Multi-well and structure combined constraint chromatography kernel function construction method and device

Abstract

The invention relates to the technical field of geophysical exploration, and particularly discloses a multi-well and structure combined constraint chromatography kernel function construction method and device, and the method comprises the steps: sequentially carrying out the resampling and frequency reduction processing of each well speed data, and obtaining the processed multi-well speed data; establishing a multi-well constraint chromatography kernel function based on the processed multi-well speed data and a conventional chromatography kernel function; extracting construction information based on the seismic imaging data; and the structure information and the multi-well constraint chromatography kernel function are fused to form a multi-well and structure combined constraint chromatography kernel function. According to the multi-well and structure combined constraint chromatography kernel function construction method provided by the invention, by introducing prior information such as well data and structure forms, a chromatography kernel function which is more accurate and reasonable, lower in ill-conditioned degree and subjected to multi-well and structure combined constraint is established, and a more accurate speed model can be obtained by solving the kernel function; the problems that a conventional kernel function inversion result is not ideal and the speed inversion precision is low are solved.

Description

Technical Field

[0001] The invention relates to the technical field of geophysical exploration, and in particular to a method and device for constructing a tomographic kernel function with joint constraints of multiple wells and structures. Background Art

[0002] In the field of seismic data imaging, the exploration of underground media is, in a sense, the exploration of underground stratum velocity. How to accurately obtain underground stratum velocity is the key. At present, in the field of deep domain velocity modeling in seismic data processing, the most commonly used and effective method is tomographic velocity modeling technology, which uses ray tracing to achieve underground illumination, establishes tomographic kernel functions, and realizes the inversion of underground stratum velocity through various solution methods. However, in practical applications, since the established tomographic kernel function belongs to a giant sparse matrix, it has a high degree of pathology, and the direct solution has strong multi-solution and insufficient stability, so the velocity inversion results are usually not ideal and the velocity inversion accuracy is low.

[0003] Based on this technical background, the present invention studies a method and device for constructing a tomographic kernel function with joint constraints of multiple wells and structures. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method and device for constructing a tomographic kernel function with joint constraints of multiple wells and structures. The method introduces prior information such as well data and structural morphology to establish a more accurate and reasonable tomographic kernel function with a lower degree of pathology and joint guidance constraints of multiple wells and structures. Solving the kernel function can obtain a more accurate velocity model, which solves the problem that the inversion results of conventional kernel functions are not ideal and the velocity inversion accuracy is low.

[0005] In order to achieve the above-mentioned object, the first aspect of the present invention provides a method for constructing a tomographic kernel function with joint constraints of multiple wells and structures, comprising:

[0006] Resampling and frequency reduction processing are performed on the velocity data of each well in turn to obtain processed multi-well velocity data;

[0007] Establishing a multi-well constrained tomographic kernel function based on the processed multi-well velocity data and a conventional tomographic kernel function;

[0008] Extracting structural information based on seismic imaging data;

[0009] The structural information and the multi-well constrained tomographic kernel function are combined to form a tomographic kernel function with joint constraints of multiple wells and structures.

[0010] A second aspect of the present invention provides a tomographic kernel function construction device for multiple wells and structure joint constraints, comprising:

[0011] A data processing module is used to sequentially resample and downsample the velocity data of each well to obtain processed multi-well velocity data;

[0012] A multi-well kernel function establishment module, used for establishing a multi-well constrained tomographic kernel function based on the processed multi-well velocity data and a conventional tomographic kernel function;

[0013] A structural information extraction module, used to extract structural information based on seismic imaging data;

[0014] The joint constraint kernel function establishment module is used to merge the structural information and the multi-well constraint tomographic kernel function to form a multi-well and structural joint constraint tomographic kernel function.

[0015] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0016] A memory storing executable instructions;

[0017] A processor runs the executable instructions in the memory to implement the method for constructing a tomographic kernel function with multiple wells and structure joint constraints as described in the first aspect.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for constructing a tomographic kernel function with joint constraints of multiple wells and structures as described in the first aspect.

[0019] The beneficial effects of the present invention include:

[0020] (1) The method for constructing a tomographic kernel function with joint constraints of multiple wells and structures proposed in the present invention introduces prior information such as well data and structural morphology to establish a more accurate and reasonable tomographic kernel function with a lower degree of pathology and joint guidance constraints of multiple wells and structures. Solving the kernel function can obtain a more accurate velocity model, which solves the problem that the inversion results of conventional kernel functions are not ideal and the velocity inversion accuracy is low.

[0021] (2) The tomographic kernel function construction method for multiple wells and structural joint constraints proposed in the present invention obtains the well velocity distance with the same sampling rate as the seismic data through resampling, and then performs frequency reduction processing, which effectively removes the high-frequency jitter in the well velocity curve and improves the velocity inversion accuracy.

[0022] (3) The method for constructing a tomographic kernel function with joint constraints of multiple wells and structures proposed in the present invention extracts structural information based on seismic imaging data, integrates the preprocessed well velocity data and structural information with the conventional tomographic kernel function, and forms a tomographic kernel function with joint guidance constraints of multiple wells and structures, thereby avoiding the problem of strong multi-solution and insufficient stability of conventional tomographic kernel functions.

[0023] (4) The method for constructing a tomographic kernel function with multiple well and structural joint constraints proposed in the present invention has a high degree of consistency between the velocity update and the structural morphology, and the update is close to the well velocity data, and the well constraint effect is obvious; compared with the conventional tomographic kernel function, the difference between the imaging depth and the well calibration depth is significantly reduced, which proves the effectiveness of the method of the present invention.

[0024] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings.

[0026] Figure 1 It is a flow chart of the method for constructing a tomographic kernel function with joint constraints of multiple wells and structures proposed in the present invention.

[0027] Figure 2 This is a schematic diagram of the original well velocity curve input in a specific implementation of the tomographic kernel function construction method for multiple wells and structural joint constraints proposed by the present invention.

[0028] Figure 3 It is a well velocity curve after resampling in a specific implementation of the tomographic kernel function construction method for multiple wells and structure joint constraints proposed by the present invention.

[0029] Figure 4 A well velocity curve is obtained by frequency reduction using a 10-point smoothing operator in a specific implementation of the tomographic kernel function construction method for multiple wells and structural joint constraints proposed by the present invention.

[0030] Figure 5 It is the velocity update amount obtained in a specific implementation of the tomographic kernel function construction method for multiple wells and structure joint constraints proposed by the present invention.

[0031] Figure 6 This is an imaging result corresponding to a velocity model obtained by a conventional method in a specific implementation of the tomographic kernel function construction method for multiple wells and structures jointly constrained by the present invention.

[0032] Figure 7 The imaging result corresponding to the velocity model obtained by the method of the present invention is obtained in a specific implementation manner of the tomographic kernel function construction method for multiple wells and structures jointly constrained by the present invention. DETAILED DESCRIPTION

[0033] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0034] The present invention provides a method for constructing a tomographic kernel function with multiple wells and structure joint constraints, such as Figure 1 As shown, including:

[0035] Resampling and frequency reduction processing are performed on the velocity data of each well in turn to obtain processed multi-well velocity data;

[0036] A multi-well constrained tomographic kernel function is established based on the processed multi-well velocity data and the conventional tomographic kernel function;

[0037] Extracting structural information based on seismic imaging data;

[0038] The structural information and the multi-well constrained tomographic kernel function are integrated to form a tomographic kernel function with joint constraints of multiple wells and structures.

[0039] In the present invention, by introducing prior information such as well data and structural morphology, a more accurate and reasonable tomographic kernel function with a lower degree of pathology and joint guidance constraints of multiple wells and structures is established. Solving the kernel function can obtain a more accurate velocity model, which solves the problem that the inversion results of conventional kernel functions are not ideal and the velocity inversion accuracy is low.

[0040] According to the present invention, the formula used for resampling is:

[0041] W re (k) = resample[W(i)];

[0042] Among them, W(i), W re (k) are the single well velocity data before and after resampling, i is the sampling point number of the single well velocity data before resampling, k is the sampling point number of the single well velocity data after resampling, and resample[·] is the resampling filter;

[0043] Set N old 、N new are the lengths of well data before and after sampling, i∈(1,N old ), k∈(1,N new ).

[0044] In the present invention, the well velocity data distance with the same sampling rate as the seismic data is obtained by resampling, and then frequency reduction processing is performed to effectively remove the high-frequency jitter in the well velocity curve and improve the velocity inversion accuracy.

[0045] According to the present invention, the formula used for frequency reduction processing is:

[0046]

[0047] Where n is the number of points of the smoothing operator, W low-f (k) is the single well velocity data after frequency reduction processing;

[0048] The single well velocity data after frequency reduction processing is the processed single well velocity data.

[0049] According to the present invention, the matrix expression of the processed multi-well velocity data is:

[0050]

[0051] Where m is the number of wells, and o, p, and q are the three-dimensional lengths of the tomographic grid model.

[0052] Preferably, the expression of the multi-well constrained tomography kernel function is:

[0053]

[0054] Among them, l is the sensitivity matrix element in the conventional tomographic kernel function, and nray is the number of rays in the conventional tomographic kernel function.

[0055] According to the present invention, the formula used to extract structural information based on seismic imaging data is:

[0056] C(o,p,q)=stru_extract[S(o,p,q)];

[0057] Where C is the extracted structural information, S is the seismic imaging data, and stru_extract[g] is the structural extraction algorithm.

[0058] Preferably, the expression of the tomographic kernel function of the multi-well and structure joint constraint is:

[0059]

[0060] Where Δs is the speed or slowness update to be sought, Δs well is the error between well velocity and seismic velocity, Δt is the residual time difference, is the simplified expression of the multi-well constrained tomography kernel function, L and Lwell are the conventional tomography kernel function and the multi-well constrained tomography kernel function, respectively.

[0061] In the present invention, structural information is extracted based on seismic imaging data, and the preprocessed well velocity data and structural information are integrated with the conventional tomographic kernel function to form a tomographic kernel function with joint guidance constraints for multiple wells and structures, thereby avoiding the problems of strong multi-solution and insufficient stability of the conventional tomographic kernel function.

[0062] In the present invention, the obtained velocity update is highly consistent with the structural morphology, and the update is close to the well velocity data, and the well constraint effect is obvious; compared with the conventional tomographic kernel function, the difference between the imaging depth and the well calibration depth is significantly reduced, which proves the effectiveness of the method of the present invention.

[0063] The present invention will be described in more detail below by way of examples.

[0064] Embodiment 1:

[0065] like Figure 1-7 As shown, this embodiment proposes a method for constructing a tomographic kernel function with joint constraints of multiple wells and structures. The method first preprocesses the well velocity curve, obtains the well velocity distance with the same sampling rate as the seismic data by resampling, and then performs frequency reduction processing to remove the high-frequency jitter in the well velocity curve to obtain the preprocessed well velocity data; on this basis, extract the structural information based on the seismic imaging data; merge the preprocessed well velocity data and the structural information with the conventional tomographic kernel function to form the tomographic kernel function with joint guidance constraints of multiple wells and structures; the specific implementation steps of the method are:

[0066] 1) Input the velocity curves W and seismic data volume S of multiple wells, use formulas (1) and (2) to complete the well data preprocessing, and obtain the preprocessed well data Wlow-f;

[0067] W re (k) = resample[W(i)] (1)

[0068] Where W(i), W re (k) represents the well velocity data before and after resampling, i represents the sampling point number of the well velocity data before resampling, and k represents the sampling point number of the well velocity data after resampling. Assuming that the lengths of the well data before and after sampling are N old 、N new , then i∈(1,N old ), k∈(1,N new ). resample[·] represents a resampling filter;

[0069]

[0070] By preprocessing all well data using the same operation, we can get W 1,low-f (k), W 2,low-f (k), ..., W m,low-f (k), m represents the number of wells;

[0071] 2) Using formulas (3), (4) and (5) to establish the tomographic kernel function under multi-well constraints;

[0072]

[0073] Among them, o, p, and q are the three-dimensional lengths of the tomographic grid model. All preprocessed well data are used to establish a multi-well constrained tomographic kernel function. The formula is as follows:

[0074]

[0075] The above is the tomographic sensitivity kernel function formed by multi-well constraints. Then the sensitivity kernel function obtained by the combined conventional tomographic inversion can be used to obtain the tomographic kernel function under multi-well constraints, which can be expressed as:

[0076]

[0077] Where l represents the sensitivity matrix element in the conventional tomographic kernel function, and nray represents the number of rays of the tomographic kernel function;

[0078] 3) Extracting structural information C using structural extraction algorithm (Formula (6));

[0079] C(o,p,q)=stru_extract[S(o,p,q)] (6)

[0080] Where C represents the extracted structural information, and S represents the input seismic imaging data volume. stru_extract[g] represents the structural extraction algorithm. There are many methods here, and you can choose any one to obtain structural information;

[0081] 4) Constructing the tomographic kernel function of multi-well and structure joint guidance constraints using formula 7;

[0082]

[0083] Formula 7 is the tomographic kernel function of the multi-well and structure joint guidance constraint, where C represents the extracted structural information, Δs is the speed (slowness) update to be determined, and Δs well is a known quantity, which represents the error between the well velocity and the seismic velocity; Δt is a known quantity, which represents the residual time difference. By solving the above formula, the velocity (slowness) update Δs can be obtained.

[0084] From Figure 2 It can be seen that the well data has a lot of high-frequency jitter, which is not conducive to constraining velocity modeling; Figure 3 It can be seen that the sampling rate of the well data after sampling is consistent with the sampling rate of the seismic data, and the data shape has not changed after resampling, which is in line with the expected resampling effect; Figure 4 As shown, using Figure 2 The high-frequency jitter of the well velocity curve after frequency reduction is effectively eliminated, and the low-frequency trend is retained, which can be used as input information for subsequent well data constraints; Figure 5It can be seen that the velocity update is basically consistent with the structural morphology, and the update is close to the well velocity data, and the well constraint effect is obvious; Figure 6 It can be seen that the imaging depth is quite different from the well calibration depth, which proves that the conventional velocity model has a large imaging error. Figure 7 It can be seen that by using the tomographic kernel function construction method with multiple wells and structural joint constraints of the present invention, the gap between the imaging depth and the well calibration depth is significantly reduced, which proves the effectiveness of the method of the present invention.

[0085] Embodiment 2:

[0086] This embodiment provides a method for constructing a tomographic kernel function with multiple wells and structure joint constraints, such as Figure 1 As shown, including:

[0087] Resampling and frequency reduction processing are performed on the velocity data of each well in turn to obtain processed multi-well velocity data;

[0088] A multi-well constrained tomographic kernel function is established based on the processed multi-well velocity data and the conventional tomographic kernel function;

[0089] Extracting structural information based on seismic imaging data;

[0090] The structural information and the multi-well constrained tomographic kernel function are integrated to form a tomographic kernel function with joint constraints of multiple wells and structures.

[0091] The formula used for resampling is:

[0092] W re (k) = resample[W(i)];

[0093] Among them, W(i), W re (k) are the single well velocity data before and after resampling, i is the sampling point number of the single well velocity data before resampling, k is the sampling point number of the single well velocity data after resampling, and resample[·] is the resampling filter;

[0094] Set N old 、N new are the lengths of well data before and after sampling, i∈(1,N old ), k∈(1,N new );

[0095] The formula used for frequency reduction is:

[0096]

[0097] Where n is the number of points of the smoothing operator, W low-f (k) is the single well velocity data after frequency reduction processing;

[0098] The single well velocity data after frequency reduction processing is the processed single well velocity data;

[0099] The matrix expression of the processed multi-well velocity data is:

[0100]

[0101] Among them, m is the number of wells, o, p, and q are the three-dimensional lengths of the tomographic grid model;

[0102] The expression of the multi-well constrained tomography kernel function is:

[0103]

[0104] Among them, l is the sensitivity matrix element in the conventional tomographic kernel function, and nray is the number of rays of the conventional tomographic kernel function;

[0105] The formula used to extract structural information based on seismic imaging data is:

[0106] C(o,p,q)=stru_extract[S(o,p,q)];

[0107] Where C is the extracted structural information, S is the seismic imaging data, and stru_extract[g] is the structural extraction algorithm;

[0108] The expression of the tomographic kernel function of multi-well and structural joint constraints is:

[0109]

[0110] Where Δs is the speed or slowness update to be sought, Δs well is the error between well velocity and seismic velocity, Δt is the residual time difference, is the simplified expression of the multi-well constrained tomography kernel function, L and Lwell are the conventional tomography kernel function and the multi-well constrained tomography kernel function, respectively.

[0111] Embodiment three:

[0112] This embodiment provides a multi-well and structure joint constraint tomographic kernel function construction device, including:

[0113] A data processing module is used to sequentially resample and downsample the velocity data of each well to obtain processed multi-well velocity data;

[0114] A multi-well kernel function establishment module is used to establish a multi-well constrained tomographic kernel function based on the processed multi-well velocity data and the conventional tomographic kernel function;

[0115] A structural information extraction module, used to extract structural information based on seismic imaging data;

[0116] The joint constraint kernel function establishment module is used to integrate the structural information and the multi-well constraint tomographic kernel function to form a multi-well and structural joint constraint tomographic kernel function;

[0117] The formula used for resampling is:

[0118] W re (k) = resample[W(i)];

[0119] Among them, W(i), W re (k) are the single well velocity data before and after resampling, i is the sampling point number of the single well velocity data before resampling, k is the sampling point number of the single well velocity data after resampling, and resample[·] is the resampling filter;

[0120] Set N old 、N new are the lengths of well data before and after sampling, i∈(1,N old ), k∈(1,N new );

[0121] The formula used for frequency reduction is:

[0122]

[0123] Where n is the number of points of the smoothing operator, W low-f (k) is the single well velocity data after frequency reduction processing;

[0124] The single well velocity data after frequency reduction processing is the processed single well velocity data;

[0125] The matrix expression of the processed multi-well velocity data is:

[0126]

[0127] Among them, m is the number of wells, o, p, and q are the three-dimensional lengths of the tomographic grid model;

[0128] The expression of the multi-well constrained tomography kernel function is:

[0129]

[0130] Among them, l is the sensitivity matrix element in the conventional tomographic kernel function, and nray is the number of rays of the conventional tomographic kernel function;

[0131] The formula used to extract structural information based on seismic imaging data is:

[0132] C(o,p,q)=stru_extract[S(o,p,q)];

[0133] Where C is the extracted structural information, S is the seismic imaging data, and stru_extract[g] is the structural extraction algorithm;

[0134] The expression of the tomographic kernel function of multi-well and structural joint constraints is:

[0135]

[0136] Where Δs is the speed or slowness update to be sought, Δs well is the error between well velocity and seismic velocity, Δt is the residual time difference, is the simplified expression of the multi-well constrained tomography kernel function, L and Lwell are the conventional tomography kernel function and the multi-well constrained tomography kernel function, respectively.

[0137] Embodiment 4:

[0138] An embodiment of the present invention provides an electronic device including a memory and a processor.

[0139] A memory storing executable instructions;

[0140] The processor runs the executable instructions in the memory to implement a tomographic kernel function construction method for multi-well and structure joint constraints.

[0141] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0142] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0143] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present invention.

[0144] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0145] Embodiment five:

[0146] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, a tomographic kernel function construction method for multiple wells and structure joint constraints is implemented.

[0147] The computer-readable storage medium according to the embodiment of the present invention stores non-transitory computer-readable instructions, and when the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of the embodiments of the present invention are executed.

[0148] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0149] The method for constructing a tomographic kernel function with joint constraints of multiple wells and structures proposed in an embodiment of the present invention introduces prior information such as well data and structural morphology to establish a more accurate and reasonable tomographic kernel function with a lower degree of pathology and joint guidance constraints of multiple wells and structures. Solving the kernel function can obtain a more accurate velocity model, which solves the problem that the inversion results of conventional kernel functions are not ideal and the velocity inversion accuracy is low.

[0150] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for constructing tomographic kernel functions with joint constraints of multiple wells and structures. It is characterized in that include: Resampling and frequency reduction processing are performed on the velocity data of each well in turn to obtain processed multi-well velocity data; Establishing a multi-well constrained tomographic kernel function based on the processed multi-well velocity data and a conventional tomographic kernel function; Extracting structural information based on seismic imaging data; The structural information and the multi-well constrained tomographic kernel function are combined to form a tomographic kernel function with joint constraints of multiple wells and structures.

2. The method according to claim 1, It is characterized in that The formula used for the resampling is: W re (k)=resample[W(i)]; Among them, W(i), W re (k) are the single well velocity data before and after resampling, i is the sampling point number of the single well velocity data before resampling, and k is the sampling point number of the single well velocity data after resampling. resample[·] is the resampling filter; Set N old 、N new are the lengths of well data before and after sampling, i∈(1,N old ), k∈(1,N new ).

3. The method according to claim 2, It is characterized in that The formula used in the frequency reduction process is: Where n is the number of points of the smoothing operator, W low-f (k) is the single well velocity data after frequency reduction processing; The single-well velocity data after the frequency reduction processing is the processed single-well velocity data.

4. The method according to claim 3, It is characterized in that The matrix expression of the processed multi-well velocity data is: Where m is the number of wells, and o, p, and q are the three-dimensional lengths of the tomographic grid model.

5. The method according to claim 4, It is characterized in that The expression of the multi-well constrained tomography kernel function is: Among them, l is the sensitivity matrix element in the conventional tomographic kernel function, and nray is the number of rays in the conventional tomographic kernel function.

6. The method according to claim 5, It is characterized in that The formula used to extract structural information based on seismic imaging data is: C(o,p,q)=stru_extract[S(o,p,q)]; Where C is the extracted structural information, S is the seismic imaging data, and stru_extract[g] is the structural extraction algorithm.

7. The method according to claim 6, It is characterized in that The expression of the tomographic kernel function of multi-well and structural joint constraints is: Where Δs is the speed or slowness update to be sought, Δs well is the error between well velocity and seismic velocity, Δt is the residual time difference, is the simplified expression of the multi-well constrained tomography kernel function, L and Lwell are the conventional tomography kernel function and the multi-well constrained tomography kernel function, respectively.

8. A tomographic kernel function construction device with multiple wells and structural joint constraints, It is characterized in that include: A data processing module is used to sequentially resample and downsample the velocity data of each well to obtain processed multi-well velocity data; A multi-well kernel function establishment module, used for establishing a multi-well constrained tomographic kernel function based on the processed multi-well velocity data and a conventional tomographic kernel function; A structural information extraction module, used to extract structural information based on seismic imaging data; The joint constraint kernel function establishment module is used to merge the structural information and the multi-well constraint tomographic kernel function to form a multi-well and structural joint constraint tomographic kernel function.

9. An electronic device, It is characterized in that The electronic device comprises: A memory storing executable instructions; A processor runs the executable instructions in the memory to implement the tomographic kernel function construction method for multiple wells and structure joint constraints according to any one of claims 1-7.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for constructing a tomographic kernel function with multiple wells and structure joint constraints according to any one of claims 1 to 7 is implemented.

Images