Three-dimensional sedimentary facies modeling method and device based on planar graph constraint

Abstract

The invention provides a three-dimensional sedimentary facies modeling method and device based on planar graph constraint, and the method comprises the steps: loading a sedimentary facies graph and a thickness graph to a three-dimensional space, projecting a planar graph to an oil reservoir grid through space coordinate transformation, and coarsening the planar graph into an oil reservoir grid surface attribute; establishing a rapid sedimentary facies model by utilizing the plane sedimentary facies diagram, and establishing lithologic probability volume data by utilizing the plane thickness diagram and through coKriging interpolation; and the logging sedimentary facies data serve as hard data, the rapid sedimentary facies model serves as spatial constraint, the probability body serves as trend constraint, and a sequential indication simulation algorithm is utilized to generate the sedimentary facies model. According to the three-dimensional sedimentary facies modeling scheme based on the planar graph constraint provided by the invention, information such as the logging facies, the planar facies and the thickness graph is comprehensively utilized, the understanding of geologists can be introduced between wells, the problems of mathematics and datamation of a conventional method are solved, and the precision of a sedimentary facies model is improved.

Description

Technical Field

[0001] This application relates to the field of static modeling technology in oil and gas development, and in particular to a three-dimensional sedimentary facies modeling method and apparatus based on planar diagram constraints. Background Technology

[0002] Currently, establishing three-dimensional geological models of reservoirs in the study area for detailed reservoir description is a common technique in geological research. In particular, reservoir geological facies control modeling technology, as it matures, has become one of the core technologies for quantitative reservoir research. The accuracy of reservoir geological facies control modeling directly restricts the economics and effectiveness of oil and gas development. Please see... Figure 1 The diagram shows the effect of phase-controlled attribute modeling provided in this application. It can be seen that the quality of the sedimentary facies model directly affects the quality of the entire reservoir geological model.

[0003] In existing technologies, there are many types of sedimentary facies modeling methods, mainly including Boolean modeling, indicator kriging, sequential indicator simulation, truncated Gaussian simulation, etc. Based on the data types used, they can be mainly divided into the following two categories:

[0004] (1) Sedimentary facies modeling technology based on well data: The data comes entirely from well logging. The well logging facies are sampled into the reservoir grid as known data, and then mathematical algorithms are used to simulate the grid without data. The advantages of this method are: simple process, simulation results are completely consistent with well data, and the simulation effect between wells is better when the wells are dense. The disadvantages of this method are: the simulation between wells is completely dependent on the algorithm, lacks geological sedimentary patterns, especially when there are few wells, the trend of mathematical modeling is serious.

[0005] (2) Seismic-constrained sedimentary facies modeling technique: using well logging sedimentary facies coarsening data as hard data and seismic data sampled to the reservoir grid as constraints, different mathematical algorithms are used to establish sedimentary facies models. The advantages of this method are: the simulation results can perfectly match the well locations with the well data and conform to the seismic trend between wells; the disadvantages of this method are: the simulation results are greatly affected by the quality of seismic data, and the identification of reservoirs with rapid spatial facies changes is low, lacking the understanding of geologists.

[0006] Due to the diverse rock types, rapid spatial facies transitions, and limited number of well logs in reservoirs, it is extremely difficult to establish a high-precision sedimentary facies model that conforms to geological laws across the entire area using any of the aforementioned traditional methods. This inevitably limits the accuracy of reservoir geological facies-controlled modeling. Therefore, there is an urgent need to provide a new sedimentary facies modeling scheme to overcome the shortcomings of existing technologies and improve the accuracy of reservoir geological facies-controlled modeling. Summary of the Invention

[0007] This application discloses a method and apparatus for three-dimensional sedimentary phase modeling based on planar diagram constraints.

[0008] In a first aspect, this application discloses a three-dimensional sedimentary facies modeling method based on planar diagram constraints, the method comprising:

[0009] The sedimentary facies map and thickness map are loaded into three-dimensional space, and the planar map is projected onto the reservoir grid using spatial coordinate transformation, and then coarsened to the reservoir grid surface attributes.

[0010] A rapid sedimentary facies model was established using a planar sedimentary facies map, and lithological probability volume data was established using a planar thickness map and cokriging interpolation.

[0011] Using well logging sedimentary facies data as hard data, rapid sedimentary facies models as spatial constraints, and probabilistic volumes as trend constraints, a sequential indicator simulation algorithm is used to generate sedimentary facies models.

[0012] Preferably, the step of loading the sedimentary facies map and thickness map into three-dimensional space, projecting the planar map onto the reservoir grid using spatial coordinate transformation, and coarsening it to the reservoir grid surface attributes includes:

[0013] The geodetic coordinate system of the planar map is converted into a grid coordinate system using the Gaussian projection method to achieve coordinate transformation;

[0014] Based on the range rules set by the user, the planar grid is scattered according to the set scatter density;

[0015] The scatter data is assigned values ​​based on the location of the scatter points and the properties of the plane, wherein the properties include at least sedimentary facies type and sand body thickness.

[0016] The scattered data of the planar map is converted into seismic horizon data format and loaded into three-dimensional space to achieve data format conversion.

[0017] Preferably, the step of loading the sedimentary facies map and thickness map into three-dimensional space, projecting the planar map onto the reservoir grid using spatial coordinate transformation, and coarsening it to the reservoir grid surface attributes includes:

[0018] The scatter points of the stratigraphic level are projected onto the grid plane, and the grid plane index of each scatter point is obtained by intersection calculation to complete the intersection processing of the scatter points of the stratigraphic level and the grid.

[0019] Get the set of scattered points within each grid;

[0020] Based on the scatter point attributes within each grid, the attribute value that appears most frequently within the grid is assigned to the grid, thereby achieving coarsening of discrete data.

[0021] Preferably, the step of loading the sedimentary facies map and thickness map into three-dimensional space, projecting the planar map onto the reservoir grid using spatial coordinate transformation, and coarsening it to the reservoir grid surface attributes includes:

[0022] The scatter points of the stratigraphic level are projected onto the grid plane, and the grid plane index of each scatter point is obtained by intersection calculation to complete the intersection processing of the scatter points of the stratigraphic level and the grid.

[0023] Get the set of scattered points within each grid;

[0024] The average attribute values ​​of all scattered points within the grid are taken and assigned to the grid to achieve continuous data coarsening.

[0025] Preferably, the steps of establishing a rapid sedimentary facies model using a planar sedimentary facies map and establishing lithological probability volume data using a planar thickness map and cokriging interpolation include:

[0026] A1. Obtain the stratigraphic information of the reservoir grid model, and obtain the set of all stratigraphic indices based on the partition information represented by the plan view;

[0027] A2. Obtain the layering information of multiple K layers in the reservoir grid model;

[0028] A3. Based on the coarsened planar properties, assign values ​​to all K-layer meshes under the control of the planar diagram, and assign the same values ​​to meshes with the same mesh coordinates;

[0029] A4. Repeat steps A1 to A3 to establish a rapid sedimentary facies model for the entire work area.

[0030] Preferably, the steps of establishing a rapid sedimentary facies model using a planar sedimentary facies map and establishing lithological probability volume data using a planar thickness map and cokriging interpolation further include:

[0031] A5. Based on all the grids represented by the surface obtained in step A2, obtain the grids with the same grid coordinates, calculate the sum of their thicknesses and use it as the formation thickness, and determine the ratio of sandstone thickness to formation thickness as the sandstone probability surface.

[0032] A6. Generate a rapid sandstone probability body according to step A3;

[0033] A7. Repeat steps A5 to A6 to establish a rapid probability body for the entire work area.

[0034] Preferably, the steps of establishing a rapid sedimentary facies model using a planar sedimentary facies map and establishing lithological probability volume data using a planar thickness map and cokriging interpolation further include:

[0035] B1. The nearest neighbor interpolation method is used to coarse the well logging sedimentary facies data into the reservoir grid, thereby achieving the coarsening processing of well logging sedimentary facies data.

[0036] B2. Using well logging coarsening data as the main variable and fast probability volume as the second variable, a fine probability volume of sandstone is generated using the Kriging interpolation algorithm.

[0037] B3. For other lithofacies probability volumes, generate other lithofacies probability volumes by repeating steps A5 to A6 and steps B1 to B2.

[0038] Preferably, after establishing lithological probability volume data using planar thickness maps and cokriging interpolation, the method further includes:

[0039] The original well logging data is statistically analyzed, and the proportion of sample points of each type of lithofacies to the total sample points is calculated to achieve statistical analysis of the proportion of sedimentary facies in well logging.

[0040] For lithofacies with fine probability volumes, if the sum of the probabilities of each lithofacies in the same grid is greater than 1, then the value of the sum of the probability volumes of the grid is determined to be 1.

[0041] For lithofacies lacking fine probabilistic volumes, the probability of coal and rock in each grid is determined as follows:

[0042] (1-Q)*0.1 / (0.1+0.3+0.2)

[0043] Where Q represents the sum of probabilities of each lithofacies that have a fine probability body, and 1-Q represents the sum of probabilities of lithofacies that do not have a fine probability body.

[0044] Preferably, the step of generating a sedimentary facies model using well logging sedimentary facies data as hard data, a rapid sedimentary facies model as spatial constraint, and a probabilistic volume as trend constraint, and employing a sequential indicator simulation algorithm, includes:

[0045] The reservoir grid model is converted from tectonic domain data to simulated sedimentary domain data, thus achieving the simulation domain conversion;

[0046] Using the rapid sedimentary facies model as the spatial constraint and the fine probabilistic volume as the trend, a fine sedimentary facies model is established based on the sequential indicator simulation algorithm.

[0047] The grids within a circular area with a radius of N grids are obtained one by one from each grid, and the grid data in the sedimentary facies model are post-processed using a moving smoothing algorithm;

[0048] Based on the mapping relationship established when converting the reservoir grid model from the tectonic domain to simulated sedimentary domain data, the processed sedimentary facies domain data is inversely transformed back to the tectonic domain.

[0049] Store the attribute model data transformed inversely to the constructed domain.

[0050] Preferably, the step of post-processing the grid data in the sedimentary facies model using a moving smoothing algorithm includes:

[0051] Determine whether the grid is the original coarsened data;

[0052] If so, the grid's properties remain unchanged;

[0053] If not, then the type with the most occurrences among all lithofacies types within the circular area is determined as the attribute of the grid.

[0054] Secondly, this application discloses a three-dimensional sedimentary phase modeling apparatus based on planar diagram constraints, the apparatus comprising:

[0055] The grid surface coarsening module is used to load sedimentary facies maps and thickness maps into three-dimensional space, project the planar map onto the reservoir grid using spatial coordinate transformation, and coarsen it into reservoir grid surface attributes.

[0056] The rapid sedimentary facies model and lithological probability volume generation module is used to establish rapid sedimentary facies models using planar sedimentary facies maps and to establish lithological probability volume data using planar thickness maps and cokriging interpolation.

[0057] The sedimentary facies modeling module is used to generate sedimentary facies models using well logging sedimentary facies data as hard data, rapid sedimentary facies models as spatial constraints, probabilistic volumes as trend constraints, and sequential indicator simulation algorithms.

[0058] Thirdly, this application discloses an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to perform the method as described in any of the preceding aspects.

[0059] Fourthly, this application discloses a non-transitory computer-readable storage medium in which, when the instructions in the storage medium are executed by a processor of an electronic device, enable the electronic device to perform the methods described in any of the preceding aspects.

[0060] Fifthly, this application discloses a computer program product in which, when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the preceding aspects.

[0061] The technical solution provided in this application may include the following beneficial effects:

[0062] The three-dimensional sedimentary facies modeling scheme based on planar diagram constraints provided in this application comprehensively utilizes information such as well logging facies, planar facies, and thickness maps, and can incorporate geologists' insights between wells, overcoming the problems of mathematical and data-driven approaches in conventional methods, and improving the accuracy of sedimentary facies models. Attached Figure Description

[0063] Figure 1 This application provides a schematic diagram of the effect of phase-controlled attribute modeling.

[0064] Figure 2A flowchart illustrating a three-dimensional sedimentary facies modeling method based on planar diagram constraints provided in this application;

[0065] Figure 3 A flowchart illustrating the technical process of the planar diagram-constrained three-dimensional sedimentary facies modeling method provided in this application;

[0066] Figure 4 This is a schematic diagram of an example of the original river channel plan provided in this application;

[0067] Figure 5 This is a schematic diagram of the scattering process of the plan view provided in this application;

[0068] Figure 6 A schematic diagram illustrating the value assignment process after scattering the plan view provided in this application;

[0069] Figure 7 A schematic diagram illustrating the acquisition of the sub-level index provided in this application;

[0070] Figure 8 A schematic diagram illustrating the rapid depositional facies modeling effect provided for this application;

[0071] Figure 9 A schematic diagram illustrating the probabilistic volume modeling effect based on a planar graph provided in this application;

[0072] Figure 10 A schematic diagram illustrating the fine sedimentary facies modeling effect provided for this application;

[0073] Figure 11 This is a schematic diagram illustrating the effect of the phase-controlled porosity model provided in this application.

[0074] Figure 12 A structural diagram of the three-dimensional sedimentary phase modeling apparatus based on planar diagram constraints provided in this application;

[0075] Figure 13 A block diagram of an electronic device provided in this application;

[0076] Figure 14 A block diagram of another electronic device provided in this application. Detailed Implementation

[0077] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0078] This application relates to reservoir sedimentary facies modeling technology, specifically static modeling technology in oil and gas development. As mentioned earlier, existing technical solutions suffer from the following problems: difficulty in establishing a high-precision sedimentary facies model that conforms to geological laws across the entire area, and inability to effectively integrate constraints from multiple sources. To address these issues, this application provides a three-dimensional sedimentary facies modeling method and apparatus based on planar map constraints.

[0079] The following describes the three-dimensional sedimentary facies modeling method based on planar diagram constraints provided in this application.

[0080] Example 1

[0081] Please see Figure 2 and Figure 3 ,in Figure 2 A flowchart illustrating a three-dimensional sedimentary phase modeling method based on planar diagram constraints provided in this application is shown. This method can be applied to electronic devices, and specifically includes the following steps:

[0082] Step S101: Load the sedimentary facies map and thickness map into three-dimensional space, use spatial coordinate transformation to project the planar map onto the reservoir grid, and coarsen it into reservoir grid surface attributes.

[0083] Step S102: Establish a rapid sedimentary facies model using a planar sedimentary facies map, and establish lithological probability volume data using a planar thickness map and cokriging interpolation.

[0084] Step S103: Using well logging sedimentary facies data as hard data, a rapid sedimentary facies model as spatial constraint, and a probabilistic volume as trend constraint, a sedimentary facies model is generated using a sequential indicator simulation algorithm.

[0085] Combination Figure 3 The provided flowchart of the three-dimensional sedimentary facies modeling method based on planar map constraints clearly shows the following approach: First, sedimentary facies maps and thickness maps based on geologists' understanding are loaded into three-dimensional space. Using spatial coordinate transformation, the planar map is projected onto the reservoir grid and coarsened to reflect reservoir grid surface attributes. Then, a rapid sedimentary facies model is established using the planar sedimentary facies map, and lithological probability volume data is established using cokriging interpolation of the planar thickness map. Finally, using well logging sedimentary facies data as hard data, the rapid sedimentary facies model as spatial constraints, and the probability volume as trend constraints, a sequential indicator simulation algorithm is used to generate the sedimentary facies model. It is evident that the three-dimensional sedimentary facies modeling scheme based on planar map constraints provided in this application comprehensively utilizes information from well logging facies, planar facies, and thickness maps, incorporating geologists' understanding between wells, overcoming the problems of mathematical and data-driven approaches in conventional methods, and improving the accuracy of the sedimentary facies model.

[0086] Understandably, the planar data involved in reservoir sedimentary modeling mainly falls into two categories: discrete data, such as planar sedimentary facies maps and planar lithofacies maps; and continuous data, such as sand body thickness maps and sand-to-land ratio maps. However, neither type of data can be directly used in a 3D mesh model; both require pre-meshing processing.

[0087] In step S101, the planar data can be scattered as follows: First, using Gaussian projection, the geodetic coordinate system of the planar map is converted into a grid coordinate system. (See [link to relevant documentation]). Figure 4 This is done to achieve coordinate transformation. Secondly, based on the user-defined range rules, the planar grid is scattered according to the set scatter density; for example, the scatter density can be set to 10 meters × 10 meters. It should be noted that when scattering planar grid data, the planar grid can be scattered according to range rules; please refer to [link to relevant documentation]. Figure 5 The density of the scatter points should be entered according to user requirements. Next, assign values ​​to the scatter point data based on the location of the scatter points and the properties of the plane; please refer to [link to relevant documentation]. Figure 6 The attributes include at least sedimentary facies type and sand body thickness. Finally, the scatter plot data of the planar map is converted into seismic horizon data format and loaded into three-dimensional space to achieve data format conversion.

[0088] After converting planar data into stratigraphic data and loading it into three-dimensional space, the stratigraphic data needs to be coarsened into grid-plane data usable by the reservoir model, i.e., the coarsening process of discrete data such as sedimentary facies. In step S101, the planar data can be scattered as follows: specifically, the stratigraphic scatter points are projected onto the grid plane, and the grid plane index of each scatter point is obtained through intersection calculation, completing the intersection processing between the stratigraphic scatter points and the grid; the set of scatter points in each grid is obtained; according to the attributes of the scatter points in each grid, the attribute value that appears most frequently in the grid is assigned to the grid, thus realizing the coarsening processing of discrete data.

[0089] In step S101, continuous data coarsening is performed as follows: specifically, the scatter points of the stratigraphic level are projected onto the grid plane, and the grid plane index of each scatter point is obtained through intersection calculation, completing the intersection processing between the scatter points of the stratigraphic level and the grid; the set of scatter points within each grid is obtained; the average attribute values ​​of all scatter points within the grid are averaged and assigned to the grid, thus realizing continuous data coarsening. It should be noted that this continuous data coarsening process is similar to the discrete data coarsening process, the only difference being that the last step of the continuous processing is to coarse data such as thickness using an averaging method or a local interpolation method.

[0090] A rapid sedimentary facies model is established based on the sedimentary facies plan view. This model represents the maximum extent of sedimentary facies in this layer, providing constraints for further refined sedimentary facies modeling. The key is to obtain the sub-layers where the plan view is located. In the generation of stepped reservoir grids, special handling is required to accommodate reverse faults; therefore, obtaining sub-layers is very difficult when reverse faults exist. In step S102, rapid sedimentary facies modeling can be performed as follows, including the following steps:

[0091] A1. Obtain the stratigraphic information of the reservoir grid model. Based on the zone information represented by the planar map, obtain the set of all stratigraphic indices. Specifically, you can obtain the reservoir grid model layer information: based on the zone information represented by the planar map, obtain the set of all layer indices. Please refer to [link to relevant documentation]. Figure 7 .

[0092] A2. Obtain the layering information of multiple K layers in the reservoir grid model; it should be noted that due to the existence of reverse faults, the same stratum may be divided into multiple K layers, so it is necessary to further obtain all K information.

[0093] A3. Based on the coarsened planar properties, assign values ​​to all K-layer meshes under the control of the planar diagram, and assign the same values ​​to meshes with the same mesh coordinates. Specifically, the mesh assignment process is as follows: based on the coarsened planar properties, assign values ​​to all K-layer meshes under the control of this planar diagram, and assign the same values ​​to meshes with the same (I, J) coordinates;

[0094] A4. Repeat steps A1 to A3 to establish a rapid sedimentary facies model for the entire work area.

[0095] Furthermore, to establish a rapid trend probability body, after establishing a rapid sedimentary facies model for the entire work area, steps A5 to A7, namely the rapid trend probability body establishment process, can be included. This involves establishing a rapid trend probability body based on sandstone thickness maps, etc., to provide internal trend constraints for the refined sedimentary facies model, as follows:

[0096] A5. Based on all the grids represented by the surface obtained in step A2, obtain the grids with the same coordinates, calculate the sum of their thicknesses and use this sum as the formation thickness. The ratio of sandstone thickness to formation thickness is determined as the sandstone probability surface. Specifically, the sandstone probability surface is generated by obtaining all the grids represented by the surface obtained in A1 and A2, acquiring the grids with the same coordinate (I, J), and calculating the sum of their thicknesses. This sum of thicknesses is the formation thickness. The sandstone thickness divided by the formation thickness is the sandstone probability surface.

[0097] A6. Generate a rapid sandstone probability body according to step A3;

[0098] A7. Repeat steps A5 to A6 to establish a rapid probability body for the entire work area.

[0099] Since the fast probability volume has consistent probabilities in the vertical direction, it cannot reflect the vertical trend, so a more refined probability volume needs to be established. In step S102, the refined probability volume is generated as follows:

[0100] B1. The nearest neighbor interpolation method is used to coarsen the well logging sedimentary facies data into the reservoir grid, thereby achieving well logging sedimentary facies data coarsening processing. Specifically, well logging sedimentary facies data coarsening involves coarsening the well logging sedimentary facies data into the reservoir grid to provide subsequent well logging data.

[0101] B2. Using well logging coarsening data as the main variable and fast probability volume as the second variable, a fine probability volume for sandstone is generated using the Kriging interpolation algorithm.

[0102] B3. For other lithofacies probability volumes, generate other lithofacies probability volumes by repeating steps A5 to A6 and steps B1 to B2.

[0103] Furthermore, after establishing lithological probabilistic volume data using planar thickness maps and cokriging interpolation, fine probabilistic volume post-processing steps can be included, such as normalization and anomaly handling, as follows:

[0104] The original well logging data includes all sample points. The proportion of sample points of each type of lithofacies to the total sample points is calculated to achieve the statistical analysis of sedimentary facies proportions in well logging. For example, sandstone accounts for 40%, mudstone accounts for 30%, shale accounts for 20%, and coal accounts for 10%.

[0105] For lithofacies with fine probability volumes, if the sum of the probabilities of each lithofacies in the same grid is greater than 1, then the value of the sum of the probability volumes of the grid is determined to be 1.

[0106] For lithofacies lacking fine probabilistic volumes, the probability of coal and rock in each grid is determined as follows:

[0107] (1-Q)*0.1 / (0.1+0.3+0.2)

[0108] Where Q represents the sum of probabilities of each lithofacies that have a fine probability body, and 1-Q represents the sum of probabilities of lithofacies that do not have a fine probability body.

[0109] In one scenario, refined probabilistic post-processing can be performed as follows:

[0110] (1) Statistics on the proportion of sedimentary facies in well logging: Statistically analyze all sample points of the original well logging data, calculate the proportion of sample points of each type of lithofacies to the total sample points. Assuming there are n types of lithofacies, they are denoted as P1, P2...Pn (fixed constants).

[0111] (2) Post - processing of lithofacies probability volume: The probability volume established according to the fine probability volume generation method is m, and m < n; then the probability of each lithofacies in a certain grid is denoted as Q1, Q2... Qm (each grid is different); then the sum of the fine probability volumes of each lithofacies is Q. If Q > 1, then Q = 1; the sum of the probabilities of lithofacies without fine probability volume is 1 - Q; obtain all the lithofacies probabilities without fine probability volume screened from 6(4), which are P1, P2... Px, a total of x; then the probability of each grid without fine probability volume is (1 - Q)P1 / (P1 + P2... Px), (1 - Q)P2 / (P1 + P2... Px)...

[0112] Taking the well - logging coarsened data as hard data, the fine probability volume as trend constraint, and the fast sedimentary facies model as spatial constraint, use the sequential indicator simulation algorithm to generate a fine sedimentary facies model. In step S103, the fine sedimentary facies model can be established in the following way. Specifically: First, convert the reservoir grid model from the tectonic domain to the simulated sedimentary domain data to achieve the simulation domain conversion. It can be understood that the reservoir grid model is in the tectonic domain, and the property simulation is in the sedimentary facies. Before simulation, all data is converted into the simulated sedimentary domain data. Second, take the fast sedimentary facies model as the spatial constraint and the fine probability volume as the trend, and establish a fine sedimentary facies model based on the sequential indicator simulation algorithm. Then, obtain the grids within a circular range with a radius of N grids from each grid one by one, and use the moving smoothing algorithm to post - process the grid data in the sedimentary facies model. It can be understood that due to the randomness of the sequential indicator simulation algorithm, the continuity is poor. Through post - processing with the moving smoothing algorithm, obtain a certain range of grids from each grid one by one. If this grid is not the original coarsened data, then take the most common type among all lithofacies types within the range as the new facies attribute of this grid; if this grid is the original coarsened data, the attribute remains unchanged. That is: Determine whether the grid is the original coarsened data; if it is, the attribute of the grid remains unchanged; if not, determine the type with the largest quantity among all lithofacies types within the circular range as the attribute of the grid. Then, according to the mapping relationship established when converting the reservoir grid model from the tectonic domain to the simulated sedimentary domain data, inverse - transform the processed sedimentary facies domain data back to the tectonic domain. Finally, store the attribute model data inverse - transformed to the tectonic domain. For example, the data can be stored in binary form.

[0113] To more clearly understand the three - dimensional sedimentary facies modeling method based on planar map constraints provided by this application, the following combines the attached Figures 1 to 11 Taking a certain work station in a certain area as an example, the three - dimensional sedimentary facies modeling method based on planar map constraints provided by the solution of this application is described in detail.

[0114] S1. Scatter - point processing of planar data.

[0115] S11. Coordinate system transformation: Convert the geodetic coordinate system of the planar map to a grid coordinate system using Gaussian projection.

[0116] S12. Planar grid scattering: Based on the range rules set by the user, the planar grid is scattered, and the density of the scattered points is 10 meters × 10 meters.

[0117] S13. Assign values ​​to scatter points of planar data. Assign values ​​to scatter point data based on the location of the scatter points and the properties of the planar data. Properties include sedimentary facies type, sand body thickness, etc.

[0118] S14. Data format conversion: Convert the scattered data of the planar map into the seismic horizon data format and load it into three-dimensional space.

[0119] S2, Coarsening of discrete data.

[0120] S21. Intersection processing of scatter points and grid: Project the scatter points of the scatter points of the scatter points of the scatter points of the scatter points onto the grid plane, and obtain the grid plane index of each scatter point through intersection calculation;

[0121] S22. Obtain the set of scattered points within each grid;

[0122] S23. Coarsening process: Based on the scattered point attributes within each grid, the most frequent method is used to achieve discrete coarsening, that is, assigning the attribute that appears most frequently within the grid to that grid.

[0123] S3, coarsening of continuous data.

[0124] S31. Intersection processing of scatter points and grid: Project the scatter points of the scatter points of the scatter points of the scatter points of the scatter points onto the grid plane, and obtain the grid plane index of each scatter point through intersection calculation;

[0125] S32. Obtain the set of scattered points within each grid;

[0126] S33. Coarsening: The averaging method is used to coarse continuous data, that is, the average value of the attribute values ​​of all scattered points in the grid is assigned to the grid.

[0127] S4, Rapid Depositional Phase Modeling.

[0128] S41. Obtain the reservoir grid model Layer information. Based on the zone information represented by the planar diagram, obtain the set of all Layer indices.

[0129] S42. Obtain K information of reservoir grid model. Due to the existence of reverse faults, the same layer may be divided into multiple K layers. Therefore, it is necessary to further obtain all K information.

[0130] S43. Mesh assignment: Based on the coarsened planar properties, assign values ​​to the meshes of all K layers under this planar diagram control, assigning the same value to the same (I, J);

[0131] S44. Repeat S41 to S43 to establish a rapid sedimentary facies model for the entire work area.

[0132] S5. Establishing a rapid trend probability body.

[0133] S51. Sandstone probability surface generation: All grids represented by the surface obtained in S42 are obtained, and the same (I, J) grids are obtained. The sum of their thicknesses is calculated as the formation thickness. The thickness of the sandstone divided by the thickness of the formation is the probability surface of the sandstone.

[0134] S52, Rapid sandstone probabilistic volume generation, generates rapid probabilistic volumes based on the principle of S43;

[0135] S53. Repeat S51 to S52 to establish a rapid probability body for the entire work area.

[0136] S6, Fine-grained probabilistic volume generation.

[0137] S61. Well logging sedimentary facies data coarsening: Well logging sedimentary facies data is coarsened into the reservoir grid. The coarsening method is nearest neighbor interpolation.

[0138] S62. Kriging interpolation generates fine probabilistic volumes of sandstone. Well logging coarsening data is used as the main variable, and fast probabilistic volumes are used as the second variable. The ordinary Kriging interpolation method is used to generate fine probabilistic volumes of sandstone.

[0139] S63. Other lithofacies probability generation. If other probability bodies exist, repeat S51, S52, S61 and S62 to establish the corresponding probability bodies.

[0140] S7, Fine-grained probabilistic post-processing.

[0141] S71. Well logging sedimentary facies ratio statistics: Statistically analyze all sample points of the original well logging data and calculate the proportion of sample points of each type of lithofacies to the total sample points, such as sandstone accounting for 40%, mudstone accounting for 30%, shale accounting for 20%, and coal rock accounting for 10%.

[0142] S72. Post-processing of lithofacies probabilistic volumes: Based on the refined probabilistic volume generation method, three types of probabilistic volumes are established: sandstone, mudstone, and shale. The probability of each lithofacies in a certain grid is denoted as Q1 (sandstone), Q2 (mudstone), and Q3 (shale). The sum of the refined probabilistic volumes of each lithofacies is Q. If Q>1, then let Q=1. The sum of the probabilities of lithofacies without refined probabilistic volumes is 1-Q, that is, the probability of coal is 1-Q. Then the probability of coal in each grid is (1-Q)*0.1 / (0.1+0.3+0.2).

[0143] S8. Sequential indicator simulation establishes a fine sedimentary facies model.

[0144] S81, Simulation Domain Conversion: Converts the reservoir grid model from the structural domain to simulated sedimentary domain data;

[0145] S82. Fine sedimentary facies model: Based on the rapid sedimentary facies model as spatial constraint and the fine probabilistic volume as trend, a fine sedimentary facies model is established using a sequential indicator simulation algorithm.

[0146] S83. Post-processing of sedimentary facies models: Due to the randomness of the sequential indicator simulation algorithm, resulting in poor continuity, a moving smoothing algorithm is used for post-processing. Each grid is obtained from a range of 5 grids in radius. If this grid is not the original coarsened data, the most frequent type among all lithofacies types in the range is taken as the new facies attribute for this grid; if this grid is the original coarsened data, the attribute remains unchanged.

[0147] S84, Tectonic Domain Transformation: The processed sedimentary facies domain data is inversely transformed into the tectonic domain according to the mapping relationship in S81.

[0148] S85, Attribute Model Data Storage, saves the final result as binary.

[0149] It should be noted that the work area is a Cenozoic graben-shaped rift basin with eastward faulting and westward overshooting. It has developed various structural combinations, such as horsts, grabens, "Y"-shaped and reverse "Y"-shaped sections, forming positive units such as anticlines and structural highs, as well as negative units such as depressions and troughs, making the structure very complex.

[0150] First, a detailed structural model was established, comprising 51 sub-layers. Based on this model, a detailed stepped reservoir grid model was generated. Then, a rapid sedimentary model was established according to the sedimentary facies plane of each sub-layer. Figure 8 This is the result of a rapid sedimentary facies model, which shows that the channel boundaries are very clear. Subsequently, probabilistic lithofacies data were established based on well logging lithofacies data and thickness map data. Figure 9 The results are from a lithofacies probabilistic volume model, which reflects the thickness variations of sand bodies. Finally, using coarsened well logging data as hard data, fine probabilistic volume as trend constraints, and a rapid sedimentary facies model as spatial constraints, a sequential indicator simulation algorithm is used to generate a fine sedimentary facies model. Please refer to [link to relevant documentation]. Figure 10 Based on the sedimentary facies model, a phase-controlled porosity property model was established. Please refer to [link to relevant documentation]. Figure 11 It can be seen that the development pattern of the properties is highly consistent with the sedimentary facies, which is in line with geological understanding.

[0151] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by this application.

[0152] Example 2

[0153] Reference Figure 12 This is a structural diagram of a three-dimensional sedimentary phase modeling apparatus based on planar diagram constraints provided in this application. The apparatus includes:

[0154] Secondly, this application discloses a three-dimensional sedimentary phase modeling apparatus based on planar diagram constraints, the apparatus comprising:

[0155] The grid surface coarsening module is used to load sedimentary facies maps and thickness maps into three-dimensional space, project the planar map onto the reservoir grid using spatial coordinate transformation, and coarsen it into reservoir grid surface attributes.

[0156] The rapid sedimentary facies model and lithological probability volume generation module is used to establish rapid sedimentary facies models using planar sedimentary facies maps and to establish lithological probability volume data using planar thickness maps and cokriging interpolation.

[0157] The sedimentary facies modeling module is used to generate sedimentary facies models using well logging sedimentary facies data as hard data, rapid sedimentary facies models as spatial constraints, probabilistic volumes as trend constraints, and sequential indicator simulation algorithms.

[0158] The three-dimensional sedimentary facies modeling scheme based on planar diagram constraints provided in this application comprehensively utilizes information such as well logging facies, planar facies, and thickness maps, and can incorporate geologists' insights between wells, overcoming the problems of mathematical and data-driven approaches in conventional methods, and improving the accuracy of sedimentary facies models.

[0159] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0160] Example 3

[0161] Optionally, this application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0162] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0163] Figure 13 This application provides a block diagram of an electronic device 800. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0164] Reference Figure 13 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0165] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0166] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, images, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0167] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0168] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0169] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0170] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0171] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0172] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast operation information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0173] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0174] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0175] Example 4

[0176] Figure 14A block diagram of another electronic device 1900 provided for this application. For example, electronic device 1900 may be provided as a server.

[0177] Reference Figure 14 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0178] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0179] Example 5

[0180] Fifthly, this application discloses a computer program product in which, when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the preceding aspects.

[0181] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0182] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0183] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0184] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0185] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0186] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0187] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0189] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0190] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A three-dimensional sedimentary facies modeling method based on planar diagram constraints, characterized in that, The method includes: The sedimentary facies map and thickness map are loaded into three-dimensional space, and the planar map is projected onto the reservoir grid using spatial coordinate transformation, and then coarsened to the reservoir grid surface attributes. A rapid sedimentary facies model was established using a planar sedimentary facies map, and lithological probability volume data was established using a planar thickness map and cokriging interpolation. Using well logging sedimentary facies data as hard data, rapid sedimentary facies models as spatial constraints, and probabilistic volumes as trend constraints, a sequential indicator simulation algorithm is used to generate sedimentary facies models.

2. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 1, characterized in that, The steps of loading sedimentary facies maps and thickness maps into three-dimensional space, projecting the planar map onto the reservoir grid using spatial coordinate transformation, and coarsening it to reservoir grid surface attributes include: The geodetic coordinate system of the planar map is converted into a grid coordinate system using the Gaussian projection method to achieve coordinate transformation; Based on the range rules set by the user, the planar grid is scattered according to the set scatter density; The scatter data is assigned values ​​based on the location of the scatter points and the properties of the plane, wherein the properties include at least sedimentary facies type and sand body thickness. The scattered data of the planar map is converted into seismic horizon data format and loaded into three-dimensional space to achieve data format conversion.

3. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 1, characterized in that, The steps of loading sedimentary facies maps and thickness maps into three-dimensional space, projecting the planar map onto the reservoir grid using spatial coordinate transformation, and coarsening it to reservoir grid surface attributes include: The scatter points of the stratigraphic level are projected onto the grid plane, and the grid plane index of each scatter point is obtained by intersection calculation to complete the intersection processing of the scatter points of the stratigraphic level and the grid. Get the set of scattered points within each grid; Based on the scatter point attributes within each grid, the attribute value that appears most frequently within the grid is assigned to the grid, thereby achieving coarsening of discrete data.

4. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 1, characterized in that, The steps of loading sedimentary facies maps and thickness maps into three-dimensional space, projecting the planar map onto the reservoir grid using spatial coordinate transformation, and coarsening it to reservoir grid surface attributes include: The scatter points of the stratigraphic level are projected onto the grid plane, and the grid plane index of each scatter point is obtained by intersection calculation to complete the intersection processing of the scatter points of the stratigraphic level and the grid. Get the set of scattered points within each grid; The average attribute values ​​of all scattered points within the grid are taken and assigned to the grid to achieve continuous data coarsening.

5. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 1, characterized in that, The steps of establishing a rapid sedimentary facies model using a planar sedimentary facies map and establishing lithological probability volume data using a planar thickness map and cokriging interpolation include: A1. Obtain the stratigraphic information of the reservoir grid model, and obtain the set of all stratigraphic indices based on the partition information represented by the plan view; A2. Obtain the layering information of multiple K layers in the reservoir grid model; A3. Based on the coarsened planar properties, assign values ​​to all K-layer meshes under the control of the planar diagram, and assign the same values ​​to meshes with the same mesh coordinates; A4. Repeat steps A1 to A3 to establish a rapid sedimentary facies model for the entire work area.

6. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 5, characterized in that, The steps of establishing a rapid sedimentary facies model using a planar sedimentary facies map and establishing lithological probability volume data using a planar thickness map and cokriging interpolation further include: A5. Based on all the grids represented by the surface obtained in step A2, obtain the grids with the same grid coordinates, calculate the sum of their thicknesses and use it as the formation thickness, and determine the ratio of sandstone thickness to formation thickness as the sandstone probability surface. A6. Generate a rapid sandstone probability body according to step A3; A7. Repeat steps A5 to A6 to establish a rapid probability body for the entire work area.

7. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 6, characterized in that, The steps of establishing a rapid sedimentary facies model using a planar sedimentary facies map and establishing lithological probability volume data using a planar thickness map and cokriging interpolation further include: B1. The nearest neighbor interpolation method is used to coarse the well logging sedimentary facies data into the reservoir grid, thereby achieving the coarsening processing of well logging sedimentary facies data. B2. Using well logging coarsening data as the main variable and fast probability volume as the second variable, a fine probability volume of sandstone is generated using the Kriging interpolation algorithm. B3. For other lithofacies probability volumes, generate other lithofacies probability volumes by repeating steps A5 to A6 and steps B1 to B2.

8. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 7, characterized in that, After establishing lithological probabilistic volume data using planar thickness maps and cokriging interpolation, the following is also included: The original well logging data is statistically analyzed, and the proportion of sample points of each type of lithofacies to the total sample points is calculated to achieve statistical analysis of the proportion of sedimentary facies in well logging. For lithofacies with fine probability volumes, if the sum of the probabilities of each lithofacies in the same grid is greater than 1, then the value of the sum of the probability volumes of the grid is determined to be 1. For lithofacies lacking fine probabilistic volumes, the probability of coal and rock in each grid is determined as follows: (1-Q)*0.1 / (0.1+0.3+0.2) Where Q represents the sum of probabilities of each lithofacies that have a fine probability body, and 1-Q represents the sum of probabilities of lithofacies that do not have a fine probability body.

9. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 1, characterized in that, The steps of generating a sedimentary facies model using well logging sedimentary facies data as hard data, a rapid sedimentary facies model as spatial constraint, a probabilistic volume as trend constraint, and a sequential indicator simulation algorithm include: The reservoir grid model is converted from tectonic domain data to simulated sedimentary domain data, thus achieving the simulation domain conversion; Using the rapid sedimentary facies model as the spatial constraint and the fine probabilistic volume as the trend, a fine sedimentary facies model is established based on the sequential indicator simulation algorithm. The grids within a circular area with a radius of N grids are obtained one by one from each grid, and the grid data in the sedimentary facies model are post-processed using a moving smoothing algorithm; Based on the mapping relationship established when converting the reservoir grid model from the tectonic domain to simulated sedimentary domain data, the processed sedimentary facies domain data is inversely transformed back to the tectonic domain. Store the attribute model data transformed inversely to the constructed domain.

10. The three-dimensional sedimentary facies modeling method based on planar diagram constraints according to claim 9, characterized in that, The step of post-processing the grid data in the sedimentary facies model using a moving smoothing algorithm includes: Determine whether the grid is the original coarsened data; If so, the grid's properties remain unchanged; If not, then the type with the most occurrences among all lithofacies types within the circular area is determined as the attribute of the grid.

11. A three-dimensional sedimentary facies modeling device based on planar diagram constraints, characterized in that, The device includes: The grid surface coarsening module is used to load sedimentary facies maps and thickness maps into three-dimensional space, project the planar map onto the reservoir grid using spatial coordinate transformation, and coarsen it into reservoir grid surface attributes. The rapid sedimentary facies model and lithological probability volume generation module is used to establish rapid sedimentary facies models using planar sedimentary facies maps and to establish lithological probability volume data using planar thickness maps and cokriging interpolation. The sedimentary facies modeling module is used to generate sedimentary facies models using well logging sedimentary facies data as hard data, rapid sedimentary facies models as spatial constraints, probabilistic volumes as trend constraints, and sequential indicator simulation algorithms.

12. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 10.

14. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device implements the method as described in any one of claims 1 to 10.

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