Property-enhanced buried hill fracture zone prediction method, device, equipment and storage medium

Abstract

The present invention provides an attribute-enhanced buried-hill fracture zone prediction method, apparatus, device, and storage medium. The method comprises: acquiring curvature seismic attributes of a target exploration area; enhancing the curvature seismic attributes using a Gaussian distance weighted grid scanning method to obtain enhanced seismic attributes; performing a complex shear wave transform on the enhanced seismic attributes to obtain complex shear wave transform data; binarizing the complex shear wave transform data to obtain fracture prediction results; and outputting the fracture prediction results. This method improves prediction accuracy.

Description

Technical Field

[0001] The present application relates to the field of oil and gas geological exploration and development, and in particular to a method, device, equipment and storage medium for predicting buried hill fracture zones with enhanced properties. Background Art

[0002] In recent years, the proportion of buried-hill oil and gas reserves has increased annually, making buried-hill reservoirs key to increasing offshore reserves and production. Fractures are crucial flow pathways and storage spaces in these reservoirs, and the quality of fracture prediction is a crucial factor in determining the effectiveness and economic benefits of buried-hill reservoir development.

[0003] In existing technology, curvature seismic attributes are an important tool for predicting fractures in buried-hill oil and gas reservoirs. First, these attributes are extracted from seismic data. Then, these attributes are subjected to linear or nonlinear adjustments to the overall color display. Finally, these attributes are binarized to produce fracture prediction results.

[0004] However, during the binarization process, small-scale crack information is filtered out due to the overly strong response of large-scale crack information, making it difficult to predict, thereby reducing the prediction effect and accuracy. Summary of the Invention

[0005] The embodiments of the present application provide a method, apparatus, device, and storage medium for predicting a buried hill fracture zone with enhanced properties, so as to achieve the effect of improving prediction accuracy.

[0006] In the first aspect, an embodiment of the present application provides an attribute-enhanced method for predicting a buried hill fracture zone, which is applied to a server, and includes: obtaining the curvature seismic attributes of the target exploration area; using the Gaussian distance weighted grid scanning method to enhance the curvature seismic attributes to obtain enhanced seismic attributes; performing a complex shear wave transform on the enhanced seismic attributes to obtain complex shear wave transform data; binarizing the complex shear wave transform data to obtain a fracture prediction result; and outputting the fracture prediction result.

[0007] In one possible implementation, the curvature seismic attribute is enhanced, including: obtaining the attribute width and attribute height of the curvature seismic attribute; determining the grid size of the scanning grid based on the attribute width and attribute height; dividing the curvature seismic attribute according to the grid size to obtain multiple scanning grids; taking any seismic attribute value in the curvature seismic attribute as the center, extracting all seismic attribute values in the scanning grid corresponding to any seismic attribute value to obtain an attribute set; weighting the attribute set according to the normalized Gaussian distance weight matrix of the scanning grid corresponding to any seismic attribute value to generate a weighted seismic attribute set; wherein the normalized Gaussian distance weight matrix is obtained based on each scanning grid; and transforming any seismic attribute value according to the weighted seismic attribute set to obtain an enhanced attribute value of any seismic attribute value.

[0008] In one possible implementation, the formula for transforming any seismic attribute value is:

[0009]

[0010] Where c represents the enhanced attribute value after any earthquake attribute value is transformed, v represents any earthquake attribute value, and B max represents the maximum value of the weighted seismic attribute set, B min Represents the minimum value of the weighted seismic attribute set.

[0011] In one possible implementation, the process of determining the normalized Gaussian distance weight matrix includes: in any scanning grid, calculating the distance from each seismic attribute value to the grid center to obtain the distance matrix of any scanning grid; performing a Gaussian transformation on the distance matrix to obtain the Gaussian weight matrix corresponding to any scanning grid; and normalizing the Gaussian weight matrix to obtain the normalized Gaussian distance weight matrix corresponding to any scanning grid.

[0012] In one possible implementation, the calculation formula of the Gaussian weight matrix is:

[0013]

[0014]

[0015] Where μ i,j represents the Gaussian weight matrix, d i,j represents the distance matrix, and L represents the grid size.

[0016] In one possible implementation, the calculation formula of the normalized Gaussian distance weight matrix is:

[0017]

[0018] Where, ω i,j represents the normalized Gaussian distance weight matrix, μ i,j represents the Gaussian weight matrix, μ max Represents the maximum value of the Gaussian weight matrix.

[0019] In a second aspect, an embodiment of the present application provides an attribute-enhanced buried-hill fracture zone prediction device, which is applied to a server and includes:

[0020] The data acquisition module is used to obtain the curvature seismic attributes of the target exploration area.

[0021] The attribute enhancement module is used to enhance the curvature seismic attributes by using the Gaussian distance weighted grid scanning method to obtain enhanced seismic attributes.

[0022] The complex shear wave transform module is used to perform complex shear wave transform on the enhanced seismic attributes to obtain complex shear wave transform data.

[0023] The binarization processing module is used to perform binarization processing on the complex shear wave transform data to obtain the crack prediction results.

[0024] The prediction result output module is used to output the crack prediction results.

[0025] In a third aspect, an embodiment of the present application provides a server, comprising: a memory, a processor;

[0026] Memory stores computer-executable instructions;

[0027] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0029] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0030] The attribute-enhanced method, device, equipment and storage medium for predicting buried hill fracture zones provided in the embodiments of the present application can highlight local features when acting on curvature seismic attributes using the Gaussian distance weighted grid scanning method, so that the changes in stratum curvature related to buried hill fractures can be clearly presented. The weighted processing reduces the influence of scattered noise points on the results and improves the signal-to-noise ratio of the data. The enhanced seismic attributes are subjected to a complex shear wave transform so that their multi-scale characteristics can respectively analyze the fracture distribution pattern in a large area and the local fine fracture structure. The present application can significantly improve the precision and accuracy of fracture prediction using curvature seismic attributes, and improve the effect of buried hill fracture prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0032] Figure 1 A schematic diagram of a scenario for the attribute-enhanced buried-hill fracture zone prediction method provided in an embodiment of the present application;

[0033] Figure 2A schematic flow chart of a method for predicting buried-hill fracture zones with enhanced attributes provided in an embodiment of the present application;

[0034] Figure 2a A schematic diagram of the original curvature seismic attributes provided in an embodiment of the present application;

[0035] Figure 2b A schematic diagram of enhanced seismic attributes provided in an embodiment of the present application;

[0036] Figure 2c A schematic diagram of the effect after complex shear wave transformation provided in an embodiment of the present application;

[0037] Figure 2d A schematic diagram comparing the crack prediction results of the prior art provided in the embodiments of the present application with the crack prediction results of the present application;

[0038] Figure 3 A schematic diagram of a process for enhancing curvature seismic attributes provided in an embodiment of the present application;

[0039] Figure 3a A schematic diagram of extracting all seismic attribute values in a scanning grid with any seismic attribute value as the center provided in an embodiment of the present application;

[0040] Figure 4 A schematic diagram of the structure of a property-enhanced buried-hill fracture zone prediction device provided in an embodiment of the present application;

[0041] Figure 5 A schematic diagram of the structure of the server provided in an embodiment of the present application.

[0042] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0044] First, let’s explain the terms involved in this application:

[0045] Buried hill fracture zone: Buried hill refers to a buried hill formed when ancient terrain is covered by new sedimentary strata. During the geological evolution process, fracture zones are formed within the buried hill due to tectonic stress, weathering, dissolution, and other effects.

[0046] Property enhancement: It is to optimize and strengthen the properties that can reflect the characteristics of the fracture zone through a series of data processing and analysis technologies.

[0047] To clearly understand the technical solution of this application, we first introduce the solutions of the prior art in detail. In recent years, the proportion of buried-hill oil and gas resource reserves has increased year by year, and buried-hill oil and gas reservoirs have become the key to increasing offshore reserves and production. Fractures are important seepage channels and storage spaces for this type of reservoir. The quality of fracture prediction is a key factor in determining the development effect and economic benefits of buried-hill oil and gas reservoirs. The development of buried-hill fractures is clearly controlled by the tectonic action of the formation. Therefore, curvature seismic attributes are an important means of predicting fractures in buried-hill oil and gas reservoirs. The current basic process for fracture prediction using curvature seismic attributes is: first, extract curvature seismic attributes from seismic data; second, perform linear or nonlinear adjustments on the overall color display of the extracted curvature seismic attributes to better reflect the location and characteristics of the fractures; finally, binarize the adjusted curvature seismic attributes to obtain the fracture prediction results. The current method can predict the development of large and medium-scale buried-hill fractures, but because the locations where large-scale fractures develop often form areas with strong attribute values, they have a certain suppressive effect on the responses of surrounding small-scale fractures. During the binarization process of curvature seismic attributes, small-scale fracture information is filtered out due to the overly strong response of large-scale fracture information, making it difficult to predict, which seriously reduces the effect and accuracy of fracture prediction based on curvature seismic attributes.

[0048] To address the above technical issues, the inventors devised a method that first uses curvature seismic attributes as input, then enhances the extracted curvature seismic attributes using Gaussian distance weighted grid scanning, highlighting weak-intensity attribute information suppressed by high-intensity attributes. Based on the enhanced attributes, the inventors then use complex shear wave transform to obtain complex shear wave transform data. Finally, the complex shear wave transform data is binarized to extract the final fracture prediction results. This method can enhance the fracture response characteristics of curvature seismic attributes and improve the accuracy of fracture prediction based on curvature seismic attributes.

[0049] Based on the above creative findings, the inventor proposed the technical solution of this application.

[0050] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0051] Figure 1 A schematic diagram of a scenario for the attribute-enhanced buried-hill fracture zone prediction method provided in an embodiment of the present application. The scenario includes: a receiving device 101, a processor 102, and a display device 103.

[0052] It should be understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the attribute-enhanced buried-hill fracture zone prediction method. In other feasible implementations of this application, the above architecture may include more or fewer components than shown, or may combine or separate certain components, or have different component arrangements. The specific configuration can be determined based on the actual application scenario and is not limited here. Figure 1 The components shown may be implemented in hardware, software, or a combination of software and hardware.

[0053] In a specific implementation process, the receiving device 101 may be an input / output interface or a communication interface, and is configured to receive the curvature seismic attributes of the target exploration area.

[0054] Processor 102 can use Gaussian distance weighted grid scanning method to enhance the curvature seismic attributes to obtain enhanced seismic attributes, perform complex shear wave transform on the enhanced seismic attributes to obtain complex shear wave transform data, and binarize the complex shear wave transform data to obtain crack prediction results.

[0055] The display device can be used to output the crack prediction results.

[0056] It should be understood that the above-mentioned processor can be implemented by the processor reading instructions in the memory and executing the instructions, or it can be implemented by a chip circuit.

[0057] Figure 2 A flow chart of the attribute-enhanced buried hill fracture zone prediction method provided in the embodiment of the present application is shown as follows: Figure 2 As shown, the method includes:

[0058] S201: Obtain curvature seismic attributes of the target exploration area.

[0059] Target exploration areas usually refer to specific areas with potential oil and gas resources or important geological research significance, such as an area where buried hill structures may exist.

[0060] Specifically, Figure 2aSchematic diagram of the original curvature seismic attribute provided for the embodiment of the present application. The acquisition of curvature seismic attributes first requires seismic exploration work. Seismic detectors and other equipment are arranged in the target area. Seismic waves are excited by artificial sources. When the seismic waves propagate underground and encounter different stratum interfaces, they are reflected. The detectors receive these reflected wave signals. After a series of data processing, including but not limited to filtering, superposition, and offset, a special algorithm is used to calculate the curvature information of the stratum interface, thereby obtaining curvature seismic attribute data. The algorithm may involve secondary derivative calculations, fitting methods, etc. to determine the degree of curvature according to the spatial morphology of the stratum interface, and the data related to this degree of curvature is the curvature seismic attribute.

[0061] S202: Using a Gaussian distance weighted grid scanning method, the curvature seismic attributes are enhanced to obtain enhanced seismic attributes.

[0062] Specifically, the size of the scanning grid, L, is determined to be an odd number within the range [3, min(attribute width, attribute height)]. The spatial range corresponding to the entire curvature seismic attribute data is divided into multiple grids. For each point in the grid, its distance to the grid center is calculated. Based on this distance, a weight is calculated using a Gaussian function. Points closer to the grid center have a larger weight, while points farther away have a smaller weight. The original curvature seismic attribute value of each point is then multiplied by the corresponding weight to obtain the weighted attribute value of the original curvature seismic attribute value. Based on the original curvature seismic attribute value and the weighted attribute value, the enhanced seismic attribute of each point is finally determined. Figure 2b The enhanced earthquake attribute diagram provided in the embodiment of the present application is as follows: Figure 2b As shown, the enhanced seismic attributes are Figure 2a Compared with the original curvature seismic attributes, the obtained weak attribute information is significantly enhanced.

[0063] S203: Performing complex shear wave transform on the enhanced seismic attributes to obtain complex shear wave transform data.

[0064] Among them, complex shear wave transform, as a time-frequency analysis method, can further explore multi-scale and multi-directional information on the basis of enhancing seismic attributes.

[0065] Specifically, Figure 2c This is a schematic diagram of the effect of complex shear wave transformation provided by the embodiment of the present application. By constructing a specific filter bank, the enhanced seismic attribute data is decomposed into different scales and directions.

[0066] S204: Binarization processing is performed on the complex shear wave transform data to obtain a crack prediction result.

[0067] Specifically, a threshold is set based on statistical analysis or an adaptive threshold method. For each complex shear wave transform data point, if it is greater than or equal to the threshold, it is assigned a value of 1, indicating a region predicted to have cracks. If it is less than the threshold, it is assigned a value of 0, indicating a region predicted to be crack-free. The complex shear wave transform data is binarized, converting the entire complex shear wave transform data set into a binary data set consisting only of 1s and 0s, representing the preliminary results of crack prediction.

[0068] Specifically, in the preliminary results after binarization, if there are some isolated points, such as those marked as 1 but surrounded by 0, or small areas of noise, morphological filtering and other methods can be used to remove these isolated points and small areas of noise.

[0069] S205: Output crack prediction results.

[0070] Specifically, Figure 2d The diagram below is a comparison diagram of the crack prediction results of the prior art and the crack prediction results of the present application provided in the embodiment of the present application, wherein the left diagram is the crack prediction result of the prior art and the right diagram is the crack prediction result of the present application. Figure 2d As shown, compared with the crack prediction results of the prior art, the crack prediction results obtained by the present application are more precise, ensuring that the locations of large-scale cracks are more accurate while enhancing the small-scale crack prediction results.

[0071] In summary, by using the Gaussian distance weighted grid scanning method on the curvature seismic attributes, local features can be highlighted, so that the changes in stratum curvature related to the buried hill fractures can be clearly presented. The weighted processing reduces the influence of scattered noise points on the results and improves the signal-to-noise ratio of the data. The enhanced seismic attributes are subjected to a complex shear wave transform so that their multi-scale characteristics can respectively analyze the fracture distribution pattern in a large area and the local fine fracture structure. This application can significantly improve the precision and accuracy of fracture prediction using curvature seismic attributes, and improve the effect of buried hill fracture prediction.

[0072] Figure 3 A schematic diagram of a process for enhancing curvature seismic attributes provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, the method specifically includes:

[0073] S301: Obtain the attribute width and attribute height of the curvature seismic attribute.

[0074] Attribute width and attribute height are key parameters describing the spatial dimensions of curvature seismic attributes, similar to the length and width of a 2D image or the extent of a 3D data volume on a given plane. Obtaining these two parameters is the basis for subsequent, appropriate division of the scanning grid.

[0075] S302: Determine the grid size of the scanning grid according to the attribute width and the attribute height.

[0076] Specifically, first, the minimum value of the attribute width and attribute height is selected as the upper limit of the grid size, and then an odd number is selected in the range of [3, min(attribute width, attribute height)] as the grid size L. The smaller the L value, the stronger the final enhancement effect.

[0077] S303: Segment the curvature seismic attributes according to the grid size to obtain multiple scanning grids.

[0078] Specifically, based on the determined grid size, the spatial range corresponding to the entire curvature seismic attribute data is divided regularly into multiple independent and interrelated scanning grids.

[0079] There are multiple curvature seismic attribute data in each scanning grid.

[0080] S304: Taking any seismic attribute value in the curvature seismic attribute as the center, extract all seismic attribute values in the scanning grid corresponding to any seismic attribute value to obtain an attribute set.

[0081] Specifically, Figure 3a This is a schematic diagram of extracting all seismic attribute values in a scan grid, centered around any seismic attribute value, according to an embodiment of the present application. Within each scan grid, one seismic attribute value is selected as the center, and all seismic attribute values within that scan grid are intended to form an attribute set. This operation focuses on the local grid area.

[0082] S305: weighting the attribute set according to the normalized Gaussian distance weight matrix of the scanning grid corresponding to any seismic attribute value to generate a weighted seismic attribute set; wherein the normalized Gaussian distance weight matrix is obtained according to each scanning grid.

[0083] Specifically, the process of determining the normalized Gaussian distance weight matrix includes Sa1 to Sa3:

[0084] Sa1: In any scanning grid, calculate the distance from each seismic attribute value to the grid center to obtain the distance matrix of any scanning grid.

[0085] Specifically, for each pre-defined scanning grid, the location of its grid center must first be determined. Once the center is determined, the distance from each seismic attribute value within the grid to the grid center is calculated using the distance formula between two points. The distances from all seismic attribute values within the grid to the grid center are arranged in the order in which they appear within the grid, forming the distance matrix for the scanning grid.

[0086] Sa2: Perform Gaussian transformation on the distance matrix to obtain the Gaussian weight matrix corresponding to any scan grid.

[0087] Specifically, the distance values are transformed using the Gaussian function to assign different weights to points at different distances. The expression of the Gaussian function is:

[0088]

[0089]

[0090] Where μ i,j represents the Gaussian weight matrix, d i,j represents the distance matrix, and L represents the grid size.

[0091] For example, if the distance matrix is m×n, then the Gaussian weight matrix obtained through Gaussian transformation is also an m×n matrix.

[0092] Sa3: Normalize the Gaussian weight matrix to obtain the normalized Gaussian distance weight matrix corresponding to any scan grid.

[0093] Specifically, the specific calculation method of normalization is to use each element μ in the Gaussian weight matrix i,j Divide by the maximum value μ in the matrix max The calculation formula of the normalized Gaussian distance weight matrix is:

[0094]

[0095] Where, ω i,j represents the normalized Gaussian distance weight matrix, μ i,j represents the Gaussian weight matrix, μ max Represents the maximum value of the Gaussian weight matrix.

[0096] S306: transform any seismic attribute value according to the weighted seismic attribute set to obtain an enhanced attribute value of any seismic attribute value.

[0097] Specifically, the formula for transforming any seismic attribute value is:

[0098]

[0099] Where c represents the enhanced attribute value after any earthquake attribute value is transformed, v represents any earthquake attribute value, and B max represents the maximum value of the weighted seismic attribute set, B min Represents the minimum value of the weighted seismic attribute set.

[0100] In summary, by accurately calculating the distance from each seismic attribute value to the grid center to construct a distance matrix, an accurate distance basis is provided for subsequent weighting, so that the distinction between attribute values at different locations can be clearly displayed. Based on the distance matrix, a Gaussian transformation is performed to obtain a Gaussian weight matrix. According to the characteristics of the Gaussian function, weights are reasonably assigned according to the distance, effectively highlighting the importance of areas close to the grid center. This is extremely beneficial for focusing on local key geological features, such as the core area of the buried hill fracture zone. The normalization of the Gaussian weight matrix results in a normalized Gaussian distance weight matrix, which further standardizes the weight range and ensures consistency and comparability of weighted operations between different scanning grids, thereby greatly improving the accuracy and reliability of the curvature seismic attribute enhancement processing.

[0101] Figure 4 This is a structural diagram of the attribute-enhanced buried hill fracture zone prediction device provided in an embodiment of the present application, which includes: a data acquisition module 401, an attribute enhancement module 402, a complex shear wave transform module 403, a binarization processing module 404 and a prediction result output module 405.

[0102] The data acquisition module 401 is used to acquire the curvature seismic attributes of the target exploration area.

[0103] The attribute enhancement module 402 is used to enhance the curvature seismic attributes by using the Gaussian distance weighted grid scanning method to obtain enhanced seismic attributes.

[0104] The complex shear wave transform module 403 is used to perform complex shear wave transform on the enhanced seismic attributes to obtain complex shear wave transform data.

[0105] The binarization processing module 404 is used to perform binarization processing on the complex shear wave transform data to obtain a crack prediction result.

[0106] The prediction result output module 405 is used to output the crack prediction result.

[0107] In one possible implementation, the attribute enhancement module 402 is specifically used to obtain the attribute width and attribute height of the curvature seismic attribute; determine the grid size of the scanning grid based on the attribute width and attribute height; divide the curvature seismic attribute according to the grid size to obtain multiple scanning grids; extract all seismic attribute values in the scanning grid corresponding to any seismic attribute value with any seismic attribute value as the center to obtain an attribute set; weight the attribute set according to the normalized Gaussian distance weight matrix of the scanning grid corresponding to any seismic attribute value to generate a weighted seismic attribute set; wherein the normalized Gaussian distance weight matrix is obtained based on each scanning grid; transform any seismic attribute value according to the weighted seismic attribute set to obtain an enhanced attribute value of any seismic attribute value.

[0108] In a possible implementation, the formula for transforming any earthquake attribute value in the attribute enhancement module 402 is:

[0109]

[0110] Where c represents the enhanced attribute value after any earthquake attribute value is transformed, v represents any earthquake attribute value, and B max represents the maximum value of the weighted seismic attribute set, B min Represents the minimum value of the weighted seismic attribute set.

[0111] In one possible implementation, the device also includes a normalized weight determination module, which is used to calculate the distance from each seismic attribute value to the grid center in any scanning grid to obtain the distance matrix of any scanning grid; perform a Gaussian transformation on the distance matrix to obtain the Gaussian weight matrix corresponding to any scanning grid; and normalize the Gaussian weight matrix to obtain a normalized Gaussian distance weight matrix corresponding to any scanning grid.

[0112] In one possible implementation, the calculation formula of the Gaussian weight matrix in the normalized weight determination module is:

[0113]

[0114]

[0115] Where μ i,j represents the Gaussian weight matrix, d i,j represents the distance matrix, and L represents the grid size.

[0116] In a possible implementation, the calculation formula of the normalized Gaussian distance weight matrix in the normalized weight determination module is:

[0117]

[0118] Where, ω i,j represents the normalized Gaussian distance weight matrix, μ i,j represents the Gaussian weight matrix, μ max Represents the maximum value of the Gaussian weight matrix.

[0119] The attribute-enhanced buried hill fracture zone prediction device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar, and are not described in detail in this embodiment.

[0120] Figure 5 This is a schematic diagram of the structure of the server provided in the embodiment of the present application. Figure 5As shown, the server provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the server also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0121] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that the at least one processor 501 performs the above method.

[0122] The specific implementation process of the processor 501 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0123] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0124] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0125] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0126] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0127] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0128] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0129] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0130] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0131] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0132] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0133] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0134] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0135] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for predicting buried-hill fracture zones with enhanced attributes, characterized in that: Applicable to servers, including: Obtaining curvature seismic attributes of the target exploration area; Using a Gaussian distance weighted grid scanning method, the curvature seismic attributes are enhanced to obtain enhanced seismic attributes; performing complex shear wave transform on the enhanced seismic attributes to obtain complex shear wave transform data; performing binarization processing on the complex shear wave transform data to obtain a crack prediction result; outputting the crack prediction result; The enhancing process of the curvature seismic attribute includes: Obtaining the attribute width and attribute height of the curvature seismic attribute; Determining a grid size of a scanning grid according to the attribute width and the attribute height; dividing the curvature seismic attribute according to the grid size to obtain a plurality of scanning grids; Taking any seismic attribute value in the curvature seismic attribute as the center, extracting all seismic attribute values in the scanning grid corresponding to the any seismic attribute value to obtain an attribute set; weighting the attribute set according to a normalized Gaussian distance weight matrix of the scanning grid corresponding to any seismic attribute value to generate a weighted seismic attribute set; wherein the normalized Gaussian distance weight matrix is obtained according to each scanning grid; The any seismic attribute value is transformed according to the weighted seismic attribute set to obtain an enhanced attribute value of the any seismic attribute value.

2. The method according to claim 1, characterized in that The formula for transforming any of the seismic attribute values is: Where, c represents the enhanced attribute value after the transformation of any seismic attribute value, v represents any seismic attribute value, and B max represents the maximum value of the weighted seismic attribute set, B min represents the minimum value of the weighted seismic attribute set.

3. The method according to claim 1, characterized in that The process of determining the normalized Gaussian distance weight matrix includes: In any scanning grid, the distance between each seismic attribute value and the grid center is calculated to obtain a distance matrix of the scanning grid; Performing a Gaussian transformation on the distance matrix to obtain a Gaussian weight matrix corresponding to any scanning grid; The Gaussian weight matrix is normalized to obtain a normalized Gaussian distance weight matrix corresponding to any scanning grid.

4. The method according to claim 3, characterized in that The calculation formula of the Gaussian weight matrix is: Where μ i,j represents the Gaussian weight matrix, d i,j represents the distance matrix, and L represents the grid size.

5. The method according to claim 3, characterized in that The calculation formula of the normalized Gaussian distance weight matrix is: Where, ω i,j represents the normalized Gaussian distance weight matrix, μ i,j represents the Gaussian weight matrix, μ max Represents the maximum value of the Gaussian weight matrix.

6. A property-enhanced buried hill fracture zone prediction device, characterized in that: Applicable to servers, including: A data acquisition module for acquiring curvature seismic attributes of a target exploration area; An attribute enhancement module is used to enhance the curvature seismic attribute by using a Gaussian distance weighted grid scanning method to obtain enhanced seismic attributes; a complex shear wave transform module, configured to perform complex shear wave transform on the enhanced seismic attributes to obtain complex shear wave transform data; A binarization processing module, used for performing binarization processing on the complex shear wave transform data to obtain a crack prediction result; A prediction result output module, used for outputting the crack prediction result; The attribute enhancement module is specifically used to obtain the attribute width and attribute height of the curvature seismic attribute; determine the grid size of the scanning grid according to the attribute width and the attribute height; divide the curvature seismic attribute according to the grid size to obtain multiple scanning grids; extract all seismic attribute values in the scanning grid corresponding to any seismic attribute value in the curvature seismic attribute as the center to obtain an attribute set; weight the attribute set according to the normalized Gaussian distance weight matrix of the scanning grid corresponding to any seismic attribute value to generate a weighted seismic attribute set; wherein the normalized Gaussian distance weight matrix is obtained according to each scanning grid; transform the any seismic attribute value according to the weighted seismic attribute set to obtain an enhanced attribute value of the any seismic attribute value.

7. A server, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

9. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 5 when the computer program is executed by a processor.

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