Carbonate rock tidal channel sedimentary configuration modeling interpolation method and system

Abstract

The invention relates to a carbonate rock tidal channel sedimentary configuration modeling interpolation method and system, and belongs to the technical field of oil and gas reservoir fine characterization methods. The method comprises the following steps: collecting wave impedance and tidal tract complex data; heuristic seed point selection is carried out on wave impedance and tidal tract complex data; carrying out multi-scale seed region growth on wave impedance and tidal tract complex data obtained by heuristic seed point selection processing; and carrying out corrosion expansion treatment on the single tidal tract model obtained by multi-scale seed region growth. The method adopts heuristic seed point selection and multi-scale seed area growth to model the configuration of the well-seismic combined reservoir single tidal tract reservoir of the thin well pattern, and solves the problem of high modeling difficulty after the open platform carbonate reservoir develops the tidal tract.

Description

Technical Field

[0001] The 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, scale, direction and spatial superposition relationship of reservoir units and interlayers of different levels. Since the concept and research methods of reservoir architecture, many scholars have studied reservoir architecture. Traditional methods for characterizing and modeling oil and gas reservoir architecture mainly include seismic architecture modeling technology and three-dimensional architecture unit seismic identification technology.

[0003] Seismic configuration modeling technology has roughly formed two types of reservoir configuration characterization methods for different data conditions, namely multi-well configuration characterization method and well-seismic combined configuration characterization method. Among them, the well configuration characterization method is aimed at oil reservoirs with dense well networks, and gradually formed modeling methods such as configuration element modeling, interlayer modeling, and multi-level phase control modeling, focusing on the combination of deterministic characterization and random simulation characterization. Due to the complex superposition relationship of carbonate tidal channel reservoirs and the difficulty of fine comparison of small layers, this characterization method has the disadvantages of large workload and high uncertainty in manually comparing the configuration relationship of reservoir units using well data in three-dimensional space. For the well-seismic combined configuration modeling method, for the situation with less well data and large well spacing, since the well point data can only explain the local configuration interface, the seismic data must be fully utilized, and the three-dimensional characterization of the configuration combined with well-seismic is required. The research on the well-seismic combined configuration modeling method is mostly qualitative characterization. This technology is still in the development stage, and has the disadvantages of time-consuming and labor-intensive manual interpretation and high uncertainty. The three-dimensional configuration unit seismic identification technology refers to the use of seismic data to transform the reservoir configuration prediction problem under the seismic resolution limit into a configuration target identification technology.

[0004] Three-dimensional spatial configuration unit recognition is to identify the target on 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 method, edge detection-based segmentation method, region-based segmentation method and deep learning-based segmentation method, but none of the above specific methods can currently meet the needs of three-dimensional configuration engraving.

[0005] Based on the threshold segmentation method, one or more thresholds are calculated according to the data characteristics of different configurations, and then divided into appropriate categories according to the seismic attribute values ​​(such as wave impedance) of each grid. The basic method is to determine the threshold values ​​or classification standards of reservoir configurations of different levels through histogram analysis and cluster analysis on the basis of correlation analysis of well-seismic data, which can usually only be divided into the level of composite river channels (tidal channels). Although it can be subdivided infinitely in theory, it loses its geological significance. In addition, due to the wide distribution range of reservoir wave impedance, it is difficult to give a reasonable threshold value, which is not suitable for three-dimensional configuration unit identification.

[0006] Based on the region segmentation method, the region segmentation algorithm is divided into two categories, region splitting and merging and region growing. The basic idea of ​​region growing is to gather pixels with similar properties to form a region. Specifically, a seed pixel is first found as the starting point of growth for each region to be segmented, and then the pixels in the area around the seed pixel that have the same or similar characteristics as the seed pixel (determined according to some predetermined growth or similarity criteria) are merged into the region where the seed pixel is located. These new pixels are treated as new seed pixels and the above process is continued until no pixels that meet the conditions can be included. In this way, a region is grown. Splitting and merging can be said to be the reverse process of region growing. Starting from the entire region, it is continuously split to obtain each sub-region, and then the sub-regions with the same properties are merged to obtain the expected target to be segmented, thereby realizing the extraction of the target. This type of method requires the design of special growth rules or column rules according to specific problems. At present, there is no region segmentation algorithm for single tidal channel recognition.

[0007] Based on edge detection methods, the identification of configuration boundaries is converted into a three-dimensional discontinuity monitoring problem. Traditional three-dimensional discontinuity detection methods, including coherence bodies and curvature bodies, are based on comparing the waveform similarity between seismic data channels to reveal the distribution characteristics of discontinuities. They are suitable for large-scale discontinuities (faults, etc.) and cannot identify reservoir configurations at the single sand body level that are smaller than the seismic resolution. The Sobel operator, Canny operator and GST algorithm are used to extract formation boundaries, faults, folds, etc. in seismic images. They are greatly affected by noise and have strong discontinuities, making them unsuitable for characterizing reservoir configurations.

[0008] Based on deep learning methods, the basic idea is sample learning. Seismic data and image data are highly similar, and the configuration carving based on seismic data can be regarded as a 3D image semantic segmentation problem. Deep learning models such as U-Net, SegNet, FCN and DeepLab can be used to identify the 3D distribution of configurations in space. However, deep learning methods require a large number of training samples. Due to the lack of suitable samples, it is difficult to apply to actual oil and gas reservoirs.

[0009] It can be seen that the traditional method of reservoir configuration characterization and modeling is mainly a multi-well joint characterization method for dense well network blocks, which is not suitable for sparse well network conditions. The role of seismic in configuration characterization relies on manual qualitative analysis, and the manual interpretation of configuration units and boundaries is labor-intensive and highly uncertain. Methods based on threshold segmentation, edge detection, and deep learning all have limitations, and there is no regional growing algorithm specifically for single tidal channel modeling. Summary of the invention

[0010] In view of the above problems, the present invention proposes a carbonate tidal channel sedimentary configuration modeling interpolation method and system, which is a technology for three-dimensional quantitative geological modeling of a single tidal channel reservoir in an open platform carbonate reservoir, and also a technology for modeling a single tidal channel in an open platform carbonate reservoir. The problem of difficulty in modeling after the development of tidal channels in open platform carbonate reservoirs is solved.

[0011] The first object of the present invention is to provide a carbonate tidal channel sedimentary configuration 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 the grids 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 "performing growth along the growth path until all meshes on the growth path have completed growth";

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

[0022] In the embodiment of the present invention, the step of 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 "secondary judgment of the target grid after the first judgment" or "first judgment of the next grid of the target grid after the first judgment";

[0025] Secondary judgment: judging 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 next mesh of the target mesh after secondary judgment".

[0027] In the embodiment of the present invention, the selecting "secondary judgment of the target grid after the primary judgment" or "primary judgment of the next grid of the target grid after the primary judgment" based on the result of the primary judgment includes:

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

[0029] The result of the first judgment on the growth condition is no, and a second judgment on the target grid is selected 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, the grid within the surrounding pixel distance d is represented as Rijk, and the grid Rijk belongs to the tidal channel complex grid;

[0032] Among them, |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 a distance d from the pixels around the grid to be grown in the tidal channel complex grid.

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

[0034] The earliest growing tidal channel grows 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:

[0035] When the maximum value of i is equal to 0, that is, max(i) = 0, a new tidal 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 dissolution and 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 configuration modeling 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, the electronic device comprising: a processor, the processor being coupled to a memory;

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

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

[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 executed 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 configuration modeling interpolation method and system, which adopts heuristic seed point selection and multi-scale seed region growth to model the configuration of a single tidal channel reservoir in a sparse well network combined with well-seismic data, thereby solving the problem of difficulty in modeling after the development of tidal channels in open platform carbonate reservoirs.

[0052] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or 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 briefly introduces 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 creative work.

[0054] Figure 1 A flowchart of a carbonate tidal channel sedimentary configuration 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 A composite tidal channel distribution diagram obtained by a conventional method according to an embodiment of the present invention is shown;

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

[0059] Figure 6 Shows Figure 5 Three-dimensional distribution map of single tidal channel No. ③:

[0060] Figure 7 A framework diagram of a carbonate tidal channel sedimentary configuration modeling 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 figure:

[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] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0065] like Figure 1 As shown, a carbonate tidal channel sedimentary configuration 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 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 a 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 facilitate the description of the method provided by the embodiment of the present invention, it is assumed 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. According to the existing technology, the tidal channel complex grid G(F) and the 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 of G(F) to identify a single tidal channel G(Fi), which is a single tidal channel body connected in three-dimensional space, where i represents the number of the tidal channel, i=1, where i=1,2,3,…,M, where M represents the largest tidal channel number, and the initial state is i=0, indicating no growth.

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

[0073] Seed point selection according to the seed point setting method of wave impedance size and local growth state. Before regional growth at each scale, seed points must be selected. Seed points are set at the location where the tidal channel is most likely to develop. G(F) grids are sorted according to wave impedance data P. The smaller the wave impedance value, the higher the sorting. The path is represented by R(P, G(F)). The regional growing algorithm will perform regional 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 by S(0). It is assumed that the grid to be grown is n0, and the surrounding grids are nb (the six grids up, down, front, back, left, and right are nb1, nb2, nb3, nb4, nb5, and nb6 respectively). If the states of nb grids are all S(-1), a seed point is added, i=i+1, and the state is set to S(i). Otherwise, the growth calculation of the tidal channel is performed according to the multi-scale seed regional growing algorithm, where S(-1) indicates that there are no seed points in the six grids up, down, 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: executing growth along the growth path until all the grids 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 later, 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 by the heuristic seed point selection process, the radius d of the regional growth unit is set, and the path is set, that is, for the grid system after the heuristic seed point selection process, the radius d of the regional growth unit is set;

[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 of the target grid after the first judgment", wherein, 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 of the target grid after the first judgment, i.e., 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 condition, wherein the growth condition is:

[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 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 grid of the tidal channel complex, i, j, k are the position data of the grid within the pixel distance d around the grid to be grown in the grid of the tidal channel complex;

[0088] Step B4, based on the secondary judgment result, select "growth of the target grid after the secondary judgment" or "a primary judgment of the next grid of the target grid after the secondary judgment"; if the secondary judgment result is that the growth condition is met, the target grid is selected to perform growth; if the secondary judgment result is that the growth condition is not met, a primary judgment of the next grid of the target grid after the secondary judgment is selected, that is, step B1 is performed;

[0089] Wherein, the performing growth comprises:

[0090] The earliest growing tidal channel grows 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 tidal 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 result, 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, which plays the role of eliminating anomalies.

[0100] If the tidal channel does not fill 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 according to the total number of grids of the smallest single tidal channel determined manually.

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

[0103] Classification of reservoir architecture of tidal channel facies reservoirs: A detailed interpretation of the reservoir architecture type of each well is given. 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 division of tidal channel complex and intra-platform depression was completed by conventional technology. The three-dimensional grid structure model was established by conventional technology, and the seismic wave impedance data was resampled to the three-dimensional grid, and then cluster analysis was performed to predict the range distribution of the river complex tidal channel. The number of grids in the three-dimensional model is 259×252×300=19580400, and the K-means cluster analysis method was used to predict the distribution of tidal channel phase 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 figure represent different single tidal channels. Figure 6 Corresponding Figure 5 Three-dimensional distribution of single tidal channel No. ③ (top and bottom surfaces are enclosed).

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

[0107] The 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 the 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 processing;

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

[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: judging 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 of 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 "perform growth on the target grid after secondary judgment" or "perform primary judgment on 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, comprising: a processor 301, the processor 301 is 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, wherein 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 computer-readable storage medium, for example, may include but is 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, which may be used by or in combination 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 substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A carbonate tidal channel sedimentary configuration 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.

2. The carbonate tidal channel sedimentary configuration modeling interpolation method according to claim 1 is characterized in that: 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 the grids on the path have completed growth; Reset: Reduce the size of the growth unit and set the growth path; Repeat growth: Repeat the "grow along the growth path until all meshes on the growth path have completed growth" step; Loop: Repeat the "Reset" and "Repeat Growth" steps until the size of the growing cell is zero.

3. The carbonate tidal channel sedimentary configuration modeling interpolation method according to claim 2 is 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 "secondary judgment of the target grid after the first judgment" or "first judgment of the next grid of the target grid after the first judgment"; Secondary judgment: judging whether the target grid meets the growth conditions; Based on the secondary judgment result, select "Perform growth of target mesh after secondary judgment" or "Perform primary judgment of the next mesh of target mesh after secondary judgment".

4. The carbonate tidal channel sedimentary configuration modeling interpolation method according to claim 3 is characterized in that: The selecting "a second judgment of the target grid after the first judgment" or "a first judgment of the next grid of the target grid after the first judgment" based on the result of the first judgment includes: If the result of the first judgment is yes, a judgment of the next grid of the target grid after the first judgment is selected; The result of the first judgment on the growth condition is no, and a second judgment on the target grid is selected after the first judgment.

5. The carbonate tidal channel sedimentary configuration modeling interpolation method according to claim 3 is characterized in that: The growth conditions are: The grid to be grown Ri0j0k0 on the growth path, the grid within the surrounding pixel distance d is represented as Rijk, and the grid Rijk belongs to the tidal channel complex grid; Among them, |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 a distance d from the pixels around the grid to be grown in the tidal channel complex grid.

6. The carbonate tidal channel sedimentary configuration modeling interpolation method according to claim 3, characterized in that: The execution growth comprises: The earliest growing tidal channel grows 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: When the maximum value of i is equal to 0, that is, max(i) = 0, a new tidal 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.

7. The carbonate tidal channel sedimentary configuration modeling interpolation method according to any one of claims 1 to 6, characterized in that: 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.

8. A carbonate tidal channel sedimentary configuration modeling interpolation system, characterized in that: 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 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.

9. 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 executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or an instruction, and when the program or the instruction is executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 7.

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