Method and device for predicting stratum sedimentary body according to pre-stack seismic data
By performing principal component analysis on pre-stack seismic data, generating a stratigraphic time model and extracting sedimentary information, the problem of low prediction accuracy caused by small impedance differences between sandstone and mudstone in post-stack seismic data was solved, and a more accurate characterization of the sedimentary body was achieved.
Patent Information
- Application Number
- CN202410329214.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-21
- Publication Date
- 2025-09-23
Smart Images
Figure CN120686327A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of oil exploration and development technology, and in particular to a method and device for predicting stratum sedimentary bodies based on pre-stack seismic data. Background Art
[0002] my country's major oil and gas basins are primarily continental deposits, primarily composed of thin interbedded sandstone and mudstone layers. With increasing exploration efforts, these thin sand bodies have become a primary target of exploration. To improve the prediction accuracy of thin-bedded sediments, many researchers have conducted inversion algorithm research from the perspective of increasing vertical resolution, thereby predicting the thickness and distribution of thin reservoirs. These researchers have developed a variety of inversion methods, including sparse reflectance inversion, spectral inversion, waveform indicator inversion, and phase-controlled random inversion. Other researchers, based on tuning theory, have studied thin-bed spectrum, thin-bed resolution, the relationship between thin-bed peak frequency and thickness, and the relationship between thin interbed tuning and resolution. These researchers have developed thin-bed prediction methods such as spectral decomposition, peak instantaneous frequency attributes, and frequency-to-amplitude ratio attributes. Zeng Hongliu, Posamentier, and others proposed the concepts of seismic sedimentology and seismic geomorphology, respectively, leveraging the lateral resolution of seismic data. Their core concept is to detect thin beds through slice scanning, primarily encompassing two techniques: 90-degree phase shift and stratigraphic slicing. Li Guofa, Liu Huaqing, and others subsequently conducted detailed research on the challenges, rationality, and application of stratigraphic slicing.
[0003] Among some technologies, sedimentary analysis technology is mainly based on post-stack seismic data. The technology of performing stratigraphic slicing based on post-stack seismic data to carry out seismic sedimentary analysis has a close relationship between the accuracy of predicting thin sand bodies and the impedance difference between sandstone and mudstone. When the impedance difference between sandstone and mudstone is small, the post-stack seismic reflection is weak, the characteristics are not significant, and the prediction accuracy is reduced.
[0004] Therefore, there is an urgent need for a technology that can more accurately predict seismic sedimentation analysis. Summary of the Invention
[0005] The present application provides a method and apparatus for predicting stratigraphic sedimentary bodies based on prestack seismic data. The method implements a complex analysis technology for thin-layer sedimentary information based on prestack seismic data, which depicts sedimentary bodies more clearly and accurately.
[0006] In a first aspect, the present application provides a method for predicting a stratigraphic sedimentary body based on pre-stack seismic data, the method comprising:
[0007] Generating a stratigraphic time model based on post-stack seismic data from the study area;
[0008] determining a target layer according to the stratigraphic time model;
[0009] The principal component analysis method was used to extract the sedimentary information of the target layer from the prestack seismic data of the study area.
[0010] In the second aspect, an embodiment of the present invention also provides a device for predicting stratigraphic sediments based on pre-stack seismic data, the device comprising: a memory and a processor; the memory is used to store a program for predicting stratigraphic sediments based on pre-stack seismic data, and the processor is used to read and execute the program for predicting stratigraphic sediments based on pre-stack seismic data, and perform any one of the methods described in the above embodiments.
[0011] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a data processing program is stored, and the data processing program is used by a processor to execute the method described in any one of the above embodiments.
[0012] Compared to related technologies, this application provides a method and apparatus for predicting stratigraphic sedimentary bodies based on prestack seismic data. The method comprises: generating a stratigraphic time model based on poststack seismic data from a study area; determining a target layer based on the stratigraphic time model; and extracting sedimentary information for the target layer from the prestack seismic data from the study area using principal component analysis. By extracting sedimentary information for the target layer using principal component analysis from prestack seismic data, this application achieves a clearer and more accurate characterization of sedimentary bodies.
[0013] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.
[0015] Figure 1 This is a flow chart of a method for predicting stratum sedimentary bodies based on pre-stack seismic data according to an embodiment of the present application;
[0016] Figure 2 This is a schematic diagram of a device for predicting stratum sediments based on pre-stack seismic data according to an embodiment of the present application;
[0017] Figure 3 A flow chart of a method for predicting stratigraphic sedimentary bodies based on prestack seismic data in some exemplary embodiments;
[0018] Figure 4 is a schematic diagram of a synthetic seismic record in some exemplary embodiments;
[0019] Figure 5A schematic diagram of a stratigraphic slice of a target layer S1 according to a conventional method in some exemplary embodiments;
[0020] Figure 6 Schematic diagram of stratigraphic slices of the target layer S1 obtained by a method for predicting stratigraphic sedimentary bodies based on pre-stack seismic data in some exemplary embodiments. DETAILED DESCRIPTION
[0021] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.
[0022] This application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the appended claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the appended claims.
[0023] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present application.
[0024] The embodiment of the present invention provides a method for predicting stratum sedimentary bodies based on pre-stack seismic data, such as Figure 1 As shown, the method includes steps S100-S120:
[0025] S100: Generate a stratigraphic time model based on post-stack seismic data from the study area;
[0026] S110: determining a target layer according to the formation time model;
[0027] S120: Extracting sedimentary information of the target layer from the prestack seismic data of the study area using the principal component analysis method.
[0028] In an exemplary embodiment, generating a formation time model based on post-stack seismic data of a study area includes:
[0029] The first step is to establish the corresponding relationship between the reflection time of post-stack seismic data and the depth of the formation;
[0030] In this step, wells with good logging quality are generally selected to produce synthetic logs, which are then compared with the post-stack seismic data traces near the wells to establish a corresponding relationship between seismic reflection time and depth.
[0031] The second step is to determine multiple reference layers based on the geological information of the study area;
[0032] In this step, select isochronous marker seismic events as reference layers. Within the 3D seismic data volume, select marker seismic events of the same geological time as the reference layer. Typically, layers with continuous reflections, such as the largest flood (lake) surface or laterally stable coal seams, are selected. Multiple reference layers may be selected depending on the work area.
[0033] The third step is to establish a formation time model based on the corresponding relationship and the reference layer.
[0034] In an exemplary embodiment, establishing a formation time model based on the corresponding relationship and the reference layer includes:
[0035] The first step is to determine a correspondence between a first geological horizon and the time of the post-stack seismic data, and a correspondence between a second geological horizon and the time of the post-stack seismic data. The first geological horizon may be the shallowest geological horizon in terms of stratigraphic depth, and the second geological horizon may be the deepest geological horizon in terms of stratigraphic depth.
[0036] The second step is to calculate the average distance or maximum distance between each two reference layers according to the pre-set sampling interval of the stratigraphic slice data volume;
[0037] Step 3: Calculate the number of interpolated stratigraphic slices between every two reference layers according to the average distance or maximum distance between every two reference layers and the sampling interval;
[0038] The fourth step is to establish a stratigraphic time model using a linear interpolation function between every two reference layers according to the number of interpolated stratigraphic slices.
[0039] In an exemplary embodiment, the method for extracting sedimentary information of the target layer from the prestack seismic data in the study area by using the principal component analysis method includes:
[0040] The first step: obtaining the position information of the target layer in the three-dimensional space by tracing the seismic reflection corresponding to the target layer;
[0041] In this step, the depth of the target layer is determined through well logging data, the corresponding time of the target layer is determined through the time-depth correspondence relationship, and the position information of the target layer in the three-dimensional space is obtained by tracing the seismic reflection corresponding to the target layer.
[0042] The second step: extracting the feature subspace W of the seismic data for each sampling point in the seismic data by using the principal component analysis method;
[0043] In this step, for the post-stack seismic data, there is only one data point for the target layer at each point, with little information, so the accuracy of the finally predicted sedimentary information is low. For the prestack seismic data, there are multiple data points for the target layer at each point, and these data reflect the sedimentary information of the same point from different angles. In order to obtain the sedimentary data at this point, it is first necessary to use the principal component analysis method to reduce the dimension of the data. The implementation process is as follows:
[0044] 1) Establishing the spatio-temporal matrix of the prestack seismic data
[0045] Let X be the feature map of the sampling points, that is, an n*m-dimensional seismic data matrix, where n represents the number of sampling points of each trace of seismic data, and m represents the number of traces of the prestack seismic data.
[0046] 2) In the PCA-L2, that is, the principal component analysis that applies the L2 norm to constrain and minimize the error, determining a d-dimensional (d < n) feature subspace W, where W is the feature subspace to be solved, and d is the dimension of this feature subspace, so as to minimize the following error function;
[0047] Among them, the error function is:
[0048]
[0049] Among them, X is an n*m-dimensional seismic data matrix; W is the feature subspace to be solved, and d is the dimension of this feature subspace; V is the projection coefficient of the original data on the new feature subspace; X i is the column vector of X, represents obtaining the column vector of WV, and this step only represents expanding the matrix operation into the row-column vector operation form. The column vectors of V are the obtained principal components and are arranged according to the magnitude of the singular values. Therefore, the first column is the first principal component, the second column is the second principal component, and so on.
[0050] The third step is to obtain the feature map of the sampling point according to the feature subspace W;
[0051] Optimizing this problem is equivalent to performing SVD decomposition on X and obtaining the feature map of the sampling points:
[0052]
[0053] Where X is the characteristic map of the sampling points, that is, the n*m dimensional seismic data matrix, r is the number of principal components, U and V are orthogonal matrices, and D is an m*n diagonal orthogonal matrix; σ i represents the singular values on the diagonal of the diagonal matrix D, u i The column vector representing the U matrix is the coordinate of the data after PCA transformation; Indicates V T The column vector of the matrix is the principal component data.
[0054] In this way, using SVD to process seismic data is to sum the characteristic maps corresponding to larger eigenvalues. Naturally, the horizontal phase axis can be reconstructed using the first characteristic map.
[0055] The fourth step is to obtain the sedimentary information of the target layer based on the pre-stack seismic data through the characteristic map of each sampling point in the three-dimensional space of the target layer.
[0056] The thin-layer sedimentary information volume obtained along each stratigraphic horizon in the stratigraphic time model generates a 3D sedimentary information volume.
[0057] The embodiment of the present application implements a method for predicting stratigraphic sedimentary bodies based on pre-stack seismic data. By using the principal component analysis method on the pre-stack seismic data, the present application achieves a clearer and more accurate characterization of the sedimentary bodies.
[0058] The embodiment of the present invention also provides a device for predicting stratum sediments based on pre-stack seismic data, such as Figure 2 As shown, the device includes: a memory 200 and a processor 210; the memory is used to store a program for predicting stratigraphic sedimentary bodies based on pre-stack seismic data, and the processor is used to read and execute the program for predicting stratigraphic sedimentary bodies based on pre-stack seismic data, and execute any one of the methods in the above embodiments.
[0059] An embodiment of the present invention further provides a computer-readable storage medium, on which a data processing program is stored. The data processing program is used by a processor to execute the method described in any one of the above embodiments.
[0060] Example 1
[0061] The above method of predicting stratigraphic sedimentary bodies based on prestack seismic data was used to characterize the distribution range of the target thin reservoir in a certain study area. The specific implementation process is as follows: Figure 3 As shown:
[0062] 1. Generation of stratigraphic time model;
[0063] The generation of the formation time model 1 includes: establishing a time and depth correspondence relationship 101, selecting an isochronous marker seismic event as a reference layer 102, and establishing a formation time model 103. Figure 4 As shown in the synthetic seismic record of well1, the GR logging curve shows that the depth of the target layer S1 is about 2047 meters, and the corresponding seismic data time is 952 milliseconds.
[0064] 2. Extraction of thin layer sedimentation information using PCA method;
[0065] The PCA method thin layer sedimentation information extraction 2 includes: determining the target layer 201, extracting sedimentation information 202 using the PCA method, and generating a thin layer sedimentation information body based on pre-stack seismic data 203.
[0066] 3. Generate 3D sedimentary information volume based on pre-stack seismic data.
[0067] Generate 3D sedimentary information volume 3 based on pre-stack seismic data.
[0068] like Figure 5 The following is a stratigraphic slice of the S1 layer obtained using traditional methods based on post-stack seismic data. Figure 6 This is a stratigraphic slice of the S1 layer obtained based on the method of predicting stratigraphic sedimentary bodies based on prestack seismic data in the embodiment of this application. Comparing the two slices, it can be seen that both methods reflect the development of northeast-southwest river channels in the study area and can reflect the distribution pattern of large river channels, but the method of predicting stratigraphic sedimentary bodies based on prestack seismic data in the embodiment of this application obtains Figure 6 It is also obvious that a number of small rivers with northwest-southeast orientation are developed in the northwest and east of the study area, and the distribution of the rivers is clear. Figure 5 The small river channels in the northwest and east of the study area are not clearly reflected, which shows that the method of predicting stratigraphic sedimentary bodies based on pre-stack seismic data in the embodiment of the present application is obviously clearer in depicting sedimentary phenomena than the traditional method based on post-stack seismic data.
[0069] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.
Claims
1. A method for predicting stratigraphic sedimentary bodies based on prestack seismic data, characterized in that: The method comprises: Generating a stratigraphic time model based on post-stack seismic data from the study area; determining a target layer according to the stratigraphic time model; The principal component analysis method is used to extract the sedimentary information of the target layer from the prestack seismic data of the study area.
2. The method for predicting stratum sedimentary bodies based on prestack seismic data according to claim 1, characterized in that: The generating of the formation time model based on the post-stack seismic data of the study area includes: Establish the corresponding relationship between reflection time and formation depth of post-stack seismic data; Determine multiple reference layers based on the geological information of the study area; A formation time model is established according to the established corresponding relationship and the reference layer.
3. The method for predicting stratum sedimentary bodies based on prestack seismic data according to claim 2, characterized in that: The reference layer is an isochronous marker seismic event.
4. The method for predicting stratum sedimentary bodies based on prestack seismic data according to claim 2, characterized in that: The step of establishing a formation time model based on the established corresponding relationship and the reference layer includes: Determining a corresponding relationship between a first geological horizon and the time of the post-stack seismic data and a corresponding relationship between a second geological horizon and the time of the post-stack seismic data; According to the pre-set sampling interval of the stratigraphic slice data volume, the average distance or maximum distance between each two reference layers is calculated respectively; Calculate the number of interpolated stratigraphic slices between every two reference layers according to the average distance or maximum distance between every two reference layers and the sampling interval; A formation time model is established according to the number of interpolated formation slices and by using a linear interpolation function between every two reference layers.
5. The method for predicting stratum sedimentary bodies based on prestack seismic data according to claim 1, characterized in that: The principal component analysis method is used to extract the sedimentary information of the target layer from the pre-stack seismic data of the study area, including: The position information of the target layer in three-dimensional space is obtained by tracking the seismic reflection corresponding to the target layer; For each sampling point in the seismic data, the characteristic subspace of the seismic data is extracted by the principal component analysis method; Obtain the feature map of the sampling point according to the feature subspace; The sedimentary information of the target layer based on pre-stack seismic data is obtained through the characteristic map of each sampling point in the three-dimensional space of the target layer.
6. The method for predicting stratum sedimentary bodies based on prestack seismic data according to claim 4, characterized in that: The extracting the characteristic subspace of the seismic data by using a principal component analysis method for each sampling point in the seismic data includes: The dimensionality reduction of the 2D seismic data of each sampling point is performed using principal component analysis; Determine the feature subspace corresponding to the minimum error function.
7. The method for predicting stratum sedimentary bodies based on pre-stack seismic data according to claim 6, characterized in that: The error function is: In the above formula, X is the characteristic map of the sampling points, that is, the n*m-dimensional seismic data matrix, n represents the number of sampling points for each seismic data, and m represents the number of pre-stack seismic data; x i is the column vector of X; W is the feature subspace, V is the projection coefficient of the original data on the new feature subspace, Column vector representing WV.
8. The method for predicting stratum sedimentary bodies based on pre-stack seismic data according to claim 5, characterized in that: The characteristic graph of the sampling point is: Among them, X is the characteristic map of the sampling point, the n*m dimensional seismic data matrix, the column vector of V is the principal component, the first column is the first principal component, the second column is the second principal component, and r is the total number of principal components; U and V are orthogonal matrices, σ i is the singular value on the diagonal of the D diagonal matrix, u i is the column vector of the U matrix, V T Column vector of the matrix.
9. A device for predicting stratum sediments based on pre-stack seismic data, characterized in that: The device includes: a memory and a processor; the memory is used to store a program for predicting stratigraphic sedimentary bodies based on pre-stack seismic data, and the processor is used to read and execute the program for predicting stratigraphic sedimentary bodies based on pre-stack seismic data, and execute the method according to one of claims 1-8.
10. A computer-readable storage medium having a data processing program stored thereon, wherein the data processing program is executed by a processor to implement the method for predicting stratigraphic sedimentary bodies based on pre-stack seismic data according to any one of claims 1 to 8.