterized in that: The method comprises the following steps: Step 1: Decompose the original seismic data into multi-level seismic data and establish a seismic data candidate library; Step 2: Determine the recognition requirement, obtain the main frequency of the original seismic data and the multi-level seismic data, and select the superimposed data that meets the recognition requirement based on the recognition requirement and the magnitude of the main frequency; Step 3: Extract different seismic attributes from the screened superimposed data, and identify cracks based on the obtained seismic attributes.

2. The multi-level decomposition and superposition method of seismic data for fracture identification according to claim 1, characterized in that: In the step 1, the original seismic data is decomposed into multi-level seismic data using a data hierarchical structure decomposition technique.

3. The multi-level decomposition and superposition method of seismic data for fracture identification according to claim 2, characterized in that: By using data hierarchical structure decomposition technology, the original seismic data can be decomposed into up to 7 levels according to the size of the original seismic data.

4. The multi-level decomposition and superposition method of seismic data for fracture identification according to claim 2, characterized in that: The data hierarchical structure decomposition technology is used to design different radial filters and directional filters according to geological characteristics to decompose the original seismic data.

5. The multi-level decomposition and superposition method of seismic data for fracture identification according to claim 4, characterized in that: The hierarchical image of each level is obtained by radially filtering the hierarchical image of the previous level through a radial filter, and then passing the filtering result through a directional filter for directional filtering.

6. The multi-level decomposition and superposition method of seismic data for fracture identification according to claim 1, characterized in that: In the step 1, the multi-level seismic data are superimposed in sequence, and the superposition results are used to establish a seismic data candidate library.

7. The multi-level decomposition and superposition method of seismic data for fracture identification according to claim 1, characterized in that: In step 2, the spectrum analysis method is used to obtain the main frequency of the original seismic data and the main frequency f of the seismic data in the candidate library. b .

8. The multi-level decomposition and superposition method of seismic data for fracture identification according to claim 1, characterized in that: The seismic attributes include curvature attributes, variance attributes and / or mean curvature attributes.

9. An electronic device, characterized in that: include: At least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the multi-level decomposition and superposition method of seismic data for crack identification as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multi-level decomposition and superposition method of seismic data for fracture identification according to any one of claims 1 to 8 is implemented.

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