Method and device for constructing heterogeneous plastid configuration data and medium

Abstract

The invention belongs to the technical field of petroleum exploration, and discloses a method, a device and a medium for constructing heterogeneous plastid configuration data, and the method realizes the construction of the heterogeneous plastid configuration data through covariance matrix and feature acquisition, matrix dimension reduction processing and identification processing of heterogeneous feature data. According to the method, the maximum common data of the stratigraphic structure with strong background energy is eliminated by means of matrix dimension reduction processing and the like, weak seismic signals in heterogeneous plastid configurations such as a small-scale pore system and the like are extracted and enhanced, and then high-precision space engraving of the small-scale pore system is achieved. The device and the medium provided by the invention are suitable for constructing a data model of a heterogeneous plastid configuration.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of geological model data construction, and relates to a heterogeneous geological body configuration data, in particular to a method for constructing a heterogeneous geological body configuration data, a device and a medium. BACKGROUND

[0002] Exploration and development results show that, compared with low-layer structure information which is easy to distinguish in geophysical characteristics, only fracture and cave containing heterogeneous geological body configuration is the key factor for oil and gas migration and accumulation. With the deepening of oil and gas exploration process, the shallow and easy-to-prove system data model has been relatively easy to obtain, and further exploration is needed for deep fracture and cave type reservoirs. In the deep fracture and cave, compared with the slightly large scale pore system, the small scale pore system has very weak geophysical characteristics itself, and its geophysical characteristics are more difficult to be distinguished under the seismic response of the strong wave impedance difference stratum, so the small scale pore system becomes the main research object which needs to be described and characterized by the exploration personnel.

[0003] The existing method for constructing geological body configuration data is mainly used for describing large scale pore system, and the prediction accuracy of the method for small scale pore system is very low in terms of conventional coherence, structural curvature, uniform root amplitude, instantaneous amplitude and other sensitive attributes, which cannot meet the needs of current oil and gas exploration and development. SUMMARY

[0004] In order to solve the above problems in the prior art, the present application aims to provide a method for constructing a heterogeneous geological body configuration data, which can realize extraction and enhancement of weak seismic signals in small scale pore system by excluding strong background energy stratum structure maximum common data, obtain heterogeneous characteristic data, and achieve high precision spatial sculpture of small scale pore system.

[0005] Another object of the present application is to provide a device, an electronic device and a medium based on the above method for constructing a heterogeneous geological body configuration data.

[0006] To achieve the above object, the technical scheme adopted by the present application is as follows:

[0007] A method for constructing a heterogeneous geological body configuration data, characterized by comprising the following steps performed in sequence:

[0008] S1. Covariance matrix and feature acquisition:

[0009] The original amplitude data is expressed as X according to the number of rows n and the number of columns m n×m , denoted as matrix I;

[0010] Each row of matrix I is subjected to zero mean processing to obtain matrix II;

[0011] The covariance matrix III is obtained by calculating the matrix II, and the eigenvalue and the corresponding eigenvector are obtained;

[0012] S2. Matrix dimension reduction processing:

[0013] The eigenvectors are arranged into a matrix IV according to the corresponding eigenvalue size;

[0014] The first k rows of the matrix IV are taken to form a principal component matrix P k×n According to the maximum commonality data Y k×m =P k×n X n×m , Y k×m is the maximum commonality data of the stratum structure after dimension reduction to k dimensions;

[0015] S3. Identify the non-homogeneous feature data:

[0016] The non-homogeneous feature data obtained by subtracting the maximum commonality data from the matrix I is subjected to attribute calculation, that is, the non-homogeneous geological body configuration data is constructed.

[0017] The attribute calculation is to generate a gray value matrix by using the non-homogeneous feature data, and extract and count the repetition times of the gray feature values.

[0018] Wherein, n, m and k are positive integers, and k<n.

[0019] As a limitation of the present application, the zero-mean processing is to calculate the mean value of each row data respectively, and to subtract the mean value of the row where each value is located from each value to form the matrix II.

[0020] As a further limitation of the present application, the matrix IV is arranged in rows from top to bottom according to the eigenvalue from large to small.

[0021] As a further limitation of the present application, the formula for calculating the covariance matrix III is Wherein, C n×n is the covariance matrix III;

[0022] X and X T are the matrix II and its transpose matrix, respectively.

[0023] As a further limitation of the present application, the attribute calculation is the calculation of seismic structure tensor attribute, variance attribute or texture attribute.

[0024] As a further limitation of the present application, the texture attribute calculation is to perform gray scale transformation according to the texture scale of the non-homogeneous feature data, generate a gray scale accompanying matrix, and then extract and count the repetition times of the gray feature values in the gray scale accompanying matrix for clustering.

[0025] The application further provides a device for constructing non-homogeneous geological body configuration data, comprising a covariance matrix and feature acquisition module, a matrix dimension reduction processing module and a non-homogeneous geological body configuration data construction module.

[0026] The covariance matrix and feature acquisition module is used to obtain a covariance matrix III, eigenvalues and corresponding eigenvectors; the matrix dimension reduction processing module is used to obtain maximum commonality data through dimension reduction processing of the matrix; and the non-homogeneous geological body configuration data construction module is used to obtain non-homogeneous feature data and complete construction of the non-homogeneous geological body configuration data.

[0027] The application further provides an electronic device, which comprises:

[0028] a memory storing executable instructions;

[0029] a processor running the executable instructions in the memory to implement the method for constructing non-homogeneous geological body configuration data according to any one of the above technical solutions.

[0030] The application further provides a computer readable storage medium storing a computer program, which is executed by a processor to implement the method for constructing non-homogeneous geological body configuration data according to any one of the above technical solutions.

[0031] Thanks to the above technical solutions, the application has the following advantages over the prior art:

[0032] (1) The method for constructing non-homogeneous geological body configuration data is based on the principle of the seismic trace cross-correlation method, and first optimizes and reconstructs the maximum commonality data of the original stratum structure through covariance matrix and feature acquisition and matrix dimension reduction processing, and removes the maximum commonality of the stratum structure from the original seismic data to enhance the seismic wave field anomaly caused by the tectonic activity of the non-homogeneous geological body configuration due to fracture fragmentation, fluid dissolution, etc.; the most important elements and structure of the seismic data in the analysis target layer are found out to reduce the dimension of the original complex data, reveal the simple structure hidden behind the complex data, and separate the fracture-vug body seismic response and the conventional stratum reflection in the seismic profile.

[0033] (2) The method for constructing non-homogeneous geological body configuration data can effectively reveal the dominant pore distribution characteristics of the fracture-controlled vug body, and improves the fine depiction capability of the fracture-vug system. In a three-dimensional working area in the Tarim Basin, the application is applied in the fracture-vug geological region with numerous small-scale pore systems, and effectively guides the division and comprehensive evaluation of the fracture-controlled vug unit, and supports the formulation and optimization of the oil reservoir development plan.

[0034] (3) The method of constructing heterogeneous geological body configuration data of the present invention significantly improves the description accuracy, and the method requires less computation and fewer hardware resources. Attached Figure Description

[0035] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0036] Figure 1 This is a schematic diagram of the original amplitude data profile in Embodiment 1 of the present invention;

[0037] Figure 2 This is a cross-sectional view of the maximum common data of the stratigraphic structure when different values ​​of k are taken in the embodiments of the present invention;

[0038] Figure 3 This is a cross-sectional view of heterogeneous feature data for different values ​​of k in an embodiment of the present invention;

[0039] Figure 4 This is a planar attribute map of the heterogeneous geological body configuration data in the comparative experiment of the present invention;

[0040] In the figure, (a)-(d) correspond to the results of Examples 1-4 respectively. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the described embodiments are only used to explain the present invention and do not limit the present invention.

[0042] Example 1: A method for constructing heterogeneous geological body configuration data

[0043] This embodiment uses a three-dimensional field in the Tarim Basin as the test site. This field has numerous micro-scale pore systems. The original amplitude data profile obtained from this field... Figure 1 The construction of heterogeneous geological body configuration data includes the following steps:

[0044] S1. Covariance matrix and feature extraction:

[0045] Extract Figure 1 The original amplitude data in the data is represented as X with n=10 rows and m=8 columns. n×m Let I be the matrix.

[0046] Perform zero-mean processing on each row of matrix I: calculate the mean of each row of data, and calculate the difference between each value and the mean of the row in which the value is located, and use the difference to form matrix II.

[0047] Let matrix II be denoted as X, and its transpose be denoted as Xtranspose. T ;

[0048] X, XT Substitute into the formula Find the covariance matrix III, i.e., C. n×n ;

[0049] Eigenvalues ​​and corresponding eigenvectors are extracted from covariance matrix III.

[0050] S2. Matrix Dimensionality Reduction:

[0051] Arrange the eigenvectors in descending order of their corresponding eigenvalues, from top to bottom, row by row, to obtain matrix IV;

[0052] Let k = 3 and n = 10, then the first 3 rows (k = 3) of matrix IV form the principal component matrix P. k×n Based on the most common data Y k×m =P k×n X n×m Y k×m This refers to the most common data of the stratigraphic structure after dimensionality reduction to 3D, and the corresponding data profile results are as follows: Figure 2 (a).

[0053] S3. Identify and process heterogeneous feature data:

[0054] The difference between matrix I and the data with the greatest commonality obtained in step S2 is calculated to obtain heterogeneous feature data. Texture attribute calculation is then performed, specifically: texture attributes are used to describe the distribution of special lithological bodies and reservoir variation characteristics. First, the texture scale is selected based on the size of the geological body. After grayscale transformation of the heterogeneous feature data, a grayscale adjoint matrix is ​​generated. Then, grayscale feature values ​​are extracted from the grayscale adjoint matrix, and the repetition frequency of grayscale feature values ​​is counted to complete the clustering of texture attributes, thus constructing heterogeneous geological body configuration data. The corresponding data profile results are as follows: Figure 3 (a).

[0055] Example 2: A method for constructing heterogeneous geological body configuration data

[0056] The original amplitude data in this embodiment is the same as in Embodiment 1, and the specific methods are also basically the same. The only difference is the selection of the k value in step S2. Therefore, the corresponding calculation method is adaptively adjusted, resulting in different graphs. The specific differences and results are as follows:

[0057] In “S2. Matrix Dimensionality Reduction Processing”, setting k=5, the resulting data profile is as follows: Figure 2 (b);

[0058] The data profile results obtained from “S3. Identifying and processing heterogeneous feature data” are as follows: Figure 3 (b)

[0059] Example 3: A method for constructing heterogeneous geological body configuration data

[0060] The original amplitude data in this embodiment is the same as in Embodiment 1, and the specific methods are also basically the same. The only difference is the selection of the k value in step S2. Therefore, the corresponding calculation method is adaptively adjusted, resulting in different graphs. The specific differences and results are as follows:

[0061] In “S2. Matrix Dimensionality Reduction Processing”, setting k=7, the resulting data profile is as follows: Figure 2 (c);

[0062] The data profile results obtained from “S3. Identifying and processing heterogeneous feature data” are as follows: Figure 3 (c)

[0063] Example 4: A method for constructing heterogeneous geological body configuration data

[0064] The original amplitude data in this embodiment is the same as in Embodiment 1, and the specific methods are also basically the same. The only difference is the selection of the k value in step S2. Therefore, the corresponding calculation method is adaptively adjusted, resulting in different graphs. The specific differences and results are as follows:

[0065] In “S2. Matrix Dimensionality Reduction Processing”, setting k=9, the resulting data profile is as follows: Figure 2 (d);

[0066] The data profile results obtained from “S3. Identifying and processing heterogeneous feature data” are as follows: Figure 3 (d)

[0067] Example 5: An apparatus for constructing heterogeneous geological body configuration data

[0068] This embodiment is an apparatus for constructing heterogeneous geological body configuration data, including a covariance matrix and feature acquisition module, a matrix dimensionality reduction processing module, and a heterogeneous geological body configuration data construction module;

[0069] The covariance matrix and feature acquisition module is used to obtain the covariance matrix III, eigenvalues ​​and corresponding eigenvectors; the matrix dimensionality reduction module is used to reduce the dimensionality of the matrix to obtain the maximum common data; the heterogeneous geological body configuration data construction module is used to obtain heterogeneous feature data and complete the construction of heterogeneous geological body configuration data.

[0070] Comparison of results:

[0071] Define L = k / n, then by comparing the heterogeneous geological body configuration data obtained from Examples 1 to 5 using different L values, such as... Figure 3The results show that the larger the L value, the more similar the obtained heterogeneous geological body configuration data is to the original amplitude data; conversely, the smaller the L value, the more linear the obtained heterogeneous geological body configuration data tends to be, making it easier to separate the seismic response of fracture-cavity bodies and conventional strata reflections in seismic profiles. Specifically, when the L value is 0.7, such as... Figure 3 (c) It is easier to separate the beaded strong amplitude and other related features of heterogeneous geological body configurations from those of homogeneous geological body configurations in seismic profiles.

[0072] Will Figure 3 (c) For characteristic regions of heterogeneous geological bodies, planar attribute calculations are performed to describe the distribution of strong heterogeneity information in the planar direction. The results are as follows: Figure 4 ,pass Figure 4 It can display regular data in the planar direction, thereby providing effective guidance for the division and comprehensive evaluation of fault-controlled fracture-cavity units, and supporting the formulation and optimization of reservoir development plans.

[0073] Example 6: An electronic device

[0074] This embodiment provides an electronic device, which includes: a memory storing executable instructions; and a processor that executes the executable instructions in the memory to implement the method for constructing heterogeneous geological body configuration data in Embodiment 1.

[0075] This 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, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0076] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment disclosed in this application, the processor is used to execute computer-readable instructions stored in the memory.

[0077] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0078] Example 7: A computer-readable storage medium

[0079] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for constructing heterogeneous geological body configuration data in Embodiment 1.

[0080] The computer-readable storage medium stores non-transitory computer-readable instructions thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods of the foregoing embodiments are performed.

[0081] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0082] Methods for constructing heterogeneous geological body configuration data in Examples 8-9

[0083] Examples 8 and 9 are basically the same as Example 1, except that the attribute calculation method in step S3 is different, wherein:

[0084] Example 8 uses seismic structural tensor properties for calculation;

[0085] Example 9 uses the earthquake structural variance attribute calculation.

[0086] Verification showed that Examples 8 and 9 can both construct heterogeneous geological body configuration data. The constructed data model can achieve a detailed characterization of the fracture-cavity system, facilitating the display of dominant pore distribution characteristics. It should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for constructing heterogeneous geological body configuration data, characterized in that, This includes the following steps performed sequentially: S1. Covariance matrix and feature extraction: The original amplitude data is represented as X by the number of rows n and the number of columns m. n×m Let I be the matrix; Each row of matrix I is zero-mean normalized to obtain matrix II; Calculate the covariance matrix III from matrix II, and obtain the eigenvalues ​​and corresponding eigenvectors; S2. Matrix Dimensionality Reduction Processing: Arrange the eigenvectors according to the size of their corresponding eigenvalues ​​to form a matrix IV; Take the first k rows of matrix IV to form the principal component matrix P k×n Based on the most common data Y k×m =P k×n X n×m Y k×m This refers to the most common data of the stratigraphic structure after dimensionality reduction to k dimensions; S3. Identify and process heterogeneous feature data: The heterogeneous feature data obtained by subtracting matrix I from the maximum commonality data are used to calculate attributes, thereby constructing heterogeneous geological body configuration data. The attribute calculation involves generating a grayscale matrix using heterogeneous feature data, and extracting and counting the number of repetitions of grayscale feature values. Where n, m, and k are all positive integers, and k <n。 2. The method for constructing heterogeneous geological body configuration data according to claim 1, characterized in that, The zero-mean process is as follows: calculate the mean of each row of data, and then calculate the difference between each value and the mean of the row in which the value is located, and use the difference to form matrix II.

3. A method for constructing heterogeneous geological body configuration data according to claim 1 or 2, characterized in that, The matrix IV is arranged in descending order of eigenvalues ​​and in rows from top to bottom.

4. The method for constructing heterogeneous geological body configuration data according to claim 3, characterized in that, The formula for obtaining the covariance matrix III is as follows: Among them, C n×n Covariance matrix III; X, X T These are matrix II and its transpose, respectively.

5. The method for constructing heterogeneous geological body configuration data according to claim 4, characterized in that, The attribute calculation is the calculation of seismic structural tensor attributes, variance attributes, or texture attributes.

6. The method for constructing heterogeneous geological body configuration data according to claim 5, characterized in that, Texture attribute calculation involves performing grayscale transformation on the texture scale of heterogeneous feature data to generate a grayscale adjoint matrix, then extracting and counting the repetition frequency of grayscale feature values ​​in the grayscale adjoint matrix, and performing clustering.

7. An apparatus for constructing configuration data of heterogeneous geological bodies, characterized in that, The device, according to any one of claims 1 to 6, provides a method for constructing heterogeneous geological body configuration data, comprising a covariance matrix and feature acquisition module, a matrix dimensionality reduction processing module, and a heterogeneous geological body configuration data construction module. The covariance matrix and feature acquisition module is used to obtain the covariance matrix III, eigenvalues ​​and corresponding eigenvectors; the matrix dimensionality reduction module is used to reduce the dimensionality of the matrix to obtain the maximum common data; the heterogeneous geological body configuration data construction module is used to obtain heterogeneous feature data and complete the construction of heterogeneous geological body configuration data.

8. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method for constructing heterogeneous geological body configuration data as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method for constructing heterogeneous geological body configuration data as described in any one of claims 1 to 6.

Images