Multi-azimuth post-stack seismic attribute interpretation method, device, medium and equipment

Abstract

The application discloses a multi-azimuth post-stack seismic attribute interpretation method, device, medium and equipment, and the method comprises the following steps: acquiring a target layer seismic interpretation horizon and a plurality of azimuth seismic attribute data bodies; setting a window used for calculating eigenvalues; selecting an eigenvalue; for the selected eigenvalue, in the window, based on the plurality of azimuth seismic attribute data bodies, attribute optimal eigenvalues of each position point in the plurality of azimuth seismic attribute data body spaces are obtained; based on the attribute optimal eigenvalues of each position point, an eigenvalue data body is obtained; and an attribute plan view is extracted in the eigenvalue data body along the target layer seismic interpretation horizon. The application acquires the most representative attribute advantage eigenvalues in different azimuths, and constructs a new data body from the eigenvalues, so that the purpose of highlighting the geological abnormal response characteristics and reducing the dimension interpretation is achieved, and the efficiency and precision of the post-stack multi-azimuth seismic interpretation are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of Earth Science and Seismic Exploration, and specifically relates to a method, apparatus, medium and electronic equipment for multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation. Background Technology

[0002] In recent years, the workload of wide-azimuth 3D seismic exploration has been increasing. Because wide-azimuth seismic acquisition can receive more complete seismic wavefield signals, especially wavefield signals from more azimuths, it can provide richer information for underground stratigraphic structure, fault interpretation and sedimentation, lithology and reservoir fluid identification and prediction.

[0003] Different seismic processing imaging methods can be used to obtain source-receiver offset vector sheet (OVT) gathers or angle-domain common imaging gathers (ADCIG) from seismic data acquired in wide azimuth. Unlike conventional seismic processing imaging results, both OVT-domain processing and angle-domain imaging preserve the azimuth information of seismic reflections in the resulting seismic gathers. Therefore, stacked data volumes and their attribute data volumes in different azimuths can be calculated from these gather data. These data volumes increase the effective information for seismic interpretation, but also increase the azimuth dimension and workload of seismic interpretation, reducing efficiency. Figure 1 As shown, taking four directions as an example, a1, a2, a3 and a4 are the characteristic values ​​or attribute values ​​of each direction.

[0004] Based on wide-azimuth OVT domain seismic processing imaging results, a so-called "five-dimensional (i.e., spatial three-dimensional coordinates XYZ + shot-receiver distance + azimuth angle) seismic interpretation" technique has been basically formed. Its core is based on the fundamental theory of seismic anisotropy. It utilizes the azimuth anisotropy information of wide-azimuth seismic data to analyze the azimuthal differences in seismic properties such as travel time, velocity, amplitude, frequency, and attenuation of seismic wavefields propagating in different azimuths of the subsurface medium. This allows for the identification and interpretation of geological phenomena corresponding to the anisotropic characteristics of the strata, improving the accuracy of seismic interpretation. Furthermore, geological analysis is performed using stacked data volumes from different azimuths. This allows for multi-perspective analysis of the structure, properties, and reservoir characteristics of the same geological body from different azimuths. The interpretation results from different azimuths are then fused to optimize the interpretation scheme (Bai Chenyang, 2015). In addition, by performing appropriate mathematical operations on seismic data volumes from different azimuths, seismic attribute data volumes from multiple azimuths can be obtained. After normalization, addition, subtraction, multiplication and other operations can be performed on the normalized attribute data volumes from different azimuths to obtain combined attributes, differential attributes and product attributes (Zhang Junhua, 2007), so as to highlight more detailed information about the geological bodies.

[0005] Clearly, for multi-directional seismic data volumes, existing technical methods, such as combining different directional attributes and performing simple arithmetic operations, do not truly extract the most representative and advantageous directional feature values ​​in each directional area, thus failing to achieve the goal of enhancing the differences in characteristics between different directional areas.

[0006] Therefore, it is necessary to study and propose a more effective method to select and extract the most advantageous feature values ​​from multiple azimuth seismic attribute data volumes, form an optimized enhanced attribute data volume, maximize the highlighting of geological anomalies, and improve interpretation accuracy. Summary of the Invention

[0007] The purpose of this invention is to propose a multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation method for extracting the most advantageous feature values ​​from multiple azimuth seismic attribute data volumes.

[0008] This invention provides a method for multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation, comprising: acquiring the seismic interpretation horizon of the target layer and seismic attribute data volumes of multiple azimuths; setting a window for obtaining feature values; selecting a feature value; for the selected feature value, within the window, based on the seismic attribute data volumes of multiple azimuths, obtaining the attribute optimization feature value of each location point in the space of the seismic attribute data volumes of multiple azimuths; obtaining the feature value data volume based on the attribute optimization feature value of each location point; and extracting an attribute plane map along the seismic interpretation horizon of the target layer from the feature value data volume.

[0009] Optionally, the feature values ​​include the maximum value, minimum value, or root square magnitude.

[0010] Optionally, when the selected feature value is the maximum value, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0011] Aimax = Max(A i1 A i2 A iN )

[0012] Where Aimax is the preferred feature value of the maximum value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0013] Optionally, when the selected feature value is the minimum value, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0014] Aimin=Min(A i1 Ai2 A iN )

[0015] Where Aimin is the minimum preferred feature value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0016] Optionally, when the selected feature value is the root mean square amplitude, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained using the following formula:

[0017]

[0018] Among them, A irms To optimize the eigenvalue of the root mean square magnitude at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0019] Optionally, the seismic attribute data volumes in multiple azimuths are obtained through input or calculation. The seismic attribute data volumes in multiple azimuths are calculated through the following steps: obtaining time-offset superimposed seismic data volumes or depth-offset superimposed seismic data volumes in multiple azimuths; and calculating the seismic attribute data volumes in multiple azimuths based on the time-offset superimposed seismic data volumes or depth-offset superimposed seismic data volumes.

[0020] Optionally, when the seismic attribute data volumes of multiple azimuths are calculated and obtained based on the time-migrated stacked seismic data volumes, the window is a time window; when the seismic attribute data volumes of multiple azimuths are calculated and obtained based on the depth-migrated stacked seismic data volumes, the window is a depth window.

[0021] The present invention also provides an electronic device, the electronic device comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the above-described method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes.

[0022] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes.

[0023] This invention also provides a multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation device, comprising: an acquisition module for acquiring the seismic interpretation horizon of the target layer and seismic attribute data volumes of multiple azimuths; a setting module for setting a window for obtaining feature values; a feature value data volume forming module for selecting a feature value, and for the selected feature value, within the window, obtaining the attribute optimization feature value of each location point in the seismic attribute data volume space of multiple azimuths based on the attribute optimization feature value of each location point, and obtaining a feature value data volume based on the attribute optimization feature value of each location point; and an extraction module for extracting an attribute plane map along the seismic interpretation horizon of the target layer from the feature value data volume.

[0024] Optionally, the feature values ​​include the maximum value, minimum value, or root square magnitude.

[0025] Optionally, when the selected feature value is the maximum value, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0026] Aimax = Max(A i1 A i2 A iN )

[0027] Where Aimax is the preferred feature value of the maximum value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0028] Optionally, when the selected feature value is the minimum value, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0029] Aimin=Min(A i1 A i2 A iN )

[0030] Where Aimin is the minimum preferred feature value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0031] Optionally, when the selected feature value is the root mean square amplitude, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained using the following formula:

[0032]

[0033] Among them, A irms To optimize the eigenvalue of the root mean square magnitude at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0034] Optionally, the seismic attribute data volumes in multiple azimuths are obtained through input or calculation. The seismic attribute data volumes in multiple azimuths are calculated through the following steps: obtaining time-offset superimposed seismic data volumes or depth-offset superimposed seismic data volumes in multiple azimuths; and calculating the seismic attribute data volumes in multiple azimuths based on the time-offset superimposed seismic data volumes or depth-offset superimposed seismic data volumes.

[0035] Optionally, when the seismic attribute data volumes of multiple azimuths are calculated and obtained based on the time-migrated stacked seismic data volumes, the window is a time window; when the seismic attribute data volumes of multiple azimuths are calculated and obtained based on the depth-migrated stacked seismic data volumes, the window is a depth window.

[0036] The beneficial effects of this invention are as follows: The multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation method of this invention obtains the most representative attribute advantage feature values ​​in different azimuths, and forms an optimized enhanced attribute data volume from these feature values, thereby achieving the purpose of highlighting geological anomaly response characteristics and dimensionality reduction interpretation, effectively improving the efficiency and accuracy of post-stack multi-azimuth seismic interpretation. Since the most representative attribute feature values ​​are retained and non-significant fuzzy feature values ​​are discarded, it has obvious technical advantages compared with the current conventional azimuth-based seismic interpretation technology, filling the gap in the current industry of OVT domain (wide azimuth vector offset) seismic data imaging processing results—multi-azimuth post-stack seismic attribute analysis and interpretation technology.

[0037] The present invention has other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0038] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the invention.

[0039] Figure 1A schematic diagram of the orientation division of the superimposed data volume is shown.

[0040] Figure 2 A flowchart of a method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes according to an embodiment of the present invention is shown.

[0041] Figure 3 A three-dimensional seismic azimuth coherence profile of a region in the Tarim Basin is shown, illustrating a method for optimal dimensionality reduction interpretation of multi-azimuth post-stack seismic attributes according to an embodiment of the present invention.

[0042] Figure 4 This paper illustrates a coherence profile of selected eigenvalues ​​from six azimuth coherent data volumes, according to an embodiment of the present invention, for a multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation method.

[0043] Figure 5 The diagram shows a 3D seismic coherence plane map of a region in the Tarim Basin, illustrating a multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation method according to an embodiment of the present invention, with six directional coherence planes.

[0044] Figure 6 The diagram illustrates a coherence plane plot extracted from selected feature values ​​in a multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation method according to an embodiment of the present invention.

[0045] Figure 7 A structural block diagram of a multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation device according to an embodiment of the present invention is shown.

[0046] Explanation of reference numerals in the attached figures

[0047] 102. Acquisition Module; 104. Setting Module; 106. Feature Value Data Volume Formation Module; 108. Extraction Module. Detailed Implementation

[0048] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0049] This invention provides a method for multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation, comprising: acquiring the seismic interpretation horizon of the target layer and seismic attribute data volumes of multiple azimuths; setting a window for obtaining feature values; selecting a feature value; for the selected feature value, within the window, based on the seismic attribute data volumes of multiple azimuths, obtaining the attribute optimization feature value of each location point in the space of the seismic attribute data volumes of multiple azimuths; obtaining the feature value data volume based on the attribute optimization feature value of each location point; and extracting an attribute plane map from the feature value data volume along the seismic interpretation horizon of the target layer.

[0050] Specifically, the process involves acquiring seismic attribute data volumes from multiple azimuths, inputting the seismic interpretation horizon of the target layer, setting the window used for obtaining the characteristic values ​​of the data volume, acquiring the preferred characteristic values ​​of the attributes at each location point in the space of the seismic attribute data volumes from multiple azimuths for a selected characteristic value, constructing a eigenvalue data volume based on the preferred characteristic values ​​of all location points, and extracting the attribute plane map from the eigenvalue data volume along the seismic interpretation horizon of the target layer.

[0051] According to an exemplary implementation, the multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation method obtains the most representative attribute advantage feature values ​​in different azimuths, and constructs a new data volume from these feature values, thereby highlighting the geological anomaly response characteristics and achieving dimensionality reduction interpretation. This effectively improves the efficiency and accuracy of post-stack multi-azimuth seismic interpretation. Because it retains the most representative attribute feature values ​​and discards insignificant fuzzy feature values, it has significant technical advantages compared with the current conventional azimuth-based seismic interpretation techniques, filling the gap in the current industry of multi-azimuth post-stack seismic attribute analysis and interpretation technology for OVT domain (wide azimuth vector offset) seismic data imaging processing results.

[0052] As an option, eigenvalues ​​include the maximum value, minimum value, or root square magnitude.

[0053] Specifically, the feature values ​​include the maximum value, minimum value, or root mean square magnitude. Depending on the type of feature value selected, the corresponding feature value extraction method strategy type can be selected to obtain the maximum value attribute data body, minimum value attribute data body, or root mean square magnitude attribute data body, respectively.

[0054] As an alternative, when the selected feature value is the maximum value, the preferred feature value of the attribute at each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0055] Aimax = Max(A i1 A i2 A iN )

[0056] Where Aimax is the preferred feature value of the maximum value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0057] Specifically, for a given location point, the maximum values ​​of the attributes in N directions at that location point are calculated and used as the preferred attribute feature values.

[0058] As an alternative, when the selected feature value is the minimum, the preferred feature value of the attribute at each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0059] Aimin=Min(A i1 A i2 A iN )

[0060] Where Aimin is the minimum preferred feature value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0061] Specifically, for a given location point, the minimum value of the attribute in N directions at that location point is calculated and used as the preferred attribute feature value.

[0062] As an alternative, when the selected feature value is the root mean square amplitude, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0063]

[0064] Among them, A irms To optimize the eigenvalue of the root mean square magnitude at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0065] Specifically, for a given location point, the root mean square magnitudes of N directions at that location point are calculated and used as the preferred feature values ​​for the attribute.

[0066] As an optional approach, seismic attribute data volumes in multiple azimuths can be obtained through input or calculation. The seismic attribute data volumes in multiple azimuths can be calculated by the following steps: obtaining time-migrated or depth-migrated superimposed seismic data volumes in multiple azimuths; and calculating the seismic attribute data volumes in multiple azimuths based on the time-migrated or depth-migrated superimposed seismic data volumes.

[0067] Specifically, you can directly input earthquake attribute data volumes for N azimuths, or you can input time-offset superimposed earthquake data volumes or depth-offset superimposed earthquake data volumes for N azimuths, and calculate the earthquake attribute data volumes based on the time-offset superimposed earthquake data volumes or depth-offset superimposed earthquake data volumes for N azimuths respectively.

[0068] As an optional solution, when the seismic attribute data volume of multiple azimuths is calculated and obtained based on the time-migrated stacked seismic data volume, the window is a time window; when the seismic attribute data volume of multiple azimuths is calculated and obtained based on the depth-migrated stacked seismic data volume, the window is a depth window.

[0069] Specifically, when the seismic attribute data volume is obtained by superimposing seismic data volumes with time migration, the window for obtaining feature values ​​is set to the time window; when the seismic attribute data volume is obtained by superimposing seismic data volumes with depth migration, the window for obtaining feature values ​​is set to the depth window. When directly inputting the seismic attribute data volume, the window set can be either the time window or the depth window.

[0070] The present invention also provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the above-mentioned method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes.

[0071] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes.

[0072] This invention also provides a multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation device, comprising: an acquisition module for acquiring the seismic interpretation horizon of the target layer and seismic attribute data volumes of multiple azimuths; a setting module for setting the window used to obtain feature values; a feature value data volume forming module for selecting a feature value, and for the selected feature value, within the window, based on the seismic attribute data volumes of multiple azimuths, obtaining the attribute optimization feature value of each location point in the space of the seismic attribute data volumes of multiple azimuths, and obtaining the feature value data volume based on the attribute optimization feature value of each location point; and an extraction module for extracting attribute plane maps along the seismic interpretation horizon of the target layer from the feature value data volume.

[0073] Specifically, the process involves acquiring seismic attribute data volumes from multiple azimuths, inputting the seismic interpretation horizon of the target layer, setting the window used for obtaining the characteristic values ​​of the data volume, acquiring the preferred characteristic values ​​of the attributes at each location point in the space of the seismic attribute data volumes from multiple azimuths for a selected characteristic value, constructing a eigenvalue data volume based on the preferred characteristic values ​​of all location points, and extracting the attribute plane map from the eigenvalue data volume along the seismic interpretation horizon of the target layer.

[0074] According to an exemplary implementation, the multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation device acquires the most representative attribute advantage feature values ​​in different azimuths, and constructs a new data volume from these feature values, thereby highlighting the geological anomaly response characteristics and achieving dimensionality reduction interpretation. This effectively improves the efficiency and accuracy of post-stack multi-azimuth seismic interpretation. Because it retains the most representative attribute feature values ​​and discards insignificant fuzzy feature values, it has significant technical advantages compared with the current conventional azimuth-based seismic interpretation technology, filling the gap in the current industry of multi-azimuth post-stack seismic attribute analysis and interpretation technology for OVT domain (wide azimuth vector offset) seismic data imaging processing results.

[0075] As an option, eigenvalues ​​include the maximum value, minimum value, or root square magnitude.

[0076] Specifically, the feature values ​​include the maximum value, minimum value, or root mean square magnitude. Depending on the type of feature value selected, the corresponding feature value extraction method strategy type can be selected to obtain the maximum value attribute data body, minimum value attribute data body, or root mean square magnitude attribute data body, respectively.

[0077] As an alternative, when the selected feature value is the maximum value, the preferred feature value of the attribute at each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0078] Aimax = Max(A i1 A i2 A iN )

[0079] Where Aimax is the preferred feature value of the maximum value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0080] Specifically, for a given location point, the maximum values ​​of the attributes in N directions at that location point are calculated and used as the preferred attribute feature values.

[0081] As an alternative, when the selected feature value is the minimum, the preferred feature value of the attribute at each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0082] Aimin=Min(A i1 A i2 A iN )

[0083] Where Aimin is the minimum preferred feature value at the i-th position, A i1Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0084] Specifically, for a given location point, the minimum value of the attribute in N directions at that location point is calculated and used as the preferred attribute feature value.

[0085] As an alternative, when the selected feature value is the root mean square amplitude, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0086]

[0087] Among them, A irms To optimize the eigenvalue of the root mean square magnitude at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0088] Specifically, for a given location point, the root mean square magnitudes of N directions at that location point are calculated and used as the preferred feature values ​​for the attribute.

[0089] As an optional approach, seismic attribute data volumes in multiple azimuths can be obtained through input or calculation. The seismic attribute data volumes in multiple azimuths can be calculated by the following steps: obtaining time-migrated or depth-migrated superimposed seismic data volumes in multiple azimuths; and calculating the seismic attribute data volumes in multiple azimuths based on the time-migrated or depth-migrated superimposed seismic data volumes.

[0090] Specifically, you can directly input earthquake attribute data volumes for N azimuths, or you can input time-offset superimposed earthquake data volumes or depth-offset superimposed earthquake data volumes for N azimuths, and calculate the earthquake attribute data volumes based on the time-offset superimposed earthquake data volumes or depth-offset superimposed earthquake data volumes for N azimuths respectively.

[0091] As an optional solution, when the seismic attribute data volume of multiple azimuths is calculated and obtained based on the time-migrated stacked seismic data volume, the window is a time window; when the seismic attribute data volume of multiple azimuths is calculated and obtained based on the depth-migrated stacked seismic data volume, the window is a depth window.

[0092] Specifically, when the seismic attribute data volume is obtained by superimposing seismic data volumes with time migration, the window for obtaining feature values ​​is set to the time window; when the seismic attribute data volume is obtained by superimposing seismic data volumes with depth migration, the window for obtaining feature values ​​is set to the depth window. When directly inputting the seismic attribute data volume, the window set can be either the time window or the depth window.

[0093] Example 1

[0094] Figure 2 A flowchart of a method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes according to an embodiment of the present invention is shown. Figure 3 A three-dimensional seismic azimuth coherence profile of a region in the Tarim Basin is shown, illustrating a method for optimal dimensionality reduction interpretation of multi-azimuth post-stack seismic attributes according to an embodiment of the present invention. Figure 4 This paper illustrates a coherence profile of selected eigenvalues ​​from six azimuth coherent data volumes, according to an embodiment of the present invention, for a multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation method. Figure 5 The diagram shows a 3D seismic coherence plane map of a region in the Tarim Basin, illustrating a multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation method according to an embodiment of the present invention, with six directional coherence planes. Figure 6 The diagram illustrates a coherence plane plot extracted from selected feature values ​​in a multi-azimuth post-stack seismic attribute optimization and dimensionality reduction interpretation method according to an embodiment of the present invention.

[0095] Combination Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown, this multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation method includes:

[0096] Step 1: Obtain the seismic interpretation horizon and seismic attribute data volume of multiple orientations for the target layer;

[0097] Step 2: Set the window used to obtain eigenvalues;

[0098] Step 3: Select a feature value. For the selected feature value, within the window, based on the seismic attribute data volume of multiple azimuths, obtain the preferred feature value of each location point in the seismic attribute data volume space of multiple azimuths. Based on the preferred feature value of each location point, obtain the feature value data volume.

[0099] Step 4: Extract the attribute plane map from the eigenvalue data volume along the seismic interpretation horizon of the target layer.

[0100] Among them, the eigenvalues ​​include the maximum value, minimum value, or root magnitude.

[0101] When the selected feature value is the maximum value, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0102] Aimax = Max(A i1 A i2 A iN )

[0103] Where Aimax is the preferred feature value of the maximum value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0104] When the selected feature value is the minimum value, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0105] Aimin=Min(A i1 A i2 A iN )

[0106] Where Aimin is the minimum preferred feature value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0107] When the selected feature value is the root mean square amplitude, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0108]

[0109] Among them, A irms To optimize the eigenvalue of the root mean square magnitude at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0110] Among them, seismic attribute data volumes in multiple azimuths are obtained through input or calculation. The seismic attribute data volumes in multiple azimuths are obtained by calculation through the following steps: obtaining time-migration superimposed seismic data volumes or depth-migration superimposed seismic data volumes in multiple azimuths; and calculating the seismic attribute data volumes in multiple azimuths based on the time-migration superimposed seismic data volumes or depth-migration superimposed seismic data volumes.

[0111] Specifically, when the seismic attribute data volume from multiple azimuths is calculated and obtained based on time-migrated stacked seismic data volume, the window is a time window; when the seismic attribute data volume from multiple azimuths is calculated and obtained based on depth-migrated stacked seismic data volume, the window is a depth window.

[0112] To further verify the practical effect of the present invention, the coherence attribute planar map extracted from the top surface (T74) of the Yijianfang Formation of the Ordovician System by the method of the present invention was compared and analyzed. Figure 3 This indicates that the fracture characteristics differ in each orientation, and no single coherent profile can reflect the fracture characteristics in all orientations. (Attached) Figure 4 This indicates that the profile incorporates all the advantageous features of coherence in six directions, significantly enhancing its fracture characterization capability. (Appendix) Figure 5 This is a coherence property planar diagram along the T74 plane in six orientations. The diagram shows that the fracture characteristics differ in each orientation, and no single coherence plane can fully represent the fractures in all orientations. (Attached) Figure 6 This is a coherence property plane map along the T74 layer extracted from the maximum value data volume obtained using the method of this invention. This plane map integrates all the advantageous features of coherence in six directions, greatly enhancing the fracture characterization capability. Comparison shows that the coherence plane map along the layer extracted from the minimum value data volume obtained by this invention possesses the fracture features of the other six directional coherence plane maps, and its overall fracture features are clearer than any one of them.

[0113] Example 2

[0114] Figure 7 A structural block diagram of a multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation device according to an embodiment of the present invention is shown.

[0115] like Figure 7 As shown, the multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation device includes:

[0116] Module 102 acquires the seismic interpretation horizon of the target layer and the seismic attribute data volume in multiple orientations;

[0117] Module 104 is configured to set the window used for obtaining eigenvalues;

[0118] The feature value data volume forming module 106 selects a feature value, and for the selected feature value, within the window, based on the seismic attribute data volumes of multiple azimuths, obtains the preferred feature value of each location point in the seismic attribute data volume space of multiple azimuths, and obtains the feature value data volume based on the preferred feature value of each location point;

[0119] Extraction module 108 extracts attribute plane maps from the eigenvalue data volume along the seismic interpretation horizon of the target layer.

[0120] Among them, the eigenvalues ​​include the maximum value, minimum value, or root magnitude.

[0121] When the selected feature value is the maximum value, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0122] Aimax = Max(A i1 A i2 A iN )

[0123] Where Aimax is the preferred feature value of the maximum value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0124] When the selected feature value is the minimum value, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0125] Aimin=Min(A i1 A i2 A iN )

[0126] Where Aimin is the minimum preferred feature value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0127] When the selected feature value is the root mean square amplitude, the preferred feature value of the attribute for each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula:

[0128]

[0129] Among them, A irmsTo optimize the eigenvalue of the root mean square magnitude at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

[0130] Among them, seismic attribute data volumes in multiple azimuths are obtained through input or calculation. The seismic attribute data volumes in multiple azimuths are obtained by calculation through the following steps: obtaining time-migration superimposed seismic data volumes or depth-migration superimposed seismic data volumes in multiple azimuths; and calculating the seismic attribute data volumes in multiple azimuths based on the time-migration superimposed seismic data volumes or depth-migration superimposed seismic data volumes.

[0131] Specifically, when the seismic attribute data volume from multiple azimuths is calculated and obtained based on time-migrated stacked seismic data volume, the window is a time window; when the seismic attribute data volume from multiple azimuths is calculated and obtained based on depth-migrated stacked seismic data volume, the window is a depth window.

[0132] Example 3

[0133] This disclosure provides an electronic device comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the aforementioned multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation method.

[0134] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0135] 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.

[0136] 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 of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0137] 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 communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0138] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0139] Example 4

[0140] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes.

[0141] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0142] 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).

[0143] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they 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 method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes, characterized in that, include: Obtain the seismic interpretation horizon and seismic attribute data volume in multiple orientations of the target layer; Set the window used to obtain eigenvalues; Select a feature value, and for the selected feature value, within the window, based on the seismic attribute data volumes of multiple azimuths, obtain the preferred feature value of each location point in the space of the seismic attribute data volumes of multiple azimuths, and obtain the feature value data volume based on the preferred feature value of each location point; The characteristic values ​​include the maximum value, minimum value, or root mean square magnitude. The attribute plane map is extracted from the eigenvalue data volume along the seismic interpretation horizon of the target layer.

2. The method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes according to claim 1, characterized in that, When the selected feature value is the maximum value, the optimal feature value of the attribute at each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula: Aimax=Max(A i1 ,A i2 ,…,A iN ) Where Aimax is the preferred feature value of the maximum value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

3. The method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes according to claim 1, characterized in that, When the selected feature value is the minimum value, the optimal feature value of the attribute at each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula: Aimin=Min(A i1 ,IN i2 ,…,IN iN ) Where Aimin is the minimum preferred feature value at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

4. The method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes according to claim 1, characterized in that, When the selected feature value is the root mean square amplitude, the optimal feature value of the attribute at each location point in the volume space of multiple azimuth seismic attribute data is obtained by the following formula: ； in, To select the optimal eigenvalue for the root mean square magnitude at the i-th position, A i1 Let A be the attribute magnitude at the i-th position and the first orientation. i2 Let A be the attribute magnitude at the i-th position and the second orientation. iN Let be the attribute magnitude at the i-th position and the N-th orientation.

5. The method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes according to claim 1, characterized in that, The seismic attribute data volumes from multiple azimuths are obtained through input or calculation. The seismic attribute data volumes from multiple azimuths are calculated through the following steps: Acquire time-migrated or depth-migrated stacked seismic data volumes from multiple azimuths; Based on the time-migrated or depth-migrated seismic data volume, seismic attribute data volumes in multiple azimuths are calculated.

6. The method for optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes according to claim 5, characterized in that, When the seismic attribute data volumes of the multiple azimuths are calculated and obtained based on the time-migrated stacked seismic data volumes, the window is a time window; when the seismic attribute data volumes of the multiple azimuths are calculated and obtained based on the depth-migrated stacked seismic data volumes, the window is a depth window.

7. 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 multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation method according to any one of claims 1-6.

8. 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 optimal dimensionality reduction interpretation of multi-directional post-stack seismic attributes according to any one of claims 1-6.

9. A multi-directional post-stack seismic attribute optimization and dimensionality reduction interpretation device, characterized in that, include: The acquisition module acquires the seismic interpretation horizon of the target layer and the seismic attribute data volume in multiple orientations; The settings module defines the window used to obtain feature values. The feature value data volume forming module selects a feature value, and for the selected feature value, within the window, obtains the preferred feature value of each location point in the seismic attribute data volume space based on the seismic attribute data volume of multiple azimuths, and obtains the feature value data volume based on the preferred feature value of each location point; The characteristic values ​​include the maximum value, minimum value, or root mean square magnitude. The extraction module extracts attribute plane maps from the eigenvalue data volume along the seismic interpretation horizon of the target layer.

Images