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

Abstract

The embodiment of the invention provides an attribute-enhanced buried hill fissure zone prediction method and device, equipment and a storage medium. Comprising the following steps: acquiring curvature seismic attributes of a target exploration area; performing enhancement processing on the curvature seismic attribute by using a Gaussian distance weighted grid scanning method to obtain an enhanced seismic attribute; performing complex shear wave transformation on the enhanced seismic attributes to obtain complex shear wave transformation data; performing binarization processing on the complex shear wave transformation data to obtain a crack prediction result; and outputting a crack prediction result. The effect of improving the prediction accuracy is achieved.

Description

Technical Field

[0001] The present application relates to the field of oil and gas geological exploration and development, and in particular to a property-enhanced buried hill fracture zone prediction method, device, equipment and storage medium. Background Art

[0002] In recent years, the proportion of buried hill oil and gas 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, and the quality of fracture prediction is an important factor in determining the development effect and economic benefits of buried hill oil and gas reservoirs.

[0003] In the prior art, curvature seismic attributes are an important means of predicting fractures in buried hill oil and gas reservoirs. First, the curvature seismic attributes are extracted from seismic data, and then the extracted curvature seismic attributes are linearly or nonlinearly adjusted in overall color display, and finally the adjusted curvature seismic attributes are binarized to obtain 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, which reduces the prediction effect and accuracy. Summary of the invention

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

[0006] In the first aspect, an embodiment of the present application provides a method for predicting a buried hill fracture zone with enhanced attributes, which is applied to a server, and includes: acquiring the curvature seismic attributes of the target exploration area; using a 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 a 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 according to 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 according to each scanning grid; 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 a possible implementation, the formula for transforming any seismic attribute value is:

[0009]

[0010] Where c represents the enhanced attribute value after any seismic attribute value is transformed, 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.

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

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

[0013]

[0014]

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

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

[0017]

[0018] In the formula, 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 a property-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 result.

[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, including: 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 implementations 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 buried hill fracture zone prediction method, device, equipment and storage medium provided in the embodiments of the present application can highlight local features when acting on the curvature seismic attributes by using the Gaussian distance weighted grid scanning method, so that the changes in stratum curvature related to the buried hill fractures can be clearly presented, and the influence of scattered noise points on the results is reduced by weighted processing, thereby improving the signal-to-noise ratio of the data. The enhanced seismic attributes are subjected to complex shear wave transformation, so that their multi-scale characteristics can respectively analyze the fracture distribution pattern in a large range and the local fine fracture structure. The present application can significantly improve the precision and accuracy of predicting fractures with curvature seismic attributes, and improve the prediction effect of buried hill fractures. 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 of a method for predicting a buried hill fracture zone with enhanced attributes provided in an embodiment of the present application;

[0033] Figure 2A schematic flow chart of a method for predicting a buried hill fracture zone with enhanced properties 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 a server provided in an embodiment of the present application.

[0042] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0044] First, the terms involved in this application are explained:

[0045] Buried hill fracture zone: Buried hill refers to a buried hill formed when an ancient terrain high is covered by a new sedimentary stratum. During the geological evolution process, fracture zones will be formed inside the buried hill due to tectonic stress, weathering, dissolution, etc.

[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] In order to clearly understand the technical solution of the present application, the solution of the prior art is first introduced 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. As important seepage channels and storage spaces for such reservoirs, the quality of fracture prediction is an important factor in determining the development effect and economic benefits of buried hill oil and gas reservoirs. The development of buried hill fractures is obviously controlled by the tectonic action of the formation, so the curvature seismic attribute is an important means to predict the fractures of buried hill oil and gas reservoirs. The basic process of fracture prediction using curvature seismic attributes is: first, extract the curvature seismic attributes from seismic data; secondly, make linear or nonlinear adjustments to the overall color display of the extracted curvature seismic attributes to make it more able to reflect the location and characteristics of the fractures, and finally binarize the adjusted curvature seismic attributes to obtain the fracture prediction results. At present, this method can predict the development of large and medium-scale buried hill fractures, but because the location of large-scale fracture development often forms a strong attribute value area, it has a certain suppressive effect on the surrounding small-scale fracture response. In the process of curvature seismic attribute binarization, small-scale crack information is filtered out due to the overly strong response of large-scale crack information, making it difficult to predict, which seriously reduces the effect and accuracy of curvature seismic attribute crack prediction.

[0048] In response to the above technical problems, the inventors firstly took the curvature seismic attribute as input, and then used Gaussian distance weighted grid scanning to enhance the extracted curvature seismic attribute, highlighting the weak intensity attribute information suppressed by the high intensity attribute, and then based on the enhanced attribute, used complex shear wave transform to obtain complex shear wave transform data, and finally binarized the complex shear wave transform data to extract the final fracture prediction result. The method of the present application can enhance the fracture response characteristics of the curvature seismic attribute and improve the fracture prediction accuracy of the curvature seismic attribute.

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

[0050] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. 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 of a method for predicting a buried hill fracture zone with enhanced attributes 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 is understandable that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the property-enhanced buried hill fracture zone prediction method. In other feasible embodiments of the present application, the above architecture may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently, which can be determined according to 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 used to receive the curvature seismic attributes of the target exploration area.

[0054] The processor 102 can use the 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 schematic diagram of a process flow of a method for predicting a buried hill fracture zone with enhanced attributes provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method includes:

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

[0059] Among them, the target exploration area usually refers to those specific areas with potential oil and gas resources or important in geological research, such as an area where a buried hill structure 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, and seismic detectors and other equipment are arranged in the target area. Seismic waves are excited by artificial sources. When seismic waves propagate underground and encounter different stratum interfaces, reflections occur. The detectors receive these reflected wave signals, and 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 curvature degree of the stratum interface according to its spatial morphology, and the data related to this curvature degree is the curvature seismic attribute.

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

[0062] Specifically, the size L of the scanning grid is determined, and its value range is within [3, min (attribute width, attribute height)] and is an odd number, and the spatial range corresponding to the entire curvature seismic attribute data is divided into multiple grids. For each point in the grid, the distance to the center of the grid is calculated, and the weight is calculated by the Gaussian function based on this distance. The weight of the point close to the center of the grid is larger, and the weight of the point far away is smaller. Then, the original curvature seismic attribute value of each point is multiplied by the corresponding weight to obtain the weighted attribute value of the original curvature seismic attribute value. According to the original curvature seismic attribute value and the weighted attribute value, the enhanced seismic attribute of each point is finally determined. Figure 2b A schematic diagram of enhanced seismic attributes provided in an embodiment of the present application, such as 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 transformation on the enhanced seismic attributes to obtain complex shear wave transformation 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 The schematic diagram of the effect after the complex shear wave transformation provided in the embodiment of the present application is as follows: 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 crack prediction results.

[0067] Specifically, the threshold is set based on a statistical analysis method or an adaptive threshold method. For each complex shear wave transform data, if it is greater than or equal to the threshold, it is assigned a value of 1, indicating that it is predicted to be a cracked area. If it is less than the threshold, it is assigned a value of 0, indicating that it is predicted to be a crack-free area. The complex shear wave transform data is binarized so that the entire complex shear wave transform data is converted into a binary data set containing only 1 and 0, 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 area noise, morphological filtering and other methods can be used to remove these isolated points and small area noise.

[0069] S205: Output crack prediction results.

[0070] Specifically, Figure 2d A schematic diagram comparing the crack prediction results of the prior art and the crack prediction results of the present application provided in the embodiments of the present application, wherein the left figure is the crack prediction result of the prior art, and the right figure is the crack prediction result of the present application, such as 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 complex shear wave transformation 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 predicting fractures with curvature seismic attributes and improve the prediction effect of buried hill fractures.

[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] Among them, attribute width and attribute height are key parameters describing the curvature seismic attribute in the spatial dimension, similar to the length and width of a two-dimensional image or the range size of a three-dimensional data volume on the corresponding plane. Obtaining these two parameters is the basis for the subsequent reasonable 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, firstly, the minimum value of the attribute width and the 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 size L of the grid, where the smaller the L value is, the stronger the enhancement effect is achieved.

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

[0078] Specifically, according to 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 A schematic diagram of extracting all seismic attribute values ​​in a scanning grid with any seismic attribute value as the center is provided for an embodiment of the present application. In each scanning grid, one seismic attribute value is selected as the center, and all seismic attribute values ​​in the scanning grid are attempted to form an attribute set. This operation focuses on the local grid range.

[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~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 scan grid that has been divided, the location of its grid center must be determined first. After the center is determined, the distance from each seismic attribute value contained in the grid to the grid center is calculated using the distance formula between two points. The distances from all seismic attribute values ​​in the grid to the grid center are arranged in the order of their positions in the grid, thus forming the distance matrix of the scan grid.

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

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

[0088]

[0089]

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

[0091] Exemplarily, if the distance matrix is ​​m×n, then the Gaussian weight matrix obtained after 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] In the formula, 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 seismic attribute value is transformed, 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.

[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 of 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 allocated according to the distance, effectively highlighting the importance of the area close to the center of the grid, which 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 obtains a normalized Gaussian distance weight matrix, which further standardizes the weight range and ensures that the weighted operations between different scanning grids are consistent and comparable, thereby greatly improving the accuracy and reliability of the curvature seismic attribute enhancement processing.

[0101] 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, the device comprises: a data acquisition module 401, a property 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 transformation module 403 is used to perform complex shear wave transformation on the enhanced seismic attributes to obtain complex shear wave transformation data.

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

[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 according to 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 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 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 seismic attribute value in the attribute enhancement module 402 is:

[0109]

[0110] Where c represents the enhanced attribute value after any seismic attribute value is transformed, 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.

[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 center of the grid in any scanning grid to obtain the distance matrix of any scanning grid; perform a Gaussian transformation on the distance matrix to obtain a 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 a possible implementation, the calculation formula of the Gaussian weight matrix in the normalized weight determination module is:

[0113]

[0114]

[0115] In the formula, 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] In the formula, 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, and its implementation principle and technical effect are similar, which will not be 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 at least one processor 501 executes the above method.

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

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

[0124] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0125] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or 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 also 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 above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage 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 memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general 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 a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: 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 only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

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

[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 the function is implemented in the form of 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 part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0134] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0135] Finally, it should be noted that those skilled in the art will readily conceive of 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, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only 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 attribute is enhanced to obtain an enhanced seismic attribute; Performing complex shear wave transformation on the enhanced seismic attributes to obtain complex shear wave transformation data; Binarizing the complex shear wave transform data to obtain a crack prediction result; The crack prediction result is output.

2. The method according to claim 1, characterized in that The enhancing process of the curvature seismic attribute comprises: Obtaining an attribute width and an attribute height of the curvature seismic attribute; Determining a grid size of a scanning grid according to the attribute width and the attribute height; According to the grid size, the curvature seismic attribute is segmented to obtain a plurality of scanning grids; Taking any seismic attribute value in the curvature seismic attribute as the center, extract all seismic attribute values ​​in the scanning grid corresponding to the any seismic attribute value to obtain an attribute set; According to the normalized Gaussian distance weight matrix of the scanning grid corresponding to any seismic attribute value, the attribute set is weighted 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.

3. The method according to claim 2, characterized in that The formula for transforming any of the seismic attribute values ​​is: Wherein, 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.

4. The method according to claim 2, characterized in that: The process of determining the normalized Gaussian distance weight matrix includes: In any scanning grid, the distance from each seismic attribute value to the grid center is calculated to obtain a distance matrix of the any 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.

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

6. The method according to claim 4, characterized in that The calculation formula of the normalized Gaussian distance weight matrix is: In the formula, 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.

7. A property-enhanced buried hill fracture zone prediction device, characterized in that: Applicable to servers, including: A data acquisition module, used to obtain the curvature seismic attributes of the 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 an enhanced seismic attribute; A complex shear wave transformation module, used for performing complex shear wave transformation on the enhanced seismic attributes to obtain complex shear wave transformation data; A binarization processing module, used for binarizing the complex shear wave transform data to obtain a crack prediction result; The prediction result output module is used to output the crack prediction result.

8. 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 6.

9. 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 6 when executed by a processor.

10. 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 6 when being executed by a processor.

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