A carbonate tidal channel sedimentary architecture modeling interpolation method and system

Abstract

The present invention relates to a carbonate tidal channel sedimentary configuration modeling interpolation method and system, belonging to the technical field of fine characterization methods for oil and gas reservoirs. The method comprises collecting wave impedance and tidal channel complex data; performing heuristic seed point selection on the wave impedance and tidal channel complex data; performing multi-scale seed region growth on the wave impedance and tidal channel complex data obtained by the heuristic seed point selection process; and performing dissolution expansion processing on the single tidal channel model obtained by the multi-scale seed region growth. The present invention uses heuristic seed point selection and multi-scale seed region growth to model the configuration of a single tidal channel reservoir in a well-seismic combined oil reservoir with a sparse well network, solving the problem of difficulty in modeling after the development of tidal channels in open platform carbonate reservoirs.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas reservoir fine characterization methods, and in particular relates to a carbonate tidal channel sedimentary configuration modeling interpolation method and system. Background Art

[0002] Reservoir architecture refers to the shape, size, orientation, and spatial overlap of reservoir units and interlayers of varying hierarchical levels. Since the concept and research methods of reservoir architecture were first established, numerous researchers have studied reservoir architecture. Traditional methods for characterizing and modeling oil and gas reservoir architecture primarily include seismic architecture modeling and three-dimensional seismic identification of architecture units.

[0003] Seismic configuration modeling technology has broadly developed two types of reservoir configuration characterization methods for different data scenarios: multi-well configuration characterization and combined well-seismic configuration characterization. Well configuration characterization methods, targeting reservoirs with dense well patterns, have gradually evolved into modeling methods such as configuration element modeling, interlayer modeling, and multi-level phase-controlled modeling, focusing on a combination of deterministic and stochastic simulation. Due to the complex stacking relationships of carbonate tidal channel reservoirs and the difficulty of fine-scale comparison of small layers, this method suffers from the high workload and high uncertainty associated with manually comparing reservoir unit configurations using well data in three-dimensional space. Combined well-seismic configuration modeling methods, for situations with limited well data and large well spacing, require full utilization of seismic data, as well point data can only interpret local configuration interfaces. This requires a combined well-seismic three-dimensional configuration characterization. Research on combined well-seismic configuration modeling methods has primarily focused on qualitative characterization, and the technology is still in its developmental stages, with the drawbacks of time-consuming and labor-intensive manual interpretation and high uncertainty. Three-dimensional configuration unit seismic identification technology refers to the use of seismic data to transform the reservoir configuration prediction problem at the limit of seismic resolution into a configuration target identification technology.

[0004] Three-dimensional spatial configuration unit recognition is to identify the target in the three-dimensional image background, which can be achieved by using image segmentation technology. Common segmentation algorithms are mainly divided into four categories: threshold-based segmentation methods, edge detection-based segmentation methods, region-based segmentation methods, and deep learning-based segmentation methods. However, none of these specific methods can currently meet the needs of three-dimensional configuration engraving.

[0005] Threshold segmentation methods calculate one or more thresholds based on the data characteristics of different configurations. Seismic attributes (such as wave impedance) are then used to classify each grid into appropriate categories. The basic approach is to determine threshold values ​​or classification criteria for different levels of reservoir configurations through histogram and cluster analysis based on correlation analysis of well-seismic data. This classification typically only extends to the level of composite river channels (tidal channels). While theoretically possible, this classification loses geological significance. Furthermore, due to the wide distribution of reservoir wave impedance, it is difficult to assign a reasonable threshold value, making it unsuitable for 3D configuration unit identification.

[0006] Region-based segmentation algorithms can be categorized into two types: region splitting and merging, and region growing. The basic idea of ​​region growing is to group pixels with similar properties to form a region. Specifically, a seed pixel is first identified for each region to be segmented as the starting point for growth. Pixels in the surrounding area that have the same or similar properties as the seed pixel (determined based on a predetermined growth or similarity criterion) are then merged into the region containing the seed pixel. These new pixels are treated as new seed pixels and the above process is repeated until no more pixels meet the criteria. Thus, a region is formed. Splitting and merging can be considered the reverse process of region growing. Starting from the entire region, the entire region is continuously split into sub-regions. Sub-regions with the same properties are then merged to obtain the desired target to be segmented, thereby achieving target extraction. This type of method requires the design of specialized growth or segmentation rules tailored to the specific problem. Currently, there is no region segmentation algorithm specifically designed for single tidal channel identification.

[0007] Edge-based methods transform structural boundary identification into a three-dimensional discontinuity monitoring problem. Traditional three-dimensional discontinuity detection methods, including coherence volumes and curvature volumes, reveal the distribution characteristics of discontinuities by comparing waveform similarities between seismic data traces. These methods are suitable for large-scale discontinuities (such as faults) but cannot identify reservoir architecture at the level of individual sand bodies, which is smaller than the seismic resolution. Using the Sobel operator, Canny operator, and GST algorithm to extract stratigraphic boundaries, faults, folds, and other features from seismic images is significantly affected by noise and exhibits strong discontinuities, making them unsuitable for characterizing reservoir architecture.

[0008] Deep learning methods rely on sample learning as their fundamental principle. Seismic data and image data are highly similar, so seismic data-based configuration carving can be viewed as a 3D image semantic segmentation problem. Deep learning models such as U-Net, SegNet, FCN, and DeepLab can all be used to identify the 3D spatial distribution of configurations. However, deep learning methods require a large number of training samples, making their application to actual oil and gas reservoirs difficult due to a lack of suitable training samples.

[0009] As can be seen, traditional reservoir architecture characterization and modeling methods primarily rely on multi-well joint characterization in densely populated areas, which is not suitable for sparsely populated areas. Seismic analysis in architecture characterization relies on qualitative analysis, and manual interpretation of architectural units and boundaries is labor-intensive and subject to high uncertainty. Methods based on threshold segmentation, edge detection, and deep learning all have limitations, and there is no region growing algorithm specifically designed for single tidal channel modeling. Summary of the Invention

[0010] To address these issues, the present invention proposes a carbonate tidal channel sedimentary architecture modeling interpolation method and system. This technology addresses the difficulty in modeling tidal channel-developed carbonate reservoirs in open platform areas, providing a technique for three-dimensional quantitative geological modeling of single tidal channel reservoirs.

[0011] A first object of the present invention is to provide a carbonate tidal channel sedimentary architecture modeling interpolation method, the method comprising:

[0012] Collect wave impedance and tidal channel complex data;

[0013] Heuristic seed point selection for wave impedance and tidal channel complex data;

[0014] Multi-scale seed region growing is performed on the wave impedance and tidal channel complex data obtained by heuristic seed point selection.

[0015] The single tidal channel model obtained by growing multi-scale seed regions is subjected to dissolution and expansion treatment.

[0016] In an embodiment of the present invention, the multi-scale seed region growing is performed on the wave impedance and tidal channel complex data obtained by the heuristic seed point selection process, including:

[0017] Settings: Set the size and growth path of the growth unit for the wave impedance and tidal channel complex data obtained by heuristic seed point selection;

[0018] Growth: Execute growth along the growth path until all meshes on the path have completed growth;

[0019] Reset: Reduce the size of the growth unit and set the growth path;

[0020] Repeated growth: Repeat the step of "grow along the growth path until all the meshes on the growth path have completed growth";

[0021] Loop: Repeat the "Reset" and "Repeat Growth" steps until the size of the growing cell is zero.

[0022] In an embodiment of the present invention, performing growth along the growth path until all meshes on the path have completed growth includes:

[0023] First judgment: judge whether the target grid on the growth path is a grown grid;

[0024] Based on the result of the first judgment, select "second judgment of the target grid after the first judgment" or "first judgment of the next grid after the target grid after the first judgment";

[0025] Secondary judgment: determine whether the target grid meets the growth conditions;

[0026] Based on the secondary judgment result, select "Perform growth of the target mesh after secondary judgment" or "Perform primary judgment of the mesh next to the target mesh after secondary judgment".

[0027] In an embodiment of the present invention, the selecting, based on the result of the first judgment, "a second judgment of the target grid after the first judgment" or "a first judgment of the next grid after the target grid after the first judgment", includes:

[0028] If the result of a judgment is yes, a judgment of the next grid of the target grid after the judgment is selected;

[0029] If the growth condition is judged as negative in the first judgment, a second judgment of the target grid is performed after the first judgment.

[0030] In an embodiment of the present invention, the growth conditions are:

[0031] The grid to be grown Ri0j0k0 on the growth path, and the grids within the surrounding pixel distance d are represented as Rijk, and the grids Rijk all belong to the tidal channel complex grid;

[0032] Wherein, |i-i0|≤d, |j-j0|≤d, |k-k0|≤d, d is the size of the growth unit, i0, j0, k0 are the position data of the grid to be grown in the tidal channel complex grid, and i, j, k are the position data of the grid within the tidal channel complex grid that is within a distance d from the pixels around the grid to be grown.

[0033] In an embodiment of the present invention, the performing growth includes:

[0034] The earliest growing tidal channel will grow first. Assume that the tidal channels around the grid to be grown are represented by G(Fi), where i represents the tidal channel number:

[0035] When the maximum value of i is equal to 0, that is, max(i) = 0, a new tide channel number is set according to the heuristic seed point selection method;

[0036] When the minimum value of i is greater than 0, the grid to be grown is set to the tidal channel with the smallest number.

[0037] In an embodiment of the present invention, the corrosion expansion treatment includes:

[0038] Calculate the total number of grids for each tidal channel;

[0039] Determine the total number of grids in each tidal channel and the size of the set threshold value;

[0040] Based on the judgment result, choose to perform dissolution calculation or expansion calculation.

[0041] A second object of the present invention is to provide a carbonate tidal channel sedimentary architecture modeling and interpolation system, the system comprising:

[0042] The collection module is used to collect wave impedance and tidal channel complex data;

[0043] The seed point selection module is used to perform heuristic seed point selection on wave impedance and tidal channel complex data;

[0044] The growth module is used to perform multi-scale seed region growth on the wave impedance and tidal channel complex data obtained by heuristic seed point selection.

[0045] The dissolution and expansion module is used to perform dissolution and expansion processing on a single tidal channel model obtained by growing multi-scale seed regions.

[0046] A third object of the present invention is to provide an electronic device, comprising: a processor coupled to a memory;

[0047] The memory is used to store computer programs;

[0048] The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the above method.

[0049] 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 run on a computer, the computer executes the method as described above.

[0050] Beneficial effects of the present invention:

[0051] The present invention provides a carbonate tidal channel sedimentary architecture modeling interpolation method and system, which uses heuristic seed point selection and multi-scale seed region growth to model the architecture of a single tidal channel reservoir in a sparse well network combined with well-seismic data. This solves the problem of difficulty in modeling tidal channels in open platform carbonate reservoirs.

[0052] 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

[0053] 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.

[0054] Figure 1 A flowchart of a carbonate tidal channel sedimentary structure modeling interpolation method according to an embodiment of the present invention is shown;

[0055] Figure 2 A diagram showing the reservoir configuration division of a tidal channel phase oil reservoir according to an embodiment of the present invention is shown;

[0056] Figure 3 shows a wave impedance data diagram according to an embodiment of the present invention;

[0057] Figure 4 shows a composite tidal channel distribution diagram obtained by a conventional method according to an embodiment of the present invention;

[0058] Figure 5 shows a single tidal channel profile distribution diagram according to an embodiment of the present invention;

[0059] Figure 6 Shown Figure 5 Three-dimensional distribution map of a single tidal channel (No. ③ in the middle):

[0060] Figure 7 A framework diagram of a carbonate tidal channel sedimentary architecture modeling and interpolation system according to an embodiment of the present invention is shown;

[0061] Figure 8 A framework diagram of an electronic device according to an embodiment of the present invention is shown;

[0062] In the picture:

[0063] Collection module 1; seed point selection module 2; growth module 3; dissolution and expansion module 4; electronic device 300; processor 301; memory 302. DETAILED DESCRIPTION

[0064] 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.

[0065] like Figure 1 As shown, a carbonate tidal channel sedimentary structure modeling interpolation method according to an embodiment of the present invention includes:

[0066] Step S1, collecting wave impedance and tidal channel complex data;

[0067] Step S2, performing heuristic seed point selection on the wave impedance and tidal channel complex data;

[0068] Step S3, performing multi-scale seed region growing on the wave impedance and tidal channel complex data obtained by heuristic seed point selection processing;

[0069] Step S4: performing dissolution and expansion processing on the single tidal channel model obtained by growing the multi-scale seed region.

[0070] In step S1, the wave impedance and tidal channel complex data are derived from a three-dimensional model grid system of the composite tidal channel, wherein the three-dimensional model grid system of the composite tidal channel is obtained through well data calibration and cluster analysis based on high-precision wave impedance seismic inversion results;

[0071] To illustrate the method provided by an embodiment of the present invention, assume that the three-dimensional model grid system of the composite tidal channel is G, the total number of grids is N, and the wave impedance data used is P. Based on existing techniques, a tidal channel complex grid G(F) and an intra-platform depression G(I) are identified, where F represents the tidal channel, I represents the intra-platform depression, and N = F + I. Regional growth is performed on the grid portion of G(F), identifying a single tidal channel G(Fi). This is a single tidal channel body connected in three dimensions, where i represents the tidal channel number, i = 1, and i = 1, 2, 3, ..., M, where M represents the largest tidal channel number. The initial state is i = 0, indicating no growth.

[0072] Specifically, in step S2, the heuristic seed point selection is:

[0073] Seed point selection is based on the wave impedance size and local growth state. Before each scale of regional growth, seed points must be selected. Seed points are set at the location where tidal channels are most likely to develop. G(F) grids are sorted according to the wave impedance data P. The smaller the wave impedance value, the higher the ranking. The path is represented as R(P, G(F)). The region growing algorithm will perform region growth along the sorted path. In the initial state, the tidal channel number i=0 of all grids is set, and the grid state is represented as S(0). Assume that the grid to be grown is n0 and the surrounding grids are nb (the six grids above, below, front, back, left, and right are nb1, nb2, nb3, nb4, nb5, and nb6 respectively). If the state of the nb grids is S(-1), a seed point is added, i=i+1, and the state is set to S(i). Otherwise, the tidal channel growth calculation is performed according to the multi-scale seed region growing algorithm, where S(-1) indicates that there are no seed points in the six grids above, below, front, back, left, and right.

[0074] In step S3, the multi-scale seed region growing is performed on the wave impedance and tidal channel complex data obtained by the heuristic seed point selection process, including:

[0075] Step A1, setting: setting the size and growth path of the growth unit based on the wave impedance and tidal channel complex data obtained by the heuristic seed point selection process;

[0076] Step A2, growth: perform growth along the growth path until all meshes on the path have completed growth;

[0077] Step A3, resetting: reducing the size of the growth unit and setting the growth path;

[0078] Step A4, repeating growth: repeating the step of "performing growth along the growth path until all the grids on the growth path have completed growth";

[0079] Step A5, loop: loop the "reset" and "repeat growth" steps until the size of the growth unit is zero.

[0080] Specifically, in step S3, multi-scale seed region growth, that is, multiple pixel units grow simultaneously, gradually growing from large scale to small scale, can effectively distinguish different single tidal channels. For the convenience of calculation, a cube with a side length of r = 2*D + 1 is used as the basic unit of seed point and regional growth, where D represents the number of pixels, and the value of D is equal to the value of the radius d of the long unit set subsequently, reflecting the multi-scale characteristics of growth. r changes from large to small, the maximum scale is estimated according to the average thickness of the tidal channel, and is determined according to the average river channel thickness, and rounds of growth are performed. Specifically:

[0081] Step A1: Based on the wave impedance and tidal channel complex data obtained through heuristic seed point selection, the radius d of the region growing unit and the path are set, that is, the radius d of the region growing unit is set for the grid system after the heuristic seed point selection process;

[0082] Step A2: Execute growth along the growth path until all meshes on the path have completed growth, including:

[0083] Step B1, primary judgment: judging whether the target grid on the growth path is a grown grid;

[0084] Step B2: Based on the result of the first judgment, select "secondary judgment of the target grid after the first judgment" or "first judgment of the next grid after the target grid after the first judgment". If the result of the first judgment is yes (i.e., the target grid is a grown grid), select the first judgment of the next grid after the first judgment, and proceed to step B1; if the result of the first judgment is no, select the second judgment of the target grid after the first judgment, and proceed to step B3;

[0085] Step B3, secondary judgment: judging whether the target grid meets the growth conditions, wherein the growth conditions are:

[0086] For the grid to be grown Ri0j0k0 on the growth path, if the grid within the surrounding pixel distance d is represented as Rijk, and the grid Rijk all belongs to the tidal channel complex grid;

[0087] Wherein, |i-i0|≤d, |j-j0|≤d, |k-k0|≤d, d is the size of the growth unit, i0, j0, k0 are the position data of the grid to be grown in the tidal channel complex grid, i, j, k are the position data of the grid within the distance d from the pixels around the grid to be grown in the tidal channel complex grid;

[0088] Step B4: Based on the secondary judgment result, select "grow the target grid after the secondary judgment" or "perform a primary judgment on the grid next to the target grid after the secondary judgment"; if the secondary judgment result shows that the growth condition is met, the target grid is selected for growth; if the secondary judgment result shows that the growth condition is not met, the primary judgment on the grid next to the target grid after the secondary judgment is selected, i.e., step B1 is performed;

[0089] The performing growth includes:

[0090] The earliest growing tidal channel will grow first. Assume that the tidal channels of the grids around the grid to be grown are represented by (G(Fi)), where i represents the tidal channel number:

[0091] When the maximum value of i is equal to 0, that is, max(i) = 0, a new tide channel number is set according to the heuristic seed point selection method;

[0092] When the minimum value of i is greater than 0, the grid to be grown is set to the tidal channel with the smallest number;

[0093] Step A3, reset: reduce the size of the growth unit, that is, reduce the growth radius d, d=d-1, and set the growth path, that is, return to step A1;

[0094] Step A4, repeating growth: repeating the step of "performing growth along the growth path until all grids on the growth path have completed growth", that is, after the reset step in step A3, re-performing step A2 (including steps B1-B4);

[0095] Step A5, loop: loop the "reset" and "repeat growth" steps until the size of the growth unit is zero, that is, repeat the growth step in step A4, loop steps A3 and A4 until the size of the growth unit is zero, that is, d = 0, the growth is completed, and the single tidal channel model G (Fi) after growth.

[0096] In step S4, the single tidal channel model obtained by growing the multi-scale seed region is subjected to dissolution and expansion processing, including:

[0097] Based on the single tidal channel model G(Fi) obtained in step S4 above, the total number of grids Sum(Fi) of each tidal channel is calculated;

[0098] Determine the total number of grids Sum(Fi) of each tidal channel and the set threshold value T;

[0099] Based on the judgment results, choose to perform dissolution calculation or expansion calculation. If Sum(Fi) is less than the given threshold value T, then perform dissolution calculation to make Fi disappear, thus eliminating the anomaly.

[0100] If the tidal channel is not full of G(F), the expansion operation is performed on each tidal channel Fi in turn until both the left and right G(F) have grown.

[0101] The threshold value T is set based on the total number of grids of the minimum single tidal channel determined manually.

[0102] On the gentle slope carbonate platform, lagoons and low-energy shoals are widely distributed, among which tidal channel deposits or local high-energy shoals develop high-quality reservoirs. Therefore, in the embodiments of the present invention, a gentle slope carbonate platform in a certain place is exemplified as an example;

[0103] Tidal channel reservoir architecture classification: The reservoir architecture type of each well is explained in detail. The classification scheme is as follows: the open platform is divided into intra-platform beach and inter-platform (lake), the intra-platform beach is divided into tidal channel complex and intra-platform depression, and the tidal channel complex is divided into inter-tidal channel (beach bar) and single tidal channel. The specific framework is shown in Figure 2;

[0104] The demarcation of the tidal channel complex and the intra-platform depression was completed using conventional techniques. A three-dimensional grid structural model was established using conventional techniques, and the seismic wave impedance data was resampled to a three-dimensional grid. Cluster analysis was then performed to determine the distribution of the river channel complex tidal channel. The number of grids in the three-dimensional model was 259×252×300=19580400. The K-means cluster analysis method was used to predict the distribution of tidal channel facies reservoirs. Figure 3 is the wave impedance, Figure 4 It is the composite tidal channel distribution obtained by traditional methods.

[0105] Through the scale region generation algorithm, specifically steps S1-S4 are performed to identify individual tidal channel units within the composite tidal channel, and then isolated beaches are identified based on morphological characteristics. The results are as follows: Figure 5 and Figure 6 As shown, Figure 5 The numbers ①-⑦ in the chart represent different single tide channels. Figure 6 Corresponding Figure 5 Three-dimensional distribution of single tidal channel No. ③ (top and bottom surfaces enveloping).

[0106] like Figure 7 As shown, a carbonate tidal channel sedimentary structure modeling and interpolation system according to an embodiment of the present invention includes:

[0107] Collection module 1 is used to collect wave impedance and tidal channel complex data;

[0108] The seed point selection module 2 is used to perform heuristic seed point selection on wave impedance and tidal channel complex data;

[0109] Growth module 3 is used to perform multi-scale seed region growth on the wave impedance and tidal channel complex data obtained by heuristic seed point selection;

[0110] The corrosion expansion module 4 is used to perform corrosion expansion processing on the single tidal channel model obtained by growing the multi-scale seed region.

[0111] In the embodiment of the present invention, the growth module 3 includes a primary judgment submodule, a primary selection submodule, a secondary judgment submodule and a secondary selection submodule;

[0112] The primary selection submodule is used for the primary judgment module to make a primary judgment: to judge whether the target grid on the growth path is a grown grid;

[0113] The primary selection submodule is used to select "secondary judgment of the target grid after the primary judgment" or "primary judgment of the next grid after the target grid after the primary judgment" based on the result of the primary judgment;

[0114] The secondary judgment submodule is used for secondary judgment: judging whether the target grid meets the growth conditions;

[0115] The secondary selection submodule selects "performing growth of the target grid after secondary judgment" or "performing primary judgment of the next grid of the target grid after secondary judgment" based on the secondary judgment result.

[0116] like Figure 8 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;

[0117] The memory 302 is used to store computer programs;

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 carbonate tidal channel sedimentary architecture modeling interpolation method, characterized in that: include: Collect wave impedance and tidal channel complex data; Heuristic seed point selection for wave impedance and tidal channel complex data; Multi-scale seed region growing is performed on the wave impedance and tidal channel complex data obtained by heuristic seed point selection. The single tidal channel model obtained by growing multi-scale seed regions is subjected to dissolution and expansion treatment; The wave impedance and tidal channel complex data are derived from a three-dimensional model grid system of the composite tidal channel, wherein the three-dimensional model grid system of the composite tidal channel is obtained through well data calibration and cluster analysis based on high-precision wave impedance seismic inversion results; The multi-scale seed region growing is performed on the wave impedance and tidal channel complex data obtained by the heuristic seed point selection process, including: Settings: Set the size and growth path of the growth unit for the wave impedance and tidal channel complex data obtained by heuristic seed point selection; Growth: Execute growth along the growth path until all meshes on the path have completed growth; Reset: Reduce the size of the growth unit and set the growth path; Repeated growth: Repeat the "grow along the growth path until all meshes on the growth path have completed growth" step; Loop: Loop through the "reset" and "repeat growth" steps until the size of the growing unit is zero; The dissolution and expansion treatment comprises: Calculate the total number of grids for each tidal channel; Determine the total number of grids in each tidal channel and the size of the set threshold value; Based on the judgment result, choose to perform dissolution calculation or expansion calculation.

2. The carbonate tidal channel sedimentary structure modeling interpolation method according to claim 1, characterized in that: The step of performing growth along the growth path until all meshes on the path have completed growth includes: First judgment: judge whether the target grid on the growth path is a grown grid; Based on the result of the first judgment, select "second judgment of the target grid after the first judgment" or "first judgment of the next grid after the target grid after the first judgment"; Secondary judgment: determine whether the target grid meets the growth conditions; Based on the secondary judgment result, select "Perform growth of the target mesh after secondary judgment" or "Perform primary judgment of the next mesh of the target mesh after secondary judgment".

3. The carbonate tidal channel sedimentary structure modeling interpolation method according to claim 2, characterized in that: The selecting of "a second judgment of the target grid after the first judgment" or "a first judgment of the next grid after the first judgment" based on the result of the first judgment includes: If the result of a judgment is yes, a judgment of the next grid of the target grid after the judgment is selected; If the growth condition is judged as negative in the first judgment, a second judgment of the target grid is performed after the first judgment.

4. The carbonate tidal channel sedimentary architecture modeling interpolation method according to claim 2, characterized in that: The growth conditions are: The grid to be grown Ri0j0k0 on the growth path, and the grids within the surrounding pixel distance d are represented as Rijk, and the grids Rijk all belong to the tidal channel complex grid; Where |i-i0|≤d, |j-j0|≤d, |k-k0|≤d, d is the size of the growth unit, i0, j0, k0 are the position data of the grid to be grown in the tidal channel complex grid, and i, j, k are the position data of the grid within the tidal channel complex grid that is within a distance d from the pixels around the grid to be grown.

5. The carbonate tidal channel sedimentary structure modeling interpolation method according to claim 2, characterized in that: The execution growth comprises: The earliest growing tidal channel will grow first. Assume that the grid tidal channels around the grid to be grown are represented by G(Fi), where i represents the tidal channel number and Fi represents the composite tidal channel: When the maximum value of i is equal to 0, that is, max(i) = 0, a new tide channel number is set according to the heuristic seed point selection method; When the minimum value of i is greater than 0, the grid to be grown is set to the tidal channel with the smallest number.

6. A carbonate tidal channel sedimentary structure modeling interpolation system, characterized by: include: The collection module is used to collect wave impedance and tidal channel complex data; The seed point selection module is used to perform heuristic seed point selection on wave impedance and tidal channel complex data; The growth module is used to perform multi-scale seed region growth on the wave impedance and tidal channel complex data obtained by heuristic seed point selection. The corrosion and expansion module is used to perform corrosion and expansion processing on the single tidal channel model obtained by growing multi-scale seed regions; The wave impedance and tidal channel complex data are derived from a three-dimensional model grid system of the composite tidal channel, wherein the three-dimensional model grid system of the composite tidal channel is obtained through well data calibration and cluster analysis based on high-precision wave impedance seismic inversion results; The multi-scale seed region growing is performed on the wave impedance and tidal channel complex data obtained by the heuristic seed point selection process, including: Settings: Set the size and growth path of the growth unit for the wave impedance and tidal channel complex data obtained by heuristic seed point selection; Growth: Execute growth along the growth path until all meshes on the path have completed growth; Reset: Reduce the size of the growth unit and set the growth path; Repeated growth: Repeat the "grow along the growth path until all meshes on the growth path have completed growth" step; Loop: Loop through the "reset" and "repeat growth" steps until the size of the growing unit is zero; The dissolution and expansion treatment comprises: Calculate the total number of grids for each tidal channel; Determine the total number of grids in each tidal channel and the size of the set threshold value; Based on the judgment result, choose to perform dissolution calculation or expansion calculation.

7. 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 5.

8. 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 5.

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