Fracture development distribution prediction method and system for multi-seismic data fusion reconstruction

Abstract

The invention relates to a fracture development distribution prediction method and system for multi-seismic data fusion reconstruction, and belongs to the technical field of oil and gas exploration and development. The method comprises the steps of obtaining optimized superimposed seismic data based on preprocessing of original seismic data; obtaining composite seismic attribute data based on composite processing of the optimized superimposed seismic data; and predicting fracture development distribution based on the composite seismic attribute data. The method comprises the following steps: performing preprocessing (multilayer decomposition processing, extraction and optimization operation) on original seismic data, screening out optimized seismic data, performing multi-seismic attribute privilege extraction and fusion reconstruction on the optimized seismic data to obtain composite seismic attribute data, and performing prediction identification by taking the data as a basis for predicting fracture development distribution. In the process, the identification and prediction advantages of each seismic attribute are mixed, and the problems of instability and multiplicity of solutions existing in the method in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and natural gas exploration and development, and in particular relates to a method and system for predicting fracture development distribution by fusion and reconstruction of multiple seismic data. Background Art

[0002] Fractures in oil reservoirs, as channels for oil and gas migration and accumulation, are an important foundation for reservoir prediction in a work area. Currently, the total geological resources of oil reservoirs in major basins in China range from (106.7 to 111.5) × 108 tons, and reservoir productivity is primarily affected by fracture development. Seismic data acquired through artificial seismic analysis can reflect the extent of fracture development, and seismic attributes derived from seismic data processing can further identify fracture characteristics. For example, Yang Guoquan et al. used a 5×5 grid cell to fit a quadratic surface and derived a curvature calculation method that has demonstrated excellent application in the identification of large-scale faults. Qin Si et al. increased the stability of the variance attribute by increasing the calculation time window of the variance algorithm, achieving significant results in the identification of mesoscale faults. Xu Hongxia et al. used curvature attributes to characterize the distribution of caves and karst gullies in carbonate rocks, which also performed well in the identification of small-scale fractures.

[0003] However, single-attribute fracture prediction methods highlight few fracture characteristics, are complex, and lack accuracy. Single-attribute identification suffers from instability and multiple solutions. Fusion of multiple seismic attributes can effectively address these issues, but current research is limited, resulting in incomplete fracture identification and low accuracy. Therefore, it is urgent to develop fracture development and distribution prediction methods that utilize multi-seismic data fusion and reconstruction to address the instability and multiple solutions inherent to existing methods. Summary of the Invention

[0004] To solve the above problems, the present invention provides a method and system for predicting fracture development and distribution by fusion and reconstruction of multiple seismic data.

[0005] The first object of the present invention is to provide a method for predicting fracture development distribution by fusion and reconstruction of multiple seismic data, the method comprising:

[0006] Based on the preprocessing of the original seismic data, the superimposed seismic data can be optimized;

[0007] Based on the composite processing of optimized superimposed seismic data, composite seismic attribute data is obtained;

[0008] Predict fracture development and distribution based on composite seismic attribute data.

[0009] In an embodiment of the present invention, the preprocessing based on the original seismic data to optimize the superimposed seismic data includes:

[0010] Perform multi-layer decomposition processing on the original seismic data;

[0011] Superimpose the seismic data of different levels obtained from multi-layer decomposition processing;

[0012] Based on the original seismic data, the superposition results and the spectrum analysis method, the superimposed seismic data are optimized.

[0013] In the embodiment of the present invention, the specific operations of the multi-layer decomposition process are as follows:

[0014] Perform radial filtering and downsampling on the original seismic data;

[0015] Performing step-by-step radial filtering and directional filtering on the hierarchical image obtained by radial filtering and downsampling;

[0016] Based on the results of step-by-step radial filtering and directional filtering, seismic data of different levels are obtained.

[0017] In the embodiment of the present invention, the directional filter used in the directional filtering is as follows:

[0018] G k =∑ m ∑ n W k G k-1 (2x+m,2y+n)

[0019]

[0020] Among them, G k represents the kth layer of the data volume hierarchical decomposition, x, y represent the image pixel arrays at different levels, and W k Represents a window function with low-pass characteristics;

[0021] f θ (x, y) is the function of the controllable filter in the θ direction, which is determined by the interpolation function k in the θ direction. j Basis functions in the (θ) and θ directions The linear combination is obtained, where M is the logarithm of the basis function and the interpolation function.

[0022] In an embodiment of the present invention, the composite processing based on the optimized superimposed seismic data to obtain composite seismic attribute data includes:

[0023] Extract multiple attributes from optimized superimposed seismic data;

[0024] Calculate the fusion weights of different seismic attributes for the extracted seismic data;

[0025] Perform linear superposition fusion calculation on the results obtained by fusion weight calculation;

[0026] Based on the linear superposition fusion calculation results, composite seismic attribute data are obtained.

[0027] In the embodiment of the present invention, the multiple attributes extracted from the optimized superimposed seismic data are curvature, variance, and mean curvature. The extraction formulas for the single curvature, variance, and mean curvature are as follows:

[0028]

[0029] Among them, K Cur represents the curvature property; x represents the horizontal coordinate of the curve; y represents the vertical coordinate of the curve; K Var represents the earthquake variance attribute; j represents the variance calculation time; i represents the number of seismic traces, whose value is a real number; I represents the number of layers and fault traces selected when calculating the variance; L represents the time window length under the variance calculation time; w represents the triangular weighted function of the earthquake variance body, whose interval range is [0,1]; z represents the average amplitude under the variance calculation time; K Amp represents the average curvature attribute; A represents the amplitude value on the seismic slice.

[0030] In an embodiment of the present invention, the step of calculating the fusion weights of different seismic attributes on the extracted seismic data includes:

[0031] Normalize the extracted seismic data according to each seismic attribute;

[0032] A constraint weight is given to each seismic attribute, wherein the constraint weight is the ratio of the fault area described by the seismic attribute of the designated target layer slice to the fault area known from existing geology.

[0033] In the embodiment of the present invention, the calculation formula for performing linear superposition fusion calculation on the results obtained by fusion weight calculation is as follows:

[0034]

[0035] Among them, ω i is the constraint weight, ω1, ω2, ω3 are the constraint weights corresponding to curvature, mean curvature, and variance respectively; r i is the correlation between different earthquake attributes and large faults; S i is the area of ​​large faults with different earthquake attributes; S is the area of ​​large faults known geologically.

[0036] A second object of the present invention is to provide a fracture development distribution prediction system based on multi-seismic data fusion reconstruction, the system comprising:

[0037] The preprocessing module is used for preprocessing the original seismic data to optimize the superimposed seismic data;

[0038] The composite processing module is used for composite processing based on optimized superimposed seismic data to obtain composite seismic attribute data;

[0039] The prediction module is used to predict the development and distribution of fractures based on composite seismic attribute data.

[0040] In an embodiment of the present invention, the pre-processing module includes a navigation processing sub-module, a superposition sub-module and an analysis sub-module;

[0041] The navigation processing submodule is used to perform multi-layer decomposition processing on the original seismic data;

[0042] The superposition submodule is used to superimpose seismic data of different levels obtained by multi-layer decomposition processing;

[0043] The analysis submodule is used to optimize the superimposed seismic data based on the original seismic data, the superimposed results and the spectrum analysis method.

[0044] In the embodiment of the present invention, the composite processing module includes an extraction submodule, a fusion weight calculation submodule, a linear superposition fusion calculation submodule and an acquisition submodule;

[0045] The extraction submodule extracts multiple attributes from the optimized superimposed seismic data;

[0046] The fusion weight calculation submodule is used to calculate the fusion weights of different seismic attributes on the extracted seismic data;

[0047] The linear superposition fusion calculation submodule is used to perform linear superposition fusion calculation on the results obtained by fusion weight calculation;

[0048] The acquisition submodule is used to obtain composite seismic attribute data based on linear superposition fusion calculation results.

[0049] A third object of the present invention is to provide an electronic device, comprising: a processor coupled to a memory;

[0050] The memory is used to store computer programs;

[0051] The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the method described above.

[0052] A fourth object of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, and when the program or instruction is executed on a computer, the computer executes the method described above.

[0053] Beneficial effects of the present invention:

[0054] The present invention provides a method and system for predicting the development and distribution of fractures by fusion and reconstruction of multiple seismic data. The method preprocesses the original seismic data (multi-layer decomposition processing, extraction and optimization operations), screens out optimized seismic data, and then performs weighting and fusion reconstruction of multiple seismic attributes on the optimized seismic data to obtain composite seismic attribute data. This data is then used as the basis for predicting the development and distribution of fractures and performing prediction and identification. This process combines the recognition and prediction advantages of various seismic attributes and solves the problems of instability and multiple solutions existing in the existing background technology methods.

[0055] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 A flowchart of a method for predicting fracture development distribution by fusion and reconstruction of multiple seismic data according to an embodiment of the present invention is shown;

[0058] Figure 2 A schematic diagram showing the results of predicting crack development using a single curvature using existing methods;

[0059] Figure 3 A schematic diagram showing the results of using the existing method to predict the crack development through a single variance;

[0060] Figure 4 A schematic diagram showing the results of using the existing method to predict the development of cracks using a single average curvature;

[0061] Figure 5 A schematic diagram showing the result of predicting crack development using the crack development distribution method provided by an embodiment of the present invention;

[0062] Figure 6 A framework diagram of a fracture development distribution prediction system based on multi-seismic data fusion and reconstruction according to an embodiment of the present invention is shown;

[0063] Figure 7 A framework diagram of an electronic device according to an embodiment of the present invention is shown;

[0064] In the picture:

[0065] Preprocessing module 1; composite processing module 2; prediction module 3; electronic device 300; processor 301; memory 302. DETAILED DESCRIPTION

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0067] like Figure 1 As shown, a method for predicting fracture development distribution by fusion and reconstruction of multiple seismic data according to an embodiment of the present invention includes:

[0068] Step S1, based on the preprocessing of the original seismic data, obtaining optimized superimposed seismic data;

[0069] Step S2, obtaining composite seismic attribute data based on composite processing of optimized superimposed seismic data;

[0070] Step S3: predicting the distribution of fracture development based on the composite seismic attribute data.

[0071] In step S1, the preprocessing of the original seismic data to optimize the superimposed seismic data includes:

[0072] Step A1, performing multi-layer decomposition processing on the original seismic data;

[0073] Step A2, superimposing the seismic data of different levels obtained by multi-layer decomposition processing;

[0074] Step A3: Based on the original seismic data, the superposition results and the spectrum analysis method, the superimposed seismic data is optimized.

[0075] In step A1, to obtain seismic data containing information at different frequencies, the raw seismic data must first be subjected to a multi-layer decomposition process. Navigation volume hierarchical decomposition is an image decomposition technique that uses multiple resolutions to interpret the multiscale structure of an image. This method uses different radial and directional filters designed based on geological characteristics to perform a multi-level decomposition of the raw seismic data.

[0076] Specifically, the specific operations of the multi-layer decomposition process are as follows:

[0077] Step B1, radial filtering and downsampling processing are performed on the original seismic data, except for the bottom layer image;

[0078] Step B2: performing radial filtering and directional filtering on the hierarchical images obtained by radial filtering and downsampling. That is, the hierarchical images of each level are obtained by radially filtering the hierarchical image of the previous level through the radial filter, and then passing the filtered results through the directional filter for directional filtering.

[0079] The radial filter and the directional filter are designed according to the geological characteristics. Therefore, different radial filters and directional filters are designed for different geological characteristics. The expressions of the radial filter and the directional filter are as follows:

[0080] G k =∑ m ∑ n W k G k-1 (2x+m,2y+n) (1)

[0081]

[0082] In formula (1), G k represents the kth layer of the data volume hierarchical decomposition, x, y represent the image pixel arrays at different levels, and W k Represents a window function with low-pass characteristics;

[0083] In formula (2), f θ (x, y) is the function of the controllable filter in the θ direction, which is determined by the interpolation function k in the θ direction. j Basis functions in the (θ) and θ directions The linear combination is obtained, where M is the logarithm of the basis function and the interpolation function.

[0084] Step B3: Based on the results of the step-by-step radial filtering and directional filtering processing, seismic data of different levels are obtained, that is, after the step-by-step radial filtering and directional filtering processing, seismic data of different levels are obtained.

[0085] In step A2, the seismic data of different levels obtained by the multi-layer decomposition processing are superimposed, that is, the seismic multi-layer decomposition processing results are superimposed to obtain higher resolution seismic data;

[0086] In the embodiment of the present invention, the seismic data volume processed in step A1 is divided into 7 levels, and a total of 28 combination modes are stacked in sequence for selection.

[0087] In step A3, the superimposed seismic data is optimized based on the original seismic data, the superimposed result and the spectrum analysis method, that is, the original seismic data and the superimposed seismic data are statistically analyzed using the spectrum analysis method to obtain the corresponding center frequency f of the original seismic data and the superimposed seismic data. m , main frequency fb and the main frequency range Δf for comparison and selection using the following conditions:

[0088] The spectrum data of the optimal seismic data needs to meet the following setting conditions of formula (3):

[0089]

[0090] In formula (3), f m (L), f b (L) and Δf(L) are the center frequency, main frequency and main frequency range corresponding to the Lth type of superimposed seismic data, respectively. m 、f b and Δf are the center frequency, main frequency and main frequency range corresponding to the original seismic data image, respectively.

[0091] By optimizing according to the above conditions, it is possible to select superimposed seismic data with a relatively high main frequency and a center frequency that is relatively consistent with the original seismic data image while ensuring that the main frequency range remains basically unchanged, that is, the optimal superimposed seismic data that is closest to the original seismic data image and contains the most effective information (the optimized superimposed seismic data finally obtained).

[0092] In step S2, the composite processing based on the optimized superimposed seismic data to obtain composite seismic attribute data includes:

[0093] Step C1, extracting multiple attributes from the optimized superimposed seismic data;

[0094] Step C2, calculating the fusion weights of different seismic attributes for the extracted seismic data;

[0095] Step C3: performing linear superposition fusion calculation on the results obtained by fusion weight calculation;

[0096] Step C4: Based on the linear superposition fusion calculation results, composite seismic attribute data is obtained.

[0097] In step C1, since different seismic attributes have different crack identification ranges and different accuracies, they are suitable for identifying different crack characteristics. For example, the curvature attribute can characterize the degree of crack development and is most sensitive to cracks with flexure and fold development, which is suitable for identifying large-scale faults. The variance attribute describes cracks through the similarity of adjacent seismic signals and is more sensitive to cracks caused by faults with strong discontinuities, which is suitable for identifying medium-scale faults. The average curvature attribute represents the abnormality of the seismic attribute in amplitude and is more sensitive to tiny cracks, which is suitable for identifying small-scale cracks. Therefore, multiple attribute extraction is performed. The extraction formulas of single curvature, variance and average curvature are shown in formula (4):

[0098]

[0099] In formula (4), K Cur represents the curvature property; x represents the horizontal coordinate of the curve; y represents the vertical coordinate of the curve; K Var represents the earthquake variance attribute; j represents the variance calculation time; i represents the number of seismic traces, whose value is a real number; I represents the number of layers and fault traces selected when calculating the variance; L represents the time window length under the variance calculation time; w represents the triangular weighting function of the earthquake variance body, whose interval range is [0, 1]; z represents the average amplitude under the variance calculation time; K Amp represents the average curvature attribute; A represents the amplitude value on the seismic slice.

[0100] In order to combine the recognition advantages of various seismic attributes and avoid recognition disadvantages, the single seismic attribute extracted in step C1 is fused and calculated, that is, the operations of steps C2 and C3 are performed:

[0101] Wherein, step C2, calculating the fusion weights of different seismic attributes on the extracted seismic data, includes:

[0102] Step D1: normalize the extracted seismic data according to each seismic attribute to ensure its additivity;

[0103] Step D2: Assign a constraint weight to each seismic attribute, wherein the constraint weight is the ratio of the fault area described by the seismic attribute of the specified target layer slice to the fault area of ​​the existing geological knowledge. The closer the ratio is to 1, the more consistent the seismic attribute is with the actual knowledge and the higher the correlation is. The constraint weight is represented by the fracture-related filling rate.

[0104] In step C3, the calculation formula for linear superposition fusion calculation of the results obtained by fusion weight calculation is shown in formula (5):

[0105]

[0106] In formula (5), ω i is the constraint weight, ω1, ω2, ω3 are the constraint weights corresponding to curvature, mean curvature, and variance respectively; r i is the correlation between different earthquake attributes and large faults; S i is the area of ​​large faults with different earthquake attributes; S is the area of ​​large faults known geologically.

[0107] In step C4, the composite seismic attribute data is obtained based on the linear superposition fusion calculation results, that is, the composite seismic attribute data is obtained based on the calculation results of step C3.

[0108] Large-scale faults are usually caused by regional tectonic movements or diagenesis, with strike extensions ranging from hundreds of meters to tens of kilometers. They often appear in seismic data as event axis misalignment or large deformation, with strong amplitude anomalies; medium-scale faults are usually caused by regional tectonic movements or folding and faulting, with strike extensions ranging from tens to hundreds of meters. They often appear in seismic data as obvious folds, chaotic reflections, and occasional "beaded" strong reflections; small-scale cracks are usually caused by folding, faulting or sedimentation and diagenesis, with strike extensions ranging from several meters to more than ten meters. They often appear in seismic data as chaotic reflection characteristics, with small "beaded" reflections appearing locally.

[0109] Based on the above common sense of seismic data, in step S3, the fracture development distribution is predicted based on the composite seismic attribute data, that is, the composite seismic attributes are multi-scale classified according to the actual large-scale faults, medium-scale faults, and small-scale fractures, and a fracture classification standard for the composite seismic attributes is established based on this classification data. Finally, multi-scale fracture identification is performed according to the classification standard, that is, the fracture development distribution is predicted;

[0110] For example, in an embodiment of the present invention:

[0111] The following classification criteria are established in the composite seismic attributes: values ​​between 0.9 and 1 are considered large-scale faults, between 0.75 and 0.9 are considered medium-scale faults, between 0.5 and 0.75 are considered small-scale fractures, and values ​​less than 0.5 are not identified as fractures.

[0112] Finally, multi-scale crack identification is performed using this classification standard.

[0113] Taking a certain actual work area as an example, the fracture development and the distribution of the curvature, variance, and mean curvature seismic attributes are as follows: Figure 2 、 Figure 3 、 Figure 4 As shown. Actual cases further verify that under different geological conditions, the fracture development characteristics are different, and the prediction accuracy of single curvature, variance, and average curvature attributes is also different. It is necessary to comprehensively consider the curvature, variance, and average curvature attributes to accurately predict the development of actual fractures in all directions. To this end, the method proposed in this embodiment is to fuse and reconstruct multiple seismic attributes such as curvature, variance, and average curvature, and predict the fracture development characteristics through the fused data. The results are as follows Figure 5 As shown. Figure 5 In the figure, the different values ​​are displayed in different colors, which can clearly identify the development of three scales of cracks.

[0114] like Figure 6 As shown, a fracture development distribution prediction system based on multi-seismic data fusion reconstruction according to an embodiment of the present invention includes:

[0115] Preprocessing module 1 is used for preprocessing based on the original seismic data to obtain optimized superimposed seismic data;

[0116] The composite processing module 2 is used for composite processing based on optimized superimposed seismic data to obtain composite seismic attribute data;

[0117] The prediction module 3 is used to predict the development and distribution of fractures based on the composite seismic attribute data.

[0118] In the embodiment of the present invention, the pre-processing module 1 includes a navigation processing sub-module, a superposition sub-module and an analysis sub-module;

[0119] The navigation processing submodule is used to perform multi-layer decomposition processing on the original seismic data;

[0120] The superposition submodule is used to superimpose seismic data of different levels obtained by multi-layer decomposition processing;

[0121] The analysis submodule is used to optimize the superimposed seismic data based on the original seismic data, the superimposed results and the spectrum analysis method.

[0122] In the embodiment of the present invention, the composite processing module 2 includes an extraction submodule, a fusion weight calculation submodule, a linear superposition fusion calculation submodule and an acquisition submodule;

[0123] The extraction submodule extracts multiple attributes from the optimized superimposed seismic data;

[0124] The fusion weight calculation submodule is used to calculate the fusion weights of different seismic attributes on the extracted seismic data;

[0125] The linear superposition fusion calculation submodule is used to perform linear superposition fusion calculation on the results obtained by fusion weight calculation;

[0126] The acquisition submodule is used to obtain composite seismic attributes based on linear superposition and fusion calculation results.

[0127] like Figure 7 As shown, some embodiments of the present invention provide an electronic device, the electronic device 300 including: a processor 301, the processor 301 coupled to a memory 302;

[0128] The memory 302 is used to store computer programs;

[0129] The processor 301 is configured to execute the computer program stored in the memory 302 , so that the electronic device executes the method described in the above embodiment.

[0130] In certain embodiments of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a program or instruction. When the program or instruction is executed on a computer, the computer executes the method described in the above embodiments.

[0131] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, an electronic device, or a device.

[0132] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fracture development distribution prediction method based on multi-seismic data fusion reconstruction, characterized in that: include: Based on the preprocessing of the original seismic data, the superimposed seismic data can be optimized; Based on the composite processing of optimized superimposed seismic data, composite seismic attribute data is obtained; Predict fracture development and distribution based on composite seismic attribute data.

2. The method for predicting fracture development and distribution based on multi-seismic data fusion and reconstruction according to claim 1, characterized in that: The preprocessing based on the original seismic data to optimize the superimposed seismic data includes: Perform multi-layer decomposition processing on the original seismic data; Superimpose the seismic data of different levels obtained from multi-layer decomposition processing; Based on the original seismic data, the superposition results and the spectrum analysis method, the superimposed seismic data are optimized.

3. The method for predicting fracture development and distribution based on multi-seismic data fusion reconstruction according to claim 2, characterized in that: The specific operations of the multi-layer decomposition process are as follows: Perform radial filtering and downsampling on the original seismic data; Performing step-by-step radial filtering and directional filtering on the hierarchical image obtained by radial filtering and downsampling; Based on the results of step-by-step radial filtering and directional filtering, seismic data of different levels are obtained.

4. The fracture development distribution prediction method based on multi-seismic data fusion reconstruction according to claim 3 is characterized in that: The directional filter used in the directional filtering is as follows: G k =S m S n W k G k-1 (2x+m,2y+n) Among them, G k represents the kth layer of the data volume hierarchical decomposition, x, y represent the pixel arrays of different levels of images, W k Represents a window function with low-pass characteristics; f θ (x, y) is the function of the controllable filter in the θ direction, which is determined by the interpolation function k in the θ direction. j Basis functions in the (θ) and θ directions The linear combination is obtained, where M is the logarithm of the basis function and the interpolation function.

5. The method for predicting fracture development and distribution by fusion and reconstruction of multiple seismic data according to claim 1, characterized in that: The composite processing based on the optimized superimposed seismic data to obtain composite seismic attribute data includes: Extract multiple attributes from optimized superimposed seismic data; Calculate the fusion weights of different seismic attributes for the extracted seismic data; Perform linear superposition fusion calculation on the results obtained by fusion weight calculation; Based on the linear superposition fusion calculation results, composite seismic attribute data are obtained.

6. The method for predicting fracture development and distribution by fusion and reconstruction of multiple seismic data according to claim 5, characterized in that: The multiple attributes extracted from the optimized superimposed seismic data are curvature, variance and mean curvature. The extraction formulas of the single curvature, variance and mean curvature are as follows: Among them, K Cur represents the curvature property; x represents the horizontal coordinate of the curve; y represents the vertical coordinate of the curve; K Var represents the earthquake variance attribute; j represents the variance calculation time; i represents the number of seismic traces, whose value is a real number; I represents the number of layers and fault traces selected when calculating the variance; L represents the time window length under the variance calculation time; w represents the triangular weighted function of the earthquake variance body, whose interval range is [0,1]; z represents the average amplitude under the variance calculation time; K Amp represents the average curvature attribute; A represents the amplitude value on the seismic slice.

7. The method for predicting fracture development and distribution by fusion and reconstruction of multiple seismic data according to claim 5, characterized in that: The calculation of fusion weights of different seismic attributes for the extracted seismic data includes: Normalize the extracted seismic data according to each seismic attribute; A constraint weight is given to each seismic attribute, wherein the constraint weight is the ratio of the fault area described by the seismic attribute of the designated target layer slice to the fault area known from existing geology.

8. The method for predicting fracture development and distribution by fusion and reconstruction of multiple seismic data according to any one of claims 6-7, characterized in that: The calculation formula for linear superposition fusion calculation of the results obtained by fusion weight calculation is as follows: Among them, ω i is the constraint weight, ω1, ω2, ω3 are the constraint weights corresponding to curvature, mean curvature, and variance respectively; r i is the correlation between different earthquake attributes and large faults; S i is the area of ​​large faults with different earthquake attributes; S is the area of ​​large faults known geologically.

9. A fracture development and distribution prediction system based on multi-seismic data fusion reconstruction, characterized by: include: The preprocessing module is used for preprocessing the original seismic data to optimize the superimposed seismic data; The composite processing module is used for composite processing based on optimized superimposed seismic data to obtain composite seismic attribute data; The prediction module is used to predict the development and distribution of fractures based on composite seismic attribute data.

10. The fracture development and distribution prediction system based on multi-seismic data fusion and reconstruction according to claim 9 is characterized in that: The pre-processing module includes a navigation processing sub-module, a superposition sub-module and an analysis sub-module; The navigation processing submodule is used to perform multi-layer decomposition processing on the original seismic data; The superposition submodule is used to superimpose seismic data of different levels obtained by multi-layer decomposition processing; The analysis submodule is used to optimize the superimposed seismic data based on the original seismic data, the superimposed results and the spectrum analysis method.

11. The fracture development and distribution prediction system based on multi-seismic data fusion and reconstruction according to any one of claims 9-10, characterized in that: The composite processing module includes an extraction submodule, a fusion weight calculation submodule, a linear superposition fusion calculation submodule and an acquisition submodule; The extraction submodule extracts multiple attributes from the optimized superimposed seismic data; The fusion weight calculation submodule is used to calculate the fusion weights of different seismic attributes on the extracted seismic data; The linear superposition fusion calculation submodule is used to perform linear superposition fusion calculation on the results obtained by fusion weight calculation; The acquisition submodule is used to obtain composite seismic attribute data based on linear superposition fusion calculation results.

12. An electronic device, characterized in that: include: a processor coupled to the memory; The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the method according to any one of claims 1 to 9.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program or instruction, and when the program or instruction is executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 9.

Images