A crack prediction method based on normal orientation wheeler domain structure class attribute PCA fusion

Abstract

The application relates to a crack prediction method based on normal azimuth Wheeler domain structure attribute PCA fusion, and the method is characterized in that: prestack wide-azimuth seismic trace set data of a to-be-detected buried hill crack reservoir are divided into prestack seismic trace set data of different azimuths, data stacking processing and optimization are respectively performed, a normal illumination azimuth seismic data body is formed, and then a normal illumination azimuth frequency division seismic data body is obtained; the normal illumination azimuth frequency division seismic data body is flattened to a Wheeler domain to obtain a Wheeler domain normal illumination azimuth frequency division seismic data body, and an attribute plane of a structure attribute body of the Wheeler domain normal illumination azimuth frequency division seismic data body is extracted along a buried hill top surface interpretation horizon; the attribute plane of the structure attribute body is subjected to fusion processing to obtain a normal illumination azimuth Wheeler domain structure attribute PCA fusion result, which is used as a plane distribution prediction result of the to-be-detected buried hill crack reservoir, and the application can be widely applied to the technical field of oil and gas field development.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a fracture prediction method based on PCA (principal component analysis) fusion of structural attributes in the normal orientation Wheeler domain (relative geological time domain). Background Technology

[0002] The seismic response of buried hill fractured reservoirs is complex, and the deep burial of buried hills generally affects the imaging quality of seismic data. Buried hill fractured reservoirs often exhibit chaotic reflections with extremely poor continuity on seismic profiles.

[0003] Against this backdrop, commonly used pre-stack anisotropic fractured reservoir prediction methods based on wide-azimuth seismic data generally perform poorly, while traditional post-stack coherent bodies, ant bodies, and other structural fault detection methods are also unable to effectively characterize the planar distribution of buried hill fractured reservoirs. Summary of the Invention

[0004] To address the aforementioned problems, the purpose of this invention is to provide a fracture prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes, which can effectively characterize the planar distribution of fractured reservoirs in buried hills.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, it provides a crack prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes, comprising:

[0006] The pre-stack wide-azimuth seismic gather data of the buried hill fractured reservoir to be tested are divided into pre-stack seismic gather data of different azimuths, and the data are stacked and optimized separately to form a normal illumination azimuth seismic data volume, and then the normal illumination azimuth frequency-divided seismic data volume is obtained.

[0007] The normal illumination azimuth frequency-division seismic data volume is flattened to the Wheeler domain to obtain the Wheeler domain normal illumination azimuth frequency-division seismic data volume, and the attribute plane of the structural class attribute volume of the Wheeler domain normal illumination azimuth frequency-division seismic data volume is extracted along the interpretation horizon of the buried mountain top.

[0008] The attribute plane of the structural attribute volume is fused to obtain the PCA fusion result of the normal illumination orientation Wheeler domain structural attribute, which is used as the planar distribution prediction result of the buried hill fracture reservoir to be tested.

[0009] Further, the process of dividing the pre-stack wide-azimuth seismic gather data of the buried hill fractured reservoir under test into pre-stack seismic gather data of different azimuths, and performing data overlay processing and optimization separately to form a normal illumination azimuth seismic data volume, thereby obtaining a normal illumination azimuth frequency-divided seismic data volume, includes:

[0010] According to the symmetrical sector method, the pre-stack wide-azimuth seismic gather data of the buried hill fracture reservoir to be tested are divided into pre-stack seismic gather data of different azimuths.

[0011] Prestack seismic gathers from different azimuths were processed by data stacking and optimization to form a seismic data volume with normal illumination azimuth.

[0012] The normal illumination azimuth seismic data volume is subjected to frequency division processing and optimization to obtain the normal illumination azimuth frequency-divided seismic data volume.

[0013] Furthermore, the process of performing data overlay and optimization on pre-stack seismic gathers from different azimuths to form a normal illumination azimuth seismic data volume includes:

[0014] Seismic stacking processing was performed on pre-stack seismic gathers from different orientations to form seismic data volumes from different orientations.

[0015] Based on the development characteristics of the buried hill fractured reservoir to be tested, the main direction of fracture development is determined and denoted as φ. Then, the normal directions of the main development directions of fractures in the buried hill fractured reservoir to be tested are determined as φ+90 and φ-90.

[0016] From the above different orientations, select the two orientations that are closest to φ+90 and φ-90 respectively, and label them as φ1 and φ2. The seismic data volumes corresponding to the two orientations φ1 and φ2 are the preferred seismic data volumes.

[0017] The selected seismic data volume is averaged, and the average result is the normal illumination azimuth seismic data volume.

[0018] Further, the process of frequency division and optimization of the normal illumination azimuth seismic data volume to obtain a normal illumination azimuth frequency-divided seismic data volume includes:

[0019] The normal illumination azimuth seismic data volume is subjected to frequency division processing to obtain a single-frequency data volume of the normal illumination azimuth seismic data volume;

[0020] The single-frequency data volume of the normal illumination azimuth seismic data volume is optimized and used as the normal illumination azimuth frequency-divided seismic data volume.

[0021] Further, the step of performing frequency division processing on the normal illumination azimuth seismic data volume to obtain a single-frequency data volume of the normal illumination azimuth seismic data volume includes:

[0022] Spectral analysis was performed on the seismic data volume of the normal illumination azimuth, the dominant frequency fa of the data volume was calculated, and the effective frequency band of the data volume was calculated. The lower limit of the effective frequency band is fb.

[0023] Calculate the three frequency values ​​of the normal illumination azimuth seismic data volume:

[0024]

[0025] f2 = fa

[0026] f3 = fa + 1 / 2 * (fa - fb);

[0027] The wavelet decomposition method was used to determine the single-frequency data volumes of the seismic data volumes with frequencies f1, f2, and f3 for the normal illumination azimuth.

[0028] Furthermore, the single-frequency data volume of the normal illumination azimuth seismic data volume is preferably selected as the normal illumination azimuth frequency-divided seismic data volume, including:

[0029] The corresponding difference values ​​are obtained by subtracting the single-frequency data volumes of frequencies f1, f2, and f3 of the normal illumination azimuth seismic data volume from the normal illumination azimuth seismic data volume, respectively.

[0030] The frequency corresponding to the maximum difference is taken as the dominant frequency, and the single-frequency data volume corresponding to this dominant frequency is the normal illumination azimuth frequency-divided seismic data volume.

[0031] Further, the step of flattening the normal illumination azimuth frequency-divided seismic data volume to the Wheeler domain to obtain the Wheeler domain normal illumination azimuth frequency-divided seismic data volume, and extracting the attribute plane of the structural class attribute volume of the Wheeler domain normal illumination azimuth frequency-divided seismic data volume along the interpretation horizon of the buried mountain top, includes:

[0032] By interpreting the stratigraphic horizon using the buried mountain top, the normal illumination azimuth frequency-divided seismic data volume is flattened to the Wheeler domain to obtain the Wheeler domain normal illumination azimuth frequency-divided seismic data volume;

[0033] The three structural attribute volumes of the Wheeler domain normal illumination azimuth frequency-divided seismic data volume are calculated, and the attribute planes of the three structural attribute volumes are extracted along the interpreted horizon of the buried mountain top.

[0034] Secondly, a crack prediction system based on PCA fusion of normal orientation Wheeler domain structural class attributes is provided, including:

[0035] The normal illumination azimuth frequency-divided seismic data volume determination module is used to divide the pre-stack wide azimuth seismic gather data of the buried hill fracture reservoir to be tested into pre-stack seismic gather data of different azimuths, and perform data superposition processing and optimization respectively to form a normal illumination azimuth seismic data volume, and then obtain the normal illumination azimuth frequency-divided seismic data volume.

[0036] The attribute plane determination module is used to flatten the normal illumination azimuth frequency-division seismic data volume to the Wheeler domain, obtain the Wheeler domain normal illumination azimuth frequency-division seismic data volume, and extract the attribute plane of the structural class attribute volume of the Wheeler domain normal illumination azimuth frequency-division seismic data volume along the interpretation horizon of the buried mountain top.

[0037] The fusion processing module is used to fuse the attribute planes of the structural attribute volumes to obtain the PCA fusion results of the structural attribute volumes in the normal illumination orientation Wheeler domain, which are used as the planar distribution prediction results of the buried hill fracture reservoir to be tested.

[0038] Thirdly, a processing device is provided, including computer program instructions, wherein when the computer program instructions are executed by the processing device, they are used to implement the steps corresponding to the crack prediction method based on the fusion of normal orientation Wheeler domain structural class attributes PCA described above.

[0039] Fourthly, a computer-readable storage medium is provided, wherein computer program instructions are stored on the computer-readable storage medium, wherein the computer program instructions, when executed by a processor, are used to implement the steps corresponding to the crack prediction method based on the fusion of normal orientation Wheeler domain structural class attributes PCA described above.

[0040] The present invention has the following advantages due to the adoption of the above technical solutions:

[0041] 1. This invention uses Wheeler domain structure class attributes, which further improves the crack characterization effect compared with traditional post-stack seismic attributes.

[0042] 2. Based on the structural seismic attributes of the normal orientation, this invention uses more comprehensive structural attributes to further improve the crack characterization effect.

[0043] 3. This invention uses PCA fusion results of multiple structural attributes, which significantly improves the crack characterization effect compared with a single attribute.

[0044] In summary, this invention can be widely applied in the field of oil and gas field development technology. Attached Figure Description

[0045] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:

[0046] Figure 1 This is a schematic diagram of a method flow provided in an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the volume profile of the normal illumination azimuth frequency-divided seismic data in the Wheeler domain of the Bohai M gas field, provided in an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of the curvature attribute plane, dip angle attribute plane, and maximum likelihood volume attribute plane of the Bohai M gas field according to an embodiment of the present invention, wherein, Figure 3 (a) is a schematic diagram of the curvature properties of the Bohai M gas field. Figure 3 (b) is a schematic diagram of the dip angle properties of the Bohai M gas field. Figure 3 (c) is a schematic diagram of the maximum likelihood properties of the Bohai M gas field.

[0049] Figure 4 This is a schematic diagram of the PCA fusion result of the normal illumination azimuth of the Bohai M gas field structure class attribute provided in an embodiment of the present invention. Detailed Implementation

[0050] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0051] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0052] Although terms such as first, second, third, etc., may be used in this document to describe multiple elements, components, regions, layers, and / or segments, these elements, components, regions, layers, and / or segments should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or segment from another. Unless the context clearly indicates otherwise, terms such as "first," "second," and other numerical terms used herein do not imply order or sequence. Therefore, the first element, component, region, layer, or segment discussed below may be referred to as the second element, component, region, layer, or segment without departing from the teachings of the exemplary embodiments.

[0053] Since traditional structural fault detection methods such as post-stack coherence bodies and ant bodies cannot effectively characterize the planar distribution of fractured reservoirs in buried hills, the fracture prediction method based on PCA fusion of normal azimuth Wheeler domain structural attributes provided in this embodiment of the invention further improves the fracture characterization effect by using Wheeler domain structural attributes and PCA fusion results of multiple structural attributes.

[0054] Example 1

[0055] like Figure 1 As shown, this embodiment provides a crack prediction method based on PCA fusion of structural class attributes in the normal orientation Wheeler domain, including the following steps:

[0056] 1) According to the symmetrical sector method, the pre-stack wide-azimuth seismic gather data of the buried hill fracture reservoir to be tested are divided into pre-stack seismic gather data of different azimuths.

[0057] Specifically, the pre-stack seismic gather data in different azimuths include pre-stack seismic gather data in azimuths of 0-30°, 30-60°, 60-90°, 90-120°, 120-150°, and 150-180°.

[0058] 2) Pre-stack seismic gathers from different azimuths are overlaid to obtain seismic data volumes from different azimuths, and then optimized to form seismic data volumes with normal illumination azimuths, specifically:

[0059] 2.1) Seismic stacking processing was performed on the pre-stack seismic gathers in six azimuths: 0-30°, 30-60°, 60-90°, 90-120°, 120-150°, and 150-180°, to form seismic data volumes in six azimuths: 0-30°, 30-60°, 60-90°, 90-120°, 120-150°, and 150-180°.

[0060] 2.2) Optimize the seismic data volumes from the above six directions:

[0061] 2.2.1) Based on the development characteristics of the buried hill fractured reservoir to be tested, the main direction of fracture development is determined and denoted as φ. Then, the normal directions of the main development directions of fractures in the buried hill fractured reservoir to be tested are determined as φ+90 and φ-90.

[0062] 2.2.2) From the above six azimuths, namely 0-30°, 30-60°, 60-90°, 90-120°, 120-150° and 150-180°, select the two azimuths that are closest to φ+90 and φ-90 respectively, and label them as φ1 and φ2. The seismic data volumes corresponding to the two azimuths φ1 and φ2 are the preferred seismic data volumes.

[0063] 2.3) The average value of the selected seismic data volume is the normal illumination azimuth seismic data volume.

[0064] Specifically, the average value 'a' of the seismic data volumes in the two azimuths φ1 and φ2 is calculated, and the seismic data volume 'a' is then used as the seismic data volume in the normal illumination azimuth.

[0065] 3) The normal illumination azimuth seismic data volume is frequency-divided to obtain a single-frequency data volume, and then optimized to obtain a frequency-divided seismic data volume for the normal illumination azimuth. Specifically:

[0066] 3.1) Frequency division processing is performed on the normal illumination azimuth seismic data volume to obtain a single-frequency data volume of the normal illumination azimuth seismic data volume:

[0067] 3.1.1) Perform spectral analysis on the normal illumination azimuth seismic data volume, count the dominant frequency fa of the data volume, and count the effective frequency band of the data volume. The lower limit of the effective frequency band is fb.

[0068] 3.1.2) Calculate the three frequency values ​​of the normal illumination azimuth seismic data volume:

[0069]

[0070] f2 = fa (2)

[0071] f3=fa+1 / 2*(fa-fb) (3)

[0072] 3.1.3) Using the commonly used wavelet decomposition method, the single-frequency data volumes of the normal illumination azimuth seismic data volume, namely f1, f2, and f3, are determined respectively.

[0073] 3.2) The single-frequency data volume of the normal illumination azimuth seismic data volume is optimized and used as the normal illumination azimuth frequency-divided seismic data volume:

[0074] 3.2.1) The single-frequency data volumes of frequencies f1, f2, and f3 of the normal illumination azimuth seismic data volume are respectively subtracted from the normal illumination azimuth seismic data volume to obtain the corresponding difference values ​​e1, e2, and e3.

[0075] 3.2.2) The frequency corresponding to the maximum difference is taken as the dominant frequency, and the single-frequency data volume corresponding to the dominant frequency is the normal illumination azimuth frequency-divided seismic data volume.

[0076] 4) Using the buried mountain top to interpret the horizon, the normal illumination azimuth frequency-divided seismic data volume is flattened to the Wheeler domain (that is, the data is flattened along the interpretation horizon of the buried mountain top), thus obtaining the Wheeler domain normal illumination azimuth frequency-divided seismic data volume.

[0077] 5) Using conventional methods, the curvature volume attribute, dip volume attribute, and maximum likelihood volume attribute of the Wheeler domain normal illumination azimuth frequency-divided seismic data volume are calculated, thus obtaining three types of structural attribute volumes.

[0078] 6) Extract the attribute planes of three structural attribute volumes along the interpretation horizon of the buried mountain top to obtain the curvature attribute plane, dip angle attribute plane and maximum likelihood volume attribute plane.

[0079] 7) The PCA fusion method is used to fuse the attribute planes of the three structural attribute volumes to obtain the PCA fusion result of the normal illumination orientation Wheeler domain structural attribute, which is used as the planar distribution prediction result of the buried hill fracture reservoir to be tested, so as to reflect the planar distribution of the buried hill fracture reservoir to be tested.

[0080] The following example uses the Archean buried hill fractured reservoir in the Bohai M gas field as a specific embodiment to implement the fracture prediction method based on normal orientation Wheeler domain structural attribute PCA fusion of the present invention to predict the buried hill fractured reservoir in the M gas field:

[0081] 1) Based on the symmetrical sector method, the pre-stack wide azimuth seismic gather data of Bohai M gas field are divided into pre-stack seismic gather data in azimuths of 0-30°, 30-60°, 60-90°, 90-120°, 120-150°, and 150-180°.

[0082] 2) Seismic stacking processing was performed on the pre-stack seismic gather data in the above six azimuths to form seismic data volumes in six azimuths: 0-30°, 30-60°, 60-90°, 90-120°, 120-150°, and 150-180°. The 0-30° and 150-180° seismic data volumes were selected from these, and the average of the two azimuth seismic data volumes was calculated. The average result was used as the normal illumination azimuth seismic data volume.

[0083] 3) The normal illumination azimuth seismic data volume is divided into frequency-division processes to form 5Hz, 10Hz and 15Hz single-frequency data volumes of the normal illumination azimuth seismic data volume, and the 10Hz single-frequency data volume is selected as the normal illumination azimuth frequency-division seismic data volume.

[0084] 4) Using the buried hilltop surface to interpret the stratigraphic horizon, the normal illumination azimuth frequency-divided seismic data volume is flattened to the Wheeler domain, resulting in the Wheeler domain normal illumination azimuth frequency-divided seismic data volume, such as... Figure 2 As shown.

[0085] 5) Calculate the curvature volume attribute, dip volume attribute, and maximum likelihood volume attribute of the Wheeler domain normal illumination azimuth frequency-divided seismic data volume as three structural attribute volumes.

[0086] 6) Attribute planes for three structural attribute volumes were extracted along the interpretation horizon of the buried mountain top of the M gas field, resulting in the curvature attribute plane, dip angle attribute plane, and maximum likelihood volume attribute plane, such as... Figure 3 As shown.

[0087] 7) The PCA fusion method is used to fuse the three attribute planes to obtain the PCA fusion result of the normal illumination orientation Wheeler domain structure class attribute, such as... Figure 4 As shown, this is used to reflect the planar distribution of the buried hill fractured reservoir.

[0088] Example 2

[0089] This embodiment provides a crack prediction system based on PCA fusion of structural class attributes in the normal orientation Wheeler domain, including:

[0090] The normal illumination azimuth frequency-divided seismic data volume determination module is used to divide the pre-stack wide-azimuth seismic gather data of the buried hill fracture reservoir to be measured into pre-stack seismic gather data of different azimuths, and perform data superposition processing and optimization respectively to form a normal illumination azimuth seismic data volume, and then obtain the normal illumination azimuth frequency-divided seismic data volume.

[0091] The attribute plane determination module is used to flatten the normal illumination azimuth frequency-divided seismic data volume to the Wheeler domain, obtain the Wheeler domain normal illumination azimuth frequency-divided seismic data volume, and extract the attribute planes of the three structural attribute volumes of the Wheeler domain normal illumination azimuth frequency-divided seismic data volume along the interpretation horizon of the buried mountain top.

[0092] The fusion processing module is used to fuse the attribute planes of the three structural attribute volumes to obtain the PCA fusion result of the normal illumination orientation Wheeler domain structural attribute, which serves as the planar distribution prediction result of the buried hill fracture reservoir to be tested.

[0093] In a preferred embodiment, the normal illumination azimuth frequency-division seismic data volume determination module includes:

[0094] The data partitioning unit is used to divide the pre-stack wide-azimuth seismic gather data of the buried hill fractured reservoir under test into pre-stack seismic gather data of different azimuths according to the symmetrical sector method.

[0095] The data overlay processing unit is used to perform data overlay processing and optimization on pre-stack seismic gathers from different azimuths to form a normal illumination azimuth seismic data volume.

[0096] The frequency division processing unit is used to perform frequency division processing and optimization on the normal illumination azimuth seismic data volume to obtain the normal illumination azimuth frequency-divided seismic data volume.

[0097] In a preferred embodiment, the attribute plane determination module includes:

[0098] Wheeler domain flattening element is used to interpret the stratigraphic level using the buried hill top surface, flattening the normal illumination azimuth frequency-divided seismic data volume to the Wheeler domain, and obtaining the Wheeler domain normal illumination azimuth frequency-divided seismic data volume;

[0099] The structural attribute volume calculation unit is used to calculate the three structural attribute volumes of the Wheeler domain normal illumination azimuth frequency-divided seismic data volume, and extract the attribute planes of the three structural attribute volumes along the interpretation horizon of the buried mountain top.

[0100] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0101] Example 3

[0102] This embodiment provides a processing device corresponding to the crack prediction method based on normal orientation Wheeler domain structural class attribute PCA fusion provided in Embodiment 1. The processing device can be applied to client processing devices, such as mobile phones, laptops, tablets, desktop computers, etc., to execute the method of Embodiment 1.

[0103] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processing device. When the processing device runs the computer program, it executes the crack prediction method based on normal orientation Wheeler domain structural class attribute PCA fusion provided in Embodiment 1.

[0104] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0105] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0106] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] Those skilled in the art will understand that the structure of the above-described computing device is only a partial structure related to the solution of this application and does not constitute a limitation on the computing device to which the solution of this application is applied. A specific computing device may include more or fewer components, or combine certain components, or have different component arrangements.

[0108] Example 4

[0109] This embodiment provides a computer program product corresponding to the crack prediction method based on normal orientation Wheeler domain structural class attribute PCA fusion provided in Embodiment 1. The computer program product may include a computer-readable storage medium on which computer-readable program instructions are loaded for executing the crack prediction method based on normal orientation Wheeler domain structural class attribute PCA fusion described in Embodiment 1.

[0110] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0111] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0112] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] The above embodiments are only used to illustrate the present invention. The structure, connection method and manufacturing process of each component can be varied. All equivalent transformations and improvements made on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A crack prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes, characterized in that, include: The pre-stack wide-azimuth seismic gather data of the buried hill fractured reservoir to be tested are divided into pre-stack seismic gather data of different azimuths, and the data are stacked and optimized separately to form a normal illumination azimuth seismic data volume, and then the normal illumination azimuth frequency-divided seismic data volume is obtained. The normal illumination azimuth frequency-division seismic data volume is flattened to the Wheeler domain to obtain the Wheeler domain normal illumination azimuth frequency-division seismic data volume, and the attribute plane of the structural class attribute volume of the Wheeler domain normal illumination azimuth frequency-division seismic data volume is extracted along the interpretation horizon of the buried mountain top. The attribute plane of the structural attribute volume is fused to obtain the PCA fusion result of the normal illumination orientation Wheeler domain structural attribute, which is used as the planar distribution prediction result of the buried hill fracture reservoir to be tested.

2. The crack prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes as described in claim 1, characterized in that, The process involves dividing the pre-stack wide-azimuth seismic gather data of the buried hill fractured reservoir under test into pre-stack seismic gather data of different azimuths, and performing data overlay processing and optimization separately to form a normal illumination azimuth seismic data volume, thereby obtaining a normal illumination azimuth frequency-divided seismic data volume, including: According to the symmetrical sector method, the pre-stack wide-azimuth seismic gather data of the buried hill fracture reservoir to be tested are divided into pre-stack seismic gather data of different azimuths. Prestack seismic gathers from different azimuths were processed by data stacking and optimization to form a seismic data volume with normal illumination azimuth. The normal illumination azimuth seismic data volume is subjected to frequency division processing and optimization to obtain the normal illumination azimuth frequency-divided seismic data volume.

3. The crack prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes as described in claim 2, characterized in that, The process involves data overlay and optimization of pre-stack seismic gathers from different azimuths to form a normal illumination azimuth seismic data volume, including: Seismic stacking processing was performed on pre-stack seismic gathers from different orientations to form seismic data volumes from different orientations. Based on the development characteristics of the buried hill fractured reservoir to be tested, the main direction of fracture development is determined and denoted as φ. Then, the normal directions of the main development directions of fractures in the buried hill fractured reservoir to be tested are determined as φ+90 and φ-90. From the above different orientations, select the two orientations that are closest to φ+90 and φ-90 respectively, and label them as φ1 and φ2. The seismic data volumes corresponding to the two orientations φ1 and φ2 are the preferred seismic data volumes. The selected seismic data volume is averaged, and the average result is the normal illumination azimuth seismic data volume.

4. The crack prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes as described in claim 2, characterized in that, The process of frequency division and optimization of the normal illumination azimuth seismic data volume to obtain a normal illumination azimuth frequency-divided seismic data volume includes: The normal illumination azimuth seismic data volume is subjected to frequency division processing to obtain a single-frequency data volume of the normal illumination azimuth seismic data volume; The single-frequency data volume of the normal illumination azimuth seismic data volume is optimized and used as the normal illumination azimuth frequency-divided seismic data volume.

5. The crack prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes as described in claim 4, characterized in that, The process of frequency division of the normal illumination azimuth seismic data volume to obtain a single-frequency data volume of the normal illumination azimuth seismic data volume includes: Spectral analysis was performed on the seismic data volume of the normal illumination azimuth, the dominant frequency fa of the data volume was calculated, and the effective frequency band of the data volume was calculated. The lower limit of the effective frequency band is fb. Calculate the three frequency values ​​of the normal illumination azimuth seismic data volume: f2 = fa f3 = fa + 1 / 2 * (fa - fb); The wavelet decomposition method was used to determine the single-frequency data volumes of the seismic data volumes with frequencies f1, f2, and f3 for the normal illumination azimuth.

6. The crack prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes as described in claim 4, characterized in that, The preferred single-frequency data volume of the normal illumination azimuth seismic data volume, as the normal illumination azimuth frequency-divided seismic data volume, includes: The corresponding difference values ​​are obtained by subtracting the single-frequency data volumes of frequencies f1, f2, and f3 of the normal illumination azimuth seismic data volume from the normal illumination azimuth seismic data volume, respectively. The frequency corresponding to the maximum difference is taken as the dominant frequency, and the single-frequency data volume corresponding to this dominant frequency is the normal illumination azimuth frequency-divided seismic data volume.

7. The crack prediction method based on PCA fusion of normal orientation Wheeler domain structural class attributes as described in claim 1, characterized in that, The process of flattening the normal illumination azimuth-divided seismic data volume to the Wheeler domain to obtain the Wheeler domain normal illumination azimuth-divided seismic data volume, and extracting the attribute plane of the structural class attribute volume of the Wheeler domain normal illumination azimuth-divided seismic data volume along the interpretation horizon of the buried mountain top, includes: By interpreting the stratigraphic horizon using the buried mountain top, the normal illumination azimuth frequency-divided seismic data volume is flattened to the Wheeler domain to obtain the Wheeler domain normal illumination azimuth frequency-divided seismic data volume; The three structural attribute volumes of the Wheeler domain normal illumination azimuth frequency-divided seismic data volume are calculated, and the attribute planes of the three structural attribute volumes are extracted along the interpreted horizon of the buried mountain top.

8. A crack prediction system based on PCA fusion of normal orientation Wheeler domain structural class attributes, characterized in that, include: The normal illumination azimuth frequency-divided seismic data volume determination module is used to divide the pre-stack wide azimuth seismic gather data of the buried hill fracture reservoir to be tested into pre-stack seismic gather data of different azimuths, and perform data superposition processing and optimization respectively to form a normal illumination azimuth seismic data volume, and then obtain the normal illumination azimuth frequency-divided seismic data volume. The attribute plane determination module is used to flatten the normal illumination azimuth frequency-division seismic data volume to the Wheeler domain, obtain the Wheeler domain normal illumination azimuth frequency-division seismic data volume, and extract the attribute plane of the structural class attribute volume of the Wheeler domain normal illumination azimuth frequency-division seismic data volume along the interpretation horizon of the buried mountain top. The fusion processing module is used to fuse the attribute planes of the structural attribute volumes to obtain the PCA fusion results of the structural attribute volumes in the normal illumination orientation Wheeler domain, which are used as the planar distribution prediction results of the buried hill fracture reservoir to be tested.

9. A processing device, characterized in that, The method includes computer program instructions, wherein when executed by a processing device, the computer program instructions are used to implement the steps corresponding to the crack prediction method based on normal orientation Wheeler domain structural class attribute PCA fusion as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, wherein when the computer program instructions are executed by a processor, they are used to implement the steps corresponding to the crack prediction method based on normal orientation Wheeler domain structural class attribute PCA fusion as described in any one of claims 1-7.

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