A fault determination method, device, equipment and medium
By utilizing the azimuth and frequency-based data volume coherence attributes of pre-stack common reflection point gathers and post-stack seismic data, combined with seismic curvature, the problem of difficulty in identifying small faults in existing technologies has been solved, and high-precision identification of faults smaller than 10 meters has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA NAT PETROLEUM CORP
- Filing Date
- 2023-07-28
- Publication Date
- 2026-07-07
AI Technical Summary
Existing technologies struggle to accurately identify faults smaller than 10 meters. Conventional properties and new methods rely on insufficient seismic phase axis variations, and post-stack seismic data loses some pre-stack positional information, making small fault detection difficult.
By utilizing pre-stack common reflection point gathers and post-stack seismic data, the dominant azimuth and dominant frequency of faults are determined through the coherence properties of azimuth-based and frequency-based data volumes, and fault identification is performed in conjunction with seismic curvature.
It improves the accuracy and reliability of fault identification, especially the ability to detect small faults, and enhances the accuracy of identifying faults less than 10 meters in length.
Smart Images
Figure CN119439260B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, equipment and medium for determining faults. Background Technology
[0002] A fault is a structural feature formed by significant relative displacement of rock blocks along its two sides after the Earth's crust fractures under stress; these faults often contain abundant oil and gas resources. Currently, with breakthroughs in marine and continental shale oil and gas in several basins in China, the design and implementation of horizontal wells have become crucial for increasing production in these unconventional oil and gas reservoirs. However, the development of small faults significantly impacts the design and implementation of horizontal wells. Furthermore, some mature exploration blocks in older oilfields in China have entered the residual oil extraction stage, making the identification of micro-structures controlled by small faults key to upper reservoir development. Therefore, accurate fault identification is an important research task in oil and gas exploration.
[0003] Regarding fault detection methods, most scholars utilize seismic tectonic attributes such as coherence, curvature, and dip angle for fault detection. These attributes can effectively predict faults larger than 20 meters. Other scholars use attributes such as structural steering filtering and ant-like structures for fault detection, which can effectively predict faults larger than 10 meters. Although these attributes and methods have shown significant effectiveness in large fault detection, they still have the following shortcomings: 1) These fault detection attributes and new methods heavily rely on changes in the seismic phase axis. Faults smaller than 10 meters do not form obvious seismic reflection characteristics on seismic profiles, so conventional attributes and some recent new methods are difficult to use changes in the seismic phase axis to predict faults smaller than 10 meters. 2) Conventional post-stack seismic data often have low dominant frequencies and lose some front-stack positional information, which is not conducive to the detection of faults smaller than 10 meters.
[0004] Therefore, how to provide an effective and accurate technical solution for identifying faults is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0005] This application provides a method, apparatus, equipment, and medium for determining faults. It makes full use of pre-stack common reflection point gathers and post-stack seismic data of the target block to determine the dominant azimuth and dominant frequency of the target layer, providing a reasonable and effective judgment criterion for fault identification and improving the accuracy of fault identification.
[0006] According to one aspect of this application, a method for determining faults is provided, the method comprising:
[0007] Based on the pre-stack common reflection point gathers and post-stack seismic data of the target block, a first preset number of azimuth data volumes and a second preset number of frequency data volumes of the target block are determined.
[0008] Based on the coherence attributes of each of the aforementioned directional data volumes, the dominant orientation of the target layer in the target block is determined;
[0009] The dominant frequency of the target layer is determined based on the coherence properties of each of the frequency division data volumes.
[0010] Based on the dominant orientation and the dominant frequency, determine whether the target layer is a fault.
[0011] According to another aspect of this application, a fault determination apparatus is provided, the apparatus comprising:
[0012] The seismic data volume partitioning module is used to determine a first preset number of azimuth data volumes and a second preset number of frequency data volumes for the target block based on the pre-stack common reflection point gathers and post-stack seismic data of the target block.
[0013] The dominant orientation determination module is used to determine the dominant orientation of the target layer in the target block based on the coherence attributes of each of the sub-orientation data volumes.
[0014] The dominant frequency determination module is used to determine the dominant frequency of the target layer based on the coherence attributes of each of the frequency division data volumes.
[0015] The fault identification module is used to determine whether the target layer is a fault based on the dominant orientation and the dominant frequency.
[0016] According to another aspect of this application, a fault-determining device is provided, the device comprising:
[0017] At least one processor; and
[0018] A memory communicatively connected to the at least one processor; wherein,
[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the tomography determination method described in any embodiment of this application.
[0020] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for determining tomography as described in any embodiment of this application.
[0021] The technical solution provided in this application determines a first predetermined number of azimuth data volumes and a second predetermined number of frequency data volumes for the target block based on pre-stack common reflection point gathers and post-stack seismic data. Based on the coherence attributes of each azimuth data volume, the dominant azimuth of the target layer in the target block is determined. Based on the coherence attributes of each frequency data volume, the dominant frequency of the target layer is determined. Based on the dominant azimuth and dominant frequency, it is determined whether the target layer is a fault. This technical solution fully utilizes the pre-stack common reflection point gathers and post-stack seismic data of the target block to determine the dominant azimuth and dominant frequency of the target layer, providing a reasonable and effective judgment criterion for fault identification and improving the accuracy of fault identification.
[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description
[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0024] Figure 1 A flowchart illustrating a method for determining a fault, as provided in Embodiment 1 of this application;
[0025] Figure 2 A flowchart illustrating a method for determining a fault, as provided in Embodiment 2 of this application;
[0026] Figure 3 This is a schematic diagram of a first fault detection plane provided in Embodiment 2 of this application;
[0027] Figure 4a This is a schematic diagram of a second fault detection plane provided in Embodiment 2 of this application;
[0028] Figure 4b This is another schematic diagram of a first fault detection plane provided in Embodiment 2 of this application;
[0029] Figure 5 This is a schematic diagram of a fault determination device provided in Embodiment 3 of this application;
[0030] Figure 6 This is a schematic diagram of the structure of a device for implementing a method for determining a fault according to an embodiment of this application. Detailed Implementation
[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0032] It should be noted that the terms "first," "second," "third," "candidate," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0033] Example 1
[0034] Figure 1 This is a flowchart illustrating a fault determination method provided in Embodiment 1 of this application. This embodiment is applicable to the detection of small faults in oil and gas exploration and development. The method can be executed by a fault determination device, which can be implemented in hardware and / or software and can be configured in a device with data processing capabilities. Figure 1 As shown, the method includes:
[0035] S110. Based on the pre-stack common reflection point gathers and post-stack seismic data of the target block, determine the first preset number of azimuth data volumes and the second preset number of frequency data volumes of the target block.
[0036] Among them, the pre-stack common reflection point gather is a gather composed of corresponding records of reflection points with common depth when the reflection interface is horizontal in the pre-stack seismic data. Post-stack seismic data can be data after denoising and amplitude normalization. Azimuth-based data volume can be amplitude data volume at different axial azimuths, and frequency-based data volume can be amplitude data volume at different frequencies.
[0037] Specifically, the first and second preset quantities can be determined according to actual needs.
[0038] As an optional but non-limiting implementation, based on the pre-stack common reflection point gathers and post-stack seismic data of the target block, a first preset number of azimuth-based data volumes and a second preset number of frequency-based data volumes of the target block are determined, including but not limited to the following steps A1 to A2:
[0039] Step A1: Based on the pre-stack common reflection point gather of the target block, divide the first preset number of azimuth data volumes according to a preset angle interval; wherein, the preset angle is determined according to the fault direction of the target block.
[0040] Since pre-stack common reflection point gathers contain abundant seismic azimuth information, they can be divided according to a preset angular interval to obtain a first preset number of sub-azimuth data volumes. In this embodiment of the invention, the first preset number of sub-azimuth data volumes can be divided according to a preset angular interval based on the quality analysis of the pre-stack common reflection point gathers and the main strike of faults within the target block.
[0041] For example, the pre-stack common reflection point gather can be divided into 30° intervals within the range of 0° to 180° to obtain 0° azimuth data volumes, 30° azimuth data volumes, 60° azimuth data volumes, 90° azimuth data volumes, 120° azimuth data volumes, and 150° azimuth data volumes, respectively.
[0042] Step A2: Based on the post-stack seismic data of the target block, divide the data into a second preset number of frequency-divided data volumes according to the preset frequency interval.
[0043] Since frequency characteristics in post-stack seismic data better highlight anisotropy, the post-stack seismic data can be divided according to a preset frequency interval to obtain a second preset number of frequency-divided data volumes. In this embodiment of the invention, the post-stack seismic data of the target block can be divided into a second preset number of frequency-divided data volumes according to a preset frequency interval through dominant frequency analysis.
[0044] For example, the post-stack seismic data can be divided into 10Hz intervals within the range of 10Hz to 60Hz, resulting in 10Hz, 20Hz, 30Hz, 40Hz, 50Hz, and 60Hz frequency-divided data volumes.
[0045] The beneficial effect of the above technical solution is that by fully considering the advantages of pre-stack common reflection point gathers in terms of azimuth and the advantages of post-stack seismic data in terms of frequency, the division of azimuth-based data volumes and frequency-based data volumes is more scientific and reasonable. It not only effectively utilizes pre-stack common reflection point gathers and post-stack seismic data, but also makes it more conducive to fault identification.
[0046] S120. Determine the dominant orientation of the target layer in the target block based on the coherence attributes of each of the sub-directional data volumes.
[0047] Among them, coherence attributes can be used to represent the discontinuity characteristics of data volumes. They can be calculated using preset coherence algorithms, such as correlation-based algorithms (C1 algorithm), seismic trace similarity-based algorithms (C2 algorithm), matrix eigenvalue-based algorithms (C3 algorithm), or sub-volume feature-based algorithms (C4 algorithm), etc.
[0048] In geology, the process of stratigraphic deposition is gradual, meaning that strata are generally horizontally continuous or gradually homogeneous. Therefore, signals measured by adjacent seismic traces should have a high degree of similarity. When a fault exists, the strata are no longer continuous or gradually homogeneous, but rather abrupt, resulting in very low coherence between adjacent traces. Therefore, by calculating the coherence attributes of each sub-azimuth data volume, incoherent data within each sub-azimuth data volume can be highlighted, thereby determining the dominant azimuth of the target layer in the target block.
[0049] The target layer can be the layer to be identified by fault identification, which can be determined according to actual needs. The dominant orientation can be the orientation corresponding to the sub-orientation data volume with the largest coherence attribute value.
[0050] In this embodiment of the invention, the coherence attributes of the target layer in each sub-azimuth data volume can be determined based on the coherence attributes of each sub-azimuth data volume and the seismic horizon interpreted by the 1*1 grid density of the target segment. Then, the coherence attributes of the target layer in each sub-azimuth data volume can be compared to determine the dominant azimuth of the target layer in the target block.
[0051] As an optional but non-limiting implementation, the dominant orientation of the target layer in the target block is determined based on the coherence attributes of each of the said sub-directional data volumes, including but not limited to the following steps B1 to B2:
[0052] Step B1: Based on the coherence attributes of each of the sub-directional data volumes, extract the first coherence attribute plane map corresponding to the target layer in the target block from each of the sub-directional data volumes.
[0053] Specifically, coherent attribute plane maps can be extracted from each sub-azimuth data volume after interpolation according to the 1*1 grid density of the target layer segment, so as to obtain the first coherent attribute plane map in each sub-azimuth data volume corresponding to the target layer in the target block.
[0054] Step B2: Compare the first coherent attribute plane maps to determine the dominant orientation of the target layer.
[0055] In this embodiment of the invention, the coherence attribute values in each first coherence attribute plane map can be compared, and the orientation of the first coherence attribute plane map corresponding to the largest coherence attribute value can be taken as the dominant orientation of the target layer. Specifically, the orientation corresponding to the first coherence attribute plane map with relatively good indication effect can also be taken as the dominant orientation.
[0056] S130. Determine the dominant frequency of the target layer based on the coherence properties of each of the frequency division data volumes.
[0057] Among them, the dominant frequency can be the frequency corresponding to the frequency sub-frequency data volume with the largest coherence attribute value.
[0058] In this embodiment of the invention, the coherence properties of the target layer in each frequency-division data volume can be determined based on the coherence properties of each frequency-division data volume and the seismic horizon interpreted by the 1*1 grid density of the target segment. Then, the coherence properties of the target layer in each frequency-division data volume can be compared to determine the dominant frequency of the target layer in the target block.
[0059] As an optional but non-limiting implementation, the dominant frequency of the target layer is determined based on the coherence properties of each of the frequency-division data volumes, including but not limited to the following steps C1 to C2:
[0060] Step C1: Based on the coherence attributes of each frequency division data volume, extract the second coherence attribute plane map corresponding to the target layer from each frequency division data volume.
[0061] Specifically, the coherent attribute plane map can be extracted from each frequency-division data volume after interpolation according to the 1*1 grid density of the target layer segment, so as to obtain the second coherent attribute plane map in each frequency-division data volume corresponding to the target layer in the target block.
[0062] Step C2: Compare each of the second coherence attribute plane maps to determine the dominant frequency of the target layer.
[0063] Specifically, the coherent attribute values in each second coherent attribute plane can be compared, and the frequency of the second coherent attribute plane corresponding to the largest coherent attribute value can be taken as the dominant frequency of the target layer.
[0064] S140. Determine whether the target layer is a fault based on the dominant orientation and the dominant frequency.
[0065] Specifically, based on the dominant frequency of the target layer, the dominant azimuth frequency seismic data volume can be calculated on the sub-azimuth data volume corresponding to the dominant azimuth, and then the target layer can be identified as a fault based on the dominant azimuth frequency seismic data volume.
[0066] Fault identification can be determined by coherent algorithm calculations on the dominant azimuth and dominant frequency seismic data volume, or by the seismic curvature of the dominant azimuth and dominant frequency seismic data volume.
[0067] This invention provides a method for determining faults. The method involves determining a first predetermined number of azimuth data volumes and a second predetermined number of frequency data volumes for a target block based on pre-stack common reflection point gathers and post-stack seismic data. Based on the coherence properties of each azimuth data volume, the dominant azimuth of the target layer within the target block is determined. Based on the coherence properties of each frequency data volume, the dominant frequency of the target layer is determined. Finally, based on the dominant azimuth and dominant frequency, it is determined whether the target layer is a fault. This technical solution fully utilizes the pre-stack common reflection point gathers and post-stack seismic data of the target block to determine the dominant azimuth and dominant frequency of the target layer, providing a reasonable and effective judgment criterion for fault identification and improving the accuracy of fault identification.
[0068] Example 2
[0069] Figure 2 This is a flowchart illustrating a method for determining a fault according to Embodiment 2 of this application. This embodiment is an optimization based on the above embodiment. Figure 2 As shown, the method in this embodiment specifically includes the following steps:
[0070] S210. Based on the pre-stack common reflection point gathers and post-stack seismic data of the target block, determine the first preset number of azimuth data volumes and the second preset number of frequency data volumes of the target block.
[0071] S220. Based on the coherence attributes of each of the said directional data volumes, determine the dominant orientation of the target layer in the target block.
[0072] S230. Determine the dominant frequency of the target layer based on the coherence properties of each of the frequency division data volumes.
[0073] S240. Generate the target seismic data volume based on the dominant azimuth and the dominant frequency.
[0074] The target seismic data volume can be calculated based on the sub-azimuth data volume corresponding to the dominant frequency in the dominant azimuth; or it can be calculated based on the frequency sub-frequency data volume corresponding to the dominant azimuth in the dominant frequency.
[0075] S250. Extract the first fault detection planar map of the target layer from the target seismic data volume, and determine the fault judgment result of the target layer based on the first fault detection planar map.
[0076] Specifically, the target seismic data volume can be interpolated according to the 1*1 grid density of the target layer to extract the fault detection planar map, thus obtaining the first fault detection planar map. Further, the fault characteristics in the first fault detection planar map can be used to determine whether the target layer is a fault.
[0077] As an optional but non-limiting implementation, the first fault detection planar image of the target layer is extracted from the target seismic data volume, including but not limited to the following steps: performing fault detection on the target seismic data volume based on amplitude direction decomposition technology, and extracting the first fault detection planar image of the target layer.
[0078] Among them, amplitude direction decomposition technology can be used to perform fault detection on target seismic data volumes by utilizing amplitude differences caused by anisotropy. For example, Figure 3 This is a schematic diagram of a first fault detection plane provided in Embodiment 2 of this application. Figure 3 As shown, the fault detection planar diagram of the first fault layer extracted from the target seismic data volume has more obvious fault characteristics, which is more conducive to intuitive analysis and improves the accuracy of the fault detection planar diagram.
[0079] The beneficial effect of the above technical solution is that it can perform fault detection on the target seismic data volume by using the amplitude difference caused by anisotropy, making fault detection more sensitive, calculation faster, and prediction accuracy higher.
[0080] Based on the above embodiments, optionally, after determining whether the target layer is a fault according to the dominant orientation and the dominant frequency, the method further includes: comparing the second fault detection plan map and the first fault detection plan map in the well logging data to determine whether the fault judgment result is correct.
[0081] The second fault detection plan can be obtained from well logging data of the target block; it is a conventional coherent fault detection plan. For example, taking a deep shale gas and oil reservoir in a basin as an example... Figure 4a This is a schematic diagram of a second fault detection plane provided in Embodiment 2 of this application, as shown below. Figure 4a The image shown is a plan view of conventional coherent fault detection from the drilling data of four wells with fault encounter information. Figure 4b This is another schematic diagram of the first fault detection plane provided in Embodiment 2 of this application, as shown below. Figure 4b As shown, for the same as Figure 4a The schematic diagram of the first fault detection plane corresponding to the target layer, through analysis Figure 4b The second fault detection plan shown can preliminarily identify the target layer as a fault. Further, by... Figure 4a The second fault detection plan shown is consistent with Figure 4aBy comparing the first fault detection plan shown, it can be seen from the comparison results that the fault displacement and fault strike are basically the same, thus verifying that the judgment that the target layer is a fault is correct and reliable.
[0082] The beneficial effect of the above technical solution is that it can ensure the reliability and effectiveness of fault determination and improve the accuracy of fault determination.
[0083] This invention provides a method for determining faults. The method involves determining a first predetermined number of azimuth data volumes and a second predetermined number of frequency data volumes for a target block based on pre-stack common reflection point gathers and post-stack seismic data. Based on the coherence attributes of each azimuth data volume, the dominant azimuth of the target layer within the target block is determined. Based on the coherence attributes of each frequency data volume, the dominant frequency of the target layer is determined. Based on the dominant azimuth and dominant frequency, a target seismic data volume is generated. A first fault detection plan of the target layer is extracted from the target seismic data volume, and the fault judgment result of the target layer is determined based on the first fault detection plan. This technical solution improves the accuracy and precision of fault identification of the target layer by processing the dominant azimuth and dominant frequency of the target layer to extract the fault detection plan.
[0084] Example 3
[0085] Figure 5 This is a schematic diagram of a fault determination device provided in Embodiment 3 of this application. Figure 5 As shown, the device includes:
[0086] The seismic data volume partitioning module 310 is used to determine a first preset number of azimuth data volumes and a second preset number of frequency data volumes of the target block based on the pre-stack common reflection point gathers and post-stack seismic data of the target block.
[0087] The dominant orientation determination module 320 is used to determine the dominant orientation of the target layer in the target block based on the coherence attributes of each of the sub-orientation data volumes.
[0088] The dominant frequency determination module 330 is used to determine the dominant frequency of the target layer based on the coherence attributes of each of the frequency division data volumes.
[0089] The fault identification module 340 is used to determine whether the target layer is a fault based on the dominant orientation and the dominant frequency.
[0090] This invention provides a fault identification device. This device determines a first predetermined number of azimuth data volumes and a second predetermined number of frequency data volumes of a target block based on pre-stack common reflection point gathers and post-stack seismic data. Based on the coherence attributes of each azimuth data volume, it determines the dominant azimuth of the target layer within the target block; based on the coherence attributes of each frequency data volume, it determines the dominant frequency of the target layer; and based on the dominant azimuth and dominant frequency, it determines whether the target layer is a fault. This technical solution fully utilizes the pre-stack common reflection point gathers and post-stack seismic data of the target block to determine the dominant azimuth and dominant frequency of the target layer, providing a reasonable and effective judgment criterion for fault identification and improving the accuracy of fault identification.
[0091] Furthermore, the fault discrimination module 340 includes:
[0092] The seismic data volume generation unit is used to generate a target seismic data volume based on the dominant azimuth and the dominant frequency.
[0093] The fault discrimination unit is used to extract the first fault detection planar map of the target layer from the target seismic data volume, and determine the fault judgment result of the target layer based on the first fault detection planar map.
[0094] Furthermore, the fault discrimination unit includes:
[0095] The fault detection planar map extraction sub-unit is used to perform fault detection on the target seismic data volume based on amplitude direction decomposition technology, and extract the first fault detection planar map of the target layer.
[0096] Furthermore, the device also includes:
[0097] The fault result verification module is used to compare the second fault detection plan and the first fault detection plan in the well logging data after determining whether the target layer is a fault based on the dominant orientation and the dominant frequency, to determine whether the fault judgment result is correct.
[0098] Furthermore, the seismic data volume partitioning module 310 includes:
[0099] The azimuth data volume division unit is used to divide the target block into a first preset number of azimuth data volumes according to a preset angle interval based on the pre-stack common reflection point gather of the target block; wherein, the preset angle is determined according to the fault strike of the target block.
[0100] The frequency-division data volume division unit is used to divide the target block into a second preset number of frequency-division data volumes according to a preset frequency interval based on the post-stack seismic data.
[0101] Furthermore, the dominant orientation determination module 320 includes:
[0102] The first coherence attribute plane map extraction unit is used to extract the first coherence attribute plane map corresponding to the target layer in the target block in each of the sub-directional data volumes according to the coherence attributes of each of the sub-directional data volumes.
[0103] The dominant orientation determination unit is used to compare each of the first coherent attribute plane maps to determine the dominant orientation of the target layer.
[0104] Furthermore, the dominant frequency determination module 330 includes:
[0105] The second coherence attribute plane extraction unit is used to extract the second coherence attribute plane corresponding to the target layer in each of the frequency division data volumes according to the coherence attributes of each of the frequency division data volumes.
[0106] The dominant frequency determination unit is used to compare each of the second coherent attribute plane maps to determine the dominant frequency of the target layer.
[0107] The fault determination device provided in this application embodiment can execute the fault determination method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.
[0108] Example 4
[0109] Figure 6 A schematic diagram of the structure of a device 10 that can be used to implement embodiments of this application is shown. The device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.
[0110] like Figure 6As shown, device 10 includes at least one processor 11 and a memory, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc., communicatively connected to at least one processor 11. The memory stores computer programs executable by at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of device 10. The processor 11, ROM 12, and RAM 13 are interconnected via bus 14. Input / output (I / O) interface 15 is also connected to bus 14.
[0111] Multiple components in device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0112] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining tomography.
[0113] In some embodiments, the method for determining the tomography may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the tomography determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the tomography determination method by any other suitable means (e.g., by means of firmware).
[0114] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0115] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0116] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or haptic feedback); and input from the user can be received in any form (including sound input, voice input, or haptic input).
[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0119] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0120] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.
[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A method of fault determination, characterized by, The method includes: Based on the pre-stack common reflection point gathers and post-stack seismic data of the target block, a first preset number of azimuth data volumes and a second preset number of frequency data volumes of the target block are determined. Based on the coherence attributes of each of the aforementioned directional data volumes, the dominant orientation of the target layer in the target block is determined; The dominant frequency of the target layer is determined based on the coherence properties of each of the frequency division data volumes. Based on the dominant orientation and the dominant frequency, determine whether the target layer is a fault; The step of determining a first preset number of azimuth-based data volumes and a second preset number of frequency-based data volumes for the target block based on pre-stack common reflection point gathers and post-stack seismic data includes: Based on the pre-stack common reflection point gather of the target block, a first preset number of azimuth data volumes are divided according to a preset angle interval; wherein, the preset angle is determined according to the fault direction of the target block; Based on the post-stack seismic data of the target block, divide the data into a second preset number of frequency-divided data volumes according to a preset frequency interval; The step of determining the tomographic result of the target layer based on the dominant orientation and the dominant frequency includes: A target seismic data volume is generated based on the dominant azimuth and the dominant frequency; wherein the target seismic data volume is calculated based on the sub-azimuth data volume corresponding to the dominant azimuth according to the dominant frequency, or the target seismic data volume is calculated based on the frequency sub-frequency data volume corresponding to the dominant frequency according to the dominant azimuth. Extract the first fault detection planar image of the target layer from the target seismic data volume, and determine the fault judgment result of the target layer based on the first fault detection planar image.
2. The method of claim 1, wherein, Extracting the first fault detection planar map of the target layer from the target seismic data volume includes: Based on amplitude direction decomposition technology, fault detection is performed on the target seismic data volume to extract the first fault detection planar map of the target layer.
3. The method according to claim 1, characterized in that, After determining whether the target layer is a fault based on the dominant orientation and the dominant frequency, the method further includes: The second fault detection plan view and the first fault detection plan view in the well logging data are compared to determine whether the fault judgment result is correct.
4. The method according to claim 1, characterized in that, Based on the coherence attributes of each of the aforementioned directional data volumes, the dominant orientation of the target layer in the target block is determined, including: Based on the coherence attributes of each of the sub-directional data volumes, extract the first coherence attribute plane map corresponding to the target layer in the target block from each of the sub-directional data volumes; By comparing the first coherent attribute plane maps, the dominant orientation of the target layer is determined.
5. The method according to claim 1, characterized in that, Determining the dominant frequency of the target layer based on the coherence properties of each of the frequency-division data volumes includes: Based on the coherence attributes of each frequency division data volume, extract the second coherence attribute plane map corresponding to the target layer in each frequency division data volume; By comparing the second coherence attribute plane maps, the dominant frequency of the target layer is determined.
6. A device for determining faults, characterized in that, The device includes: The seismic data volume partitioning module is used to determine a first preset number of azimuth data volumes and a second preset number of frequency data volumes for the target block based on the pre-stack common reflection point gathers and post-stack seismic data of the target block. The dominant orientation determination module is used to determine the dominant orientation of the target layer in the target block based on the coherence attributes of each of the sub-orientation data volumes. The dominant frequency determination module is used to determine the dominant frequency of the target layer based on the coherence attributes of each of the frequency division data volumes. The fault identification module is used to determine whether the target layer is a fault based on the dominant orientation and the dominant frequency. The seismic data volume partitioning module includes: The azimuth data volume division unit is used to divide the target block into a first preset number of azimuth data volumes according to a preset angle interval based on the pre-stack common reflection point gather of the target block; wherein, the preset angle is determined according to the fault strike of the target block. The frequency division data volume partitioning unit is used to divide the target block into a second preset number of frequency division data volumes according to a preset frequency interval based on the post-stack seismic data of the target block. The fault detection module includes: The seismic data volume generation unit is used to generate a target seismic data volume based on the dominant azimuth and the dominant frequency; wherein the target seismic data volume is calculated based on the sub-azimuth data volume corresponding to the dominant azimuth according to the dominant frequency, or the target seismic data volume is calculated based on the frequency sub-frequency data volume corresponding to the dominant frequency according to the dominant azimuth. The fault discrimination unit is used to extract the first fault detection planar map of the target layer from the target seismic data volume, and determine the fault judgment result of the target layer based on the first fault detection planar map.
7. An electronic device, characterized in that, The device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method for determining a fault as described in any one of claims 1-5.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining a fault as described in any one of claims 1-5.