Combination method and device of different dimensional geological models, equipment and storage medium

Abstract

The application relates to the field of oil and gas exploration, and discloses a method, device and equipment for merging geological models of different dimensions and a storage medium. The method comprises the following steps: acquiring a first geological model to be merged and a second geological model to be merged; performing data preprocessing operations on the first geological model to be merged and the second geological model to be merged respectively; and performing grid expansion processing on the first geological model to be merged based on an adaptive grid expansion algorithm. In the application, the low-dimensional geological model is subjected to adaptive grid expansion so as to match the grid structure of the high-dimensional geological model, and meanwhile, the spatial alignment of the two geological models is accurately realized by using a feature point matching and elastic registration algorithm, so that the geological models of different dimensions are quickly and accurately merged together, the tedious manual adjustment work is reduced, the time and cost of model merging are greatly shortened, and the work efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, equipment and storage medium for merging geological models of different dimensions. Background Technology

[0002] In the field of oil and gas exploration technology, with the continuous advancement of exploration techniques, geological model data of different dimensions are becoming increasingly abundant. These geological models typically include two-dimensional and three-dimensional models, which can effectively characterize the complexity and diversity of subsurface geological structures from different perspectives. However, in the actual process of geological analysis and resource assessment, it is often necessary to merge geological models of different dimensions in order to gain a more comprehensive and accurate understanding of subsurface geological conditions.

[0003] Currently, research and practice on geological model merging have made some progress, mainly in two ways: The first is a model merging method based on data format conversion. Its principle is to define a unified intermediate data format and map the geometric structure and attribute values ​​of geological models from different dimensions into this intermediate data format, thereby achieving data integration and model merging. The other is a model merging method based on feature matching. This method focuses on extracting feature information from geological models, such as key geological features like faults and stratigraphic interfaces, and utilizes the spatial location and attribute correlations of this feature information to complete model matching and merging.

[0004] However, in practical applications, model merging methods based on data format conversion struggle to achieve automatic expansion and alignment when dealing with geological models of different dimensions, resulting in low merging efficiency. Furthermore, the significant differences in mesh structure and properties between models of different dimensions can easily lead to data loss or error accumulation during the conversion to a unified intermediate data format, affecting the integrity and accuracy of the final model. For feature-matching-based model merging methods, the difficulty of feature extraction and matching increases dramatically when dealing with geological models with large dimensional differences, especially when there are differences in mesh density and resolution, making it difficult to ensure the geometric continuity and property consistency of the merged model.

[0005] Therefore, existing technologies have significant bottlenecks in merging geological models across different dimensions, making it difficult to achieve efficient and accurate model merging. Summary of the Invention

[0006] This application provides a method and apparatus for merging geological models of different dimensions, the main purpose of which is to improve the merging efficiency and accuracy of geological models of different dimensions during oil and gas exploration.

[0007] To address the aforementioned technical problems, this application provides the following technical solutions: Firstly, this application provides a method for merging geological models of different dimensions, the method comprising: Obtain a first geological model to be merged and a second geological model to be merged, wherein the first geological model to be merged is a low-dimensional geological model and the second geological model to be merged is a high-dimensional geological model; Data preprocessing operations are performed on the first geological model to be merged and the second geological model to be merged, respectively. The first geological model to be merged is subjected to mesh expansion processing based on an adaptive mesh expansion algorithm, wherein the mesh structure of the first geological model to be merged after mesh expansion processing is matched with that of the second geological model to be merged. Extract multiple first feature points corresponding to the first geological model to be merged after the grid expansion process, and extract multiple second feature points corresponding to the second geological model to be merged; Based on the elastic registration algorithm, multiple first feature points and multiple second feature points, spatial alignment processing is performed on the second geological model to be merged and the first geological model to be merged after grid expansion processing. An attribute merging operation is performed on the second geological model to be merged and the first geological model to be merged after grid expansion processing to obtain the target geological model.

[0008] Secondly, this application also provides a device for merging geological models of different dimensions, the device comprising: The acquisition unit is used to acquire a first geological model to be merged and a second geological model to be merged, wherein the first geological model to be merged is a low-dimensional geological model and the second geological model to be merged is a high-dimensional geological model. The first processing unit is used to perform data preprocessing operations on the first geological model to be merged and the second geological model to be merged, respectively. The second processing unit is used to perform mesh expansion processing on the first geological model to be merged based on an adaptive mesh expansion algorithm, wherein the mesh structure of the first geological model to be merged after mesh expansion processing is matched with that of the second geological model to be merged. The extraction unit is used to extract multiple first feature points corresponding to the first geological model to be merged after the grid expansion process, and to extract multiple second feature points corresponding to the second geological model to be merged. The third processing unit is used to perform spatial alignment processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing, based on the elastic registration algorithm, multiple first feature points and multiple second feature points. The fourth processing unit is used to perform attribute merging processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing, so as to obtain the target geological model.

[0009] Thirdly, embodiments of this application provide a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0010] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0011] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0012] By employing the above-described technical solution, the technical solution provided in this application has at least the following advantages: This application provides a method, apparatus, device, and storage medium for merging geological models of different dimensions. The application obtains a first geological model and a second geological model to be merged using a geological model merging application. After performing data preprocessing operations on the first and second geological models respectively, the application first performs mesh expansion processing on the first geological model based on an adaptive mesh expansion algorithm. Then, it extracts multiple first feature points corresponding to the first geological model and multiple second feature points corresponding to the second geological model after mesh expansion processing. Based on an elastic registration algorithm, multiple first feature points, and multiple second feature points, it performs spatial alignment processing on the second geological model and the first geological model after mesh expansion processing. Finally, it performs attribute merging processing on the second geological model and the first geological model after mesh expansion processing to obtain the target geological model. In this application, by adaptively expanding the mesh of the low-dimensional geological model to match the mesh structure of the high-dimensional geological model, and by using feature point matching and elastic registration algorithms to accurately align the two geological models spatially, it is possible to quickly and accurately merge geological models of different dimensions together, reducing tedious manual adjustment work, greatly shortening the time and cost of model merging, and improving work efficiency.

[0013] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0014] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein: Figure 1 A flowchart illustrating a method for merging geological models of different dimensions provided in an embodiment of this application is shown. Figure 2 A flowchart illustrating another method for merging geological models of different dimensions provided in an embodiment of this application is shown; Figure 3 This paper shows a block diagram illustrating the composition of a device for merging geological models of different dimensions provided in an embodiment of this application. Figure 4 A block diagram of another merging device for geological models of different dimensions provided in an embodiment of this application is shown. Detailed Implementation

[0015] Exemplary embodiments of this application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0016] Furthermore, the terms “first,” “second,” and similar terms used in this application do not indicate any order, quantity, or importance, but are merely used to distinguish different parts.

[0017] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains.

[0018] In practical applications, model merging methods based on data format conversion struggle to achieve automatic expansion and alignment when dealing with geological models of different dimensions, resulting in low merging efficiency. Furthermore, the significant differences in mesh structure and properties between models of different dimensions can easily lead to data loss or error accumulation during the conversion to a unified intermediate data format, affecting the integrity and accuracy of the final model. For feature-matching-based model merging methods, the difficulty of feature extraction and matching increases dramatically when dealing with geological models with large dimensional differences, especially when there are differences in mesh density and resolution, making it difficult to ensure the geometric continuity and property consistency of the merged model.

[0019] Therefore, existing technologies have significant bottlenecks in merging geological models across different dimensions, making it difficult to achieve efficient and accurate model merging.

[0020] To improve the efficiency and accuracy of merging geological models of different dimensions during oil and gas exploration, this application provides a method for merging geological models of different dimensions, such as... Figure 1 As shown.

[0021] 101. Obtain the first geological model to be merged and the second geological model to be merged.

[0022] Among them, the first geological model to be merged is a low-dimensional geological model, and the second geological model to be merged is a high-dimensional geological model. Both the first and second geological models to be merged are geological models corresponding to the target area.

[0023] In the embodiments of this application, the execution entity in each step is a geological model merging application running on the target terminal device, wherein the target terminal device may be, but is not limited to, a computer, tablet computer, laptop computer, etc.

[0024] When staff members wish to merge the first and second geological models to be merged, they can input the first and second geological models to be merged into the geological model merging application through the input device of the target terminal device, or send the first and second geological models to be merged to the geological model merging application through other terminal devices; at this time, the geological model merging application can obtain the first and second geological models to be merged.

[0025] 102. Perform data preprocessing operations on the first and second geological models to be merged, respectively.

[0026] After obtaining the first and second geological models to be merged, the geological model merging application needs to perform data preprocessing operations on the first and second geological models to be merged respectively. For example, data cleaning is performed on the first geological model to be merged, and the basic grid parameters corresponding to the first geological model to be merged are calculated; data cleaning is performed on the second geological model to be merged, and the basic grid parameters corresponding to the second geological model to be merged are calculated, and so on.

[0027] 103. The first geological model to be merged is subjected to grid expansion processing based on the adaptive grid expansion algorithm.

[0028] Among them, the grid structure of the first geological model to be merged after grid expansion processing is matched with that of the second geological model to be merged.

[0029] After performing data preprocessing operations on the first and second geological models to be merged, the geological model merging application needs to perform mesh expansion processing on the first geological model to be merged based on the adaptive mesh expansion algorithm, so as to expand the first geological model to be merged into a three-dimensional model that matches the mesh structure of the second geological model to be merged.

[0030] Specifically, in this step, the specific process of performing grid expansion processing on the first geological model to be merged based on the adaptive grid expansion algorithm includes three steps: (1) geometric topology analysis, (2) generating new grid nodes, and (3) determining the attribute values ​​of the new grid nodes.

[0031] (1) Geometric topology analysis: Analyze the geometric structure and topological relationships of the first geological model to be merged, and determine the expansion method and rules in the direction of the missing dimension. For example, for a two-dimensional geological profile model, it is necessary to analyze its geometric characteristics such as the continuity of geological bodies and the dip of strata in the direction perpendicular to the profile plane, so as to determine how to reasonably expand and generate a three-dimensional mesh structure in this direction.

[0032] (2) Generating new mesh nodes: Based on the geometric topology analysis results and the adaptive mesh expansion algorithm, new mesh nodes are generated in the missing dimension direction of the first geological model to be merged. The generation of new nodes should follow geological laws and the continuity requirements of the model to ensure that the expanded mesh can accurately reflect the morphology of the geological body in three-dimensional space. For example, based on the two-dimensional geological profile model, new mesh layers are generated along the direction perpendicular to the profile (assuming it is the z-axis direction) at a certain step size (such as 2-5m). The mesh nodes of each layer correspond to the mesh nodes of the original two-dimensional model in planar position, thus forming a three-dimensional mesh structure.

[0033] (3) Determine the attribute values ​​of new grid nodes: For newly generated grid nodes, calculate their attribute values ​​using appropriate interpolation methods based on the attribute values ​​of adjacent existing nodes. Commonly used interpolation methods include linear interpolation, spline interpolation, and kriging interpolation. For example, in a three-dimensional oil and gas reservoir model, if the porosity, permeability, and other attribute values ​​of the upper and lower grid nodes are known, the corresponding attribute values ​​of the newly generated intermediate grid nodes can be estimated using linear interpolation or kriging interpolation methods, so that the expanded grid model has reasonable continuity and variation trend in the attribute field distribution.

[0034] 104. Extract multiple first feature points corresponding to the first geological model to be merged after grid expansion processing, and extract multiple second feature points corresponding to the second geological model to be merged.

[0035] For any geological model to be merged, its corresponding feature point is a key point that can accurately reflect the location and morphology of the key geological features of the geological model to which it belongs. For example, in a fault model, the fault point on the fault plane or the fault line can be selected as a feature point; in a stratigraphic interface model, the stratigraphic thickness change point or the lithological change point can be selected as a feature point.

[0036] After performing mesh expansion processing on the first geological model to be merged, the geological model merging application needs to extract multiple first feature points corresponding to the first geological model to be merged after mesh expansion processing, and extract multiple second feature points corresponding to the second geological model to be merged.

[0037] 105. Based on the elastic registration algorithm, multiple first feature points and multiple second feature points, spatial alignment processing is performed on the second geological model to be merged and the first geological model to be merged after grid expansion processing.

[0038] After extracting multiple first feature points corresponding to the first geological model to be merged and multiple second feature points corresponding to the second geological model to be merged after mesh expansion processing, the geological model merging application can perform spatial alignment processing on the second geological model to be merged and the first geological model to be merged after mesh expansion processing based on the elastic registration algorithm, multiple first feature points, and multiple second feature points. That is, firstly, the matching relationship (such as spatial distance relationship and geometric relationship) between multiple first feature points and multiple second feature points is determined, and then the spatial alignment processing is performed on the second geological model to be merged and the first geological model to be merged after mesh expansion processing based on the elastic registration algorithm and the matching relationship between multiple first feature points and multiple second feature points.

[0039] 106. Perform attribute merging processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing to obtain the target geological model.

[0040] After spatially aligning the second geological model to be merged and the first geological model to be merged after mesh expansion, the geological model merging application can perform attribute merging operations on the second geological model to be merged and the first geological model to be merged after mesh expansion to obtain the target geological model.

[0041] Specifically, in this step, the process of performing attribute merging on the second geological model to be merged and the first geological model to be merged after mesh expansion is as follows: (1) Establish attribute mapping relationship: First, the attribute data in the second geological model to be merged and the first geological model to be merged after grid expansion are classified and standardized to ensure the consistency and comparability of attribute types. For example, the definitions, units, and dimensions of attributes such as porosity, permeability, and saturation in the two geological models to be merged are unified to facilitate subsequent attribute fusion operations. If the porosity in the first geological model to be merged is expressed as a percentage (0-100%), while that in the second geological model to be merged is expressed as a decimal (0-1), it needs to be converted to the same representation.

[0042] Secondly, based on the spatial grid structure aligned in the preceding steps, a spatial correspondence between the attributes of the two geological models to be merged is established. For each grid node, its corresponding spatial location is found in different models, thereby determining the mapping relationship between attribute values.

[0043] (2) Based on the attribute mapping relationship, perform attribute merging processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing to obtain the target geological model.

[0044] This application provides a method for merging geological models of different dimensions. This method involves obtaining a first geological model to be merged and a second geological model to be merged using a geological model merging application. After performing data preprocessing operations on the first and second geological models, the geological model merging application first performs mesh expansion processing on the first geological model based on an adaptive mesh expansion algorithm. Then, it extracts multiple first feature points corresponding to the first geological model and multiple second feature points corresponding to the second geological model after mesh expansion processing. Based on an elastic registration algorithm, multiple first feature points, and multiple second feature points, it performs spatial alignment processing on the second geological model to be merged and the first geological model after mesh expansion processing. Finally, it performs attribute merging processing on the second geological model to be merged and the first geological model after mesh expansion processing to obtain the target geological model. In this embodiment, by adaptively expanding the low-dimensional geological model to match the grid structure of the high-dimensional geological model, and using feature point matching and elastic registration algorithms to accurately align the two geological models spatially, geological models of different dimensions can be merged quickly and accurately. This reduces tedious manual adjustment work, greatly shortens the time and cost of model merging, and improves work efficiency.

[0045] To illustrate this in more detail, the embodiments of this application provide another method for merging geological models of different dimensions, as follows: Figure 2 As shown.

[0046] 201. Obtain the first geological model to be merged and the second geological model to be merged.

[0047] Regarding step 201, obtaining the first geological model to be merged and the second geological model to be merged, please refer to the relevant description of step 101 above. This embodiment of the application will not repeat it here.

[0048] 202. Perform data preprocessing operations on the first and second geological models to be merged respectively.

[0049] After obtaining the first and second geological models to be merged, the geological model merging application needs to perform data preprocessing operations on the first and second geological models to be merged respectively.

[0050] Specifically, in this step, the data preprocessing operations for the first and second geological models to be merged are as follows: (1) Perform data cleaning on the first and second geological models to be merged respectively: including abnormal attribute value detection and removal and missing attribute value filling.

[0051] The abnormal attribute value detection and removal process can be carried out using statistical analysis, visualization analysis, and spatial filtering methods.

[0052] In statistical analysis, the statistical characteristics of geological model attribute data are calculated, such as mean, standard deviation, variance, skewness, and kurtosis. For each attribute data point, it is determined whether it falls within a reasonable range. For example, porosity data is generally between 0 and 0.5 (for most common rock types), and permeability data is typically in the range of nano-Darcy (nD) to micro-Darcy (μD). Data exceeding these reasonable ranges can be considered outliers. Taking porosity data as an example, the mean (μ) and standard deviation (σ) of all porosity data are calculated, and a threshold range of μ ± 3σ is set. Data points exceeding this range are considered outlier attribute values ​​and are removed.

[0053] For visualization analysis, data visualization tools are used to create scatter plots, histograms, box plots, etc., of geological model attribute data. For example, a scatter plot of porosity versus depth is created to observe the distribution pattern of the data points. If some porosity data points are found to deviate significantly from the mainstream trend (e.g., porosity values ​​suddenly increase or decrease by several times compared to surrounding data points at the same depth), they are marked as abnormal attribute values ​​and processed accordingly.

[0054] For spatial filtering methods, noise is removed from attribute data of geological models with spatial structures. For example, in a 3D geological model, median filtering or mean filtering is used, taking each grid node as the center and considering the attribute values ​​of its surrounding neighboring nodes to recalculate the node's attribute value. For porosity data, if the porosity value of a grid node differs significantly from the porosity values ​​of its surrounding neighboring nodes, it is replaced with the median or mean of the porosity values ​​of the surrounding nodes to eliminate the influence of local noise.

[0055] In the process of filling missing attribute values, it can be done based on the nearest neighbor interpolation method or geostatistical methods, respectively.

[0056] For nearest neighbor interpolation, interpolation is performed using the attribute values ​​of neighboring points around the missing value, based on the spatial correlation of the geological model's attribute data. In a regular grid geological model, if the attribute value of a certain grid node is missing, the missing value can be estimated using linear interpolation, bilinear interpolation, or cubic spline interpolation methods based on the attribute values ​​of its upper, lower, left, right, and front / back adjacent nodes (for 3D models) or left, right, and front / back adjacent nodes (for 2D models).

[0057] For geostatistical methods, geostatistical theories, such as Kriging interpolation, are applied to estimate missing attribute values, considering the spatial autocorrelation of the data. For permeability data, the spatial variogram of the permeability data is first calculated to analyze its spatial structure characteristics. Then, based on the known spatial distribution of permeability data and the variogram model, Kriging interpolation is used to calculate the permeability values ​​at the missing grid nodes, ensuring that the filled permeability data has reasonable spatial continuity and variation trends.

[0058] (2) Statistical analysis of the basic grid parameters corresponding to the first geological model to be merged and the second geological model to be merged: including statistical analysis of grid density, resolution, node coordinates, etc.

[0059] In calculating the mesh density, different operations can be performed depending on the form of the mesh model.

[0060] For regular mesh models, mesh density can be represented by the number of rows and columns of mesh cells (for 2D models) or the number of rows, columns, and layers (for 3D models). For example, a 2D regular mesh model with m rows and n columns of mesh cells has a mesh density of (m×n) mesh cells; a 3D regular mesh model with m rows, n columns, and k layers of mesh cells has a mesh density of (m×n×k) mesh cells.

[0061] For irregular mesh models, calculating mesh density is relatively complex. It typically requires counting the number of mesh cells per unit area or volume. For example, in a two-dimensional irregular mesh, the area covered by the entire model can be calculated, and then the total number of mesh cells within that area can be counted; the mesh density is then the total number of mesh cells divided by the area of ​​the model region. In a three-dimensional irregular mesh, the volume covered by the model can be calculated, and the mesh density is obtained by dividing the total number of mesh cells by the volume.

[0062] In determining the grid resolution, different operations can be performed depending on the form of the grid model.

[0063] For regular mesh models, the resolution of the regular mesh is determined by the spacing of the mesh cells in each dimension. For example, in a two-dimensional regular mesh model, the mesh spacing in the x and y directions is Δx and Δy, respectively; in a three-dimensional regular mesh model, there is also a mesh spacing Δz in the z direction. For instance, if the mesh spacing of a three-dimensional geological model is Δx = 10m, Δy = 10m, and Δz = 2m, then its mesh resolution can be expressed as 10m × 10m × 2m.

[0064] For irregular mesh models, determining the mesh resolution is more complex. The following methods can be used: First, calculate the statistical characteristic values ​​of the side length or side width of all mesh cells, such as the average or median, as an approximation of the mesh resolution. Second, for triangular meshes, the average value of the circumcircle radius or incircle radius of the triangle can be calculated as the mesh resolution. For tetrahedral meshes, the average value of the incircle radius or circumcircle radius of the tetrahedron can be calculated as the mesh resolution.

[0065] In the process of performing node coordinate statistics, the coordinate information of the grid nodes is first extracted from the geological model data file.

[0066] In a regular mesh model, node coordinates can be calculated using formulas based on the initial coordinates, mesh spacing, and mesh row and column indices. For example, for a two-dimensional regular mesh model with initial coordinates (x0, y0) and mesh spacings of Δx and Δy, the coordinates of the mesh node in the i-th row and j-th column are (x0 + j × Δx, y0 + i × Δy). For a three-dimensional regular mesh model with initial coordinates (x0, y0, z0) and mesh spacings of Δx, Δy, and Δz, the coordinates of the mesh node in the i-th row and j-th column, k-th layer are (x0 + j × Δx, y0 + i × Δy, z0 + k × Δz).

[0067] In irregular grid models, node coordinates are extracted directly from the data file. The coordinates of each grid node are typically stored as a list or array, which needs to be read and formatted into an easily analyzable and processable format, such as a data frame or matrix. Then, in some cases, verification and correction of the coordinate data are required. This involves validating the extracted node coordinate data to check for duplicate, missing, or unreasonable coordinate values. If duplicate coordinate values ​​are found, it may be an error in the data storage or extraction process, requiring deduplication. If missing coordinate values ​​are found, interpolation estimation can be performed based on the coordinates of adjacent nodes and the continuity of the geological model. If unreasonable coordinate values ​​are found (such as those exceeding the model space or causing grid intersections or folds), the data needs to be corrected or the grid regenerated.

[0068] 203. The first geological model to be merged is subjected to grid expansion processing based on the adaptive grid expansion algorithm.

[0069] After performing data preprocessing operations on the first and second geological models to be merged, the geological model merging application needs to perform mesh expansion processing on the first geological model to be merged based on the adaptive mesh expansion algorithm, so as to expand the first geological model to be merged into a three-dimensional model that matches the mesh structure of the second geological model to be merged.

[0070] Specifically, in this step, the process of performing mesh expansion processing on the first geological model to be merged based on the adaptive mesh expansion algorithm is as follows: (1) In the missing dimension direction of the first geological model to be merged, generate target grid nodes based on the basic grid parameters corresponding to the first geological model to be merged, the basic grid parameters corresponding to the second geological model to be merged, and the principles of geometric topology: First, based on the basic grid parameters of the first and second geological models to be merged, the missing dimensions of the first geological model relative to the second geological model are determined. For example, if a two-dimensional geological profile model (only x and y directions) is merged with a three-dimensional geological structure model (x, y, and z directions), the two-dimensional model is missing in the z direction (perpendicular to the profile), and mesh expansion is needed in this direction.

[0071] Then, based on the basic grid parameters corresponding to the first and second geological models to be merged, the geometric morphology and topological structure of the first geological model to be merged in the missing dimension direction are analyzed. Factors such as the continuity of the geological body in this direction, the dip and dip angle of the strata, and the changing trend of geological structures are considered. For example, if the geological body exhibits a layered distribution and the dip angle of the strata is small in the missing dimension direction, new grid nodes can be generated in an approximately horizontal or inclined manner; if there are faults or other structures that cause abrupt changes in the geological body in this direction, the direction and density of grid generation need to be adjusted according to the geometric characteristics of the faults.

[0072] Finally, based on the geometric topology analysis results, the layout of the new mesh nodes in the missing dimension is planned. The spacing, number, and connection relationship between the new nodes and the existing nodes are determined. A regular or approximately regular mesh layout is typically used, such as using hexahedral or tetrahedral meshes in 3D extensions. For example, for the 3D extension of layered geological bodies, multiple mesh planes with equal spacing or unequal spacing according to the stratigraphic thickness can be set along the z-direction. The mesh nodes in each plane correspond to the mesh nodes of the original 2D model in the x and y directions, forming a regular hexahedral mesh structure.

[0073] (2) Determine the attribute value corresponding to each target grid node based on the attribute values ​​of adjacent grid nodes: First, select an interpolation method. This involves choosing an appropriate interpolation method based on the type and variation characteristics of the geological attribute. For geological attributes with good continuity (such as porosity and permeability), linear interpolation, bilinear interpolation, or cubic spline interpolation can be used. For attributes with obvious geological interfaces or discontinuous changes (such as lithology), nearest neighbor interpolation or piecewise interpolation based on geological interfaces can be used. For example, in a three-dimensional hydrocarbon reservoir model, if the permeability changes relatively smoothly in the vertical direction, bilinear interpolation can be used to calculate the permeability value at newly generated z-direction grid nodes.

[0074] Then, collect the attribute values ​​of adjacent mesh nodes. Before interpolation calculation, collect the attribute values ​​of adjacent mesh nodes around the target mesh node as the basis for interpolation. For a new mesh node in 3D mesh expansion, its adjacent mesh nodes usually include surrounding nodes in the same plane (adjacent in the x and y directions) and nodes in the upper and lower adjacent layers (adjacent in the z direction, if some expansion layers already exist).

[0075] Finally, interpolation calculations are performed. This involves calculating the attribute values ​​of the target grid node using the selected interpolation method and the collected attribute values ​​of adjacent grid nodes. Taking bilinear interpolation as an example, in a 3D grid, firstly, linear interpolation is performed on the attribute values ​​of adjacent grid nodes in the same plane along the x and y directions to obtain intermediate results; then, linear interpolation is performed along the z direction on the attribute values ​​of the upper and lower layer grid nodes, as well as the intermediate results obtained from the in-plane interpolation, to finally obtain the attribute values ​​of the target grid node. This method ensures that the attribute values ​​of the newly generated grid nodes have reasonable spatial continuity and variation trends, and that their relationship with the attributes of adjacent grid nodes conforms to geological laws.

[0076] 204. Extract multiple first feature points corresponding to the first geological model to be merged after grid expansion processing, and extract multiple second feature points corresponding to the second geological model to be merged.

[0077] After performing mesh expansion processing on the first geological model to be merged, the geological model merging application needs to extract multiple first feature points corresponding to the first geological model to be merged after mesh expansion processing, and extract multiple second feature points corresponding to the second geological model to be merged.

[0078] Specifically, in this step, the process of extracting multiple first feature points corresponding to the first geological model to be merged after mesh expansion processing, and extracting multiple second feature points corresponding to the second geological model to be merged, is as follows: (1) Determine the key geological features corresponding to the first geological model to be merged after grid expansion processing, and determine the key geological features corresponding to the second geological model to be merged. That is, use the attribute data in the geological model to identify key geological features. Taking stratigraphic interfaces as an example, different strata have different properties such as lithology, color, and grain size. The location of stratigraphic interfaces can be determined by analyzing the changes in these properties. In the geological model, sudden changes in properties such as porosity and permeability may also indicate the existence of stratigraphic interfaces or faults. For example, if porosity and permeability data show an abnormal decrease or increase at a certain location, and this change has a certain continuity in adjacent areas, then it is very likely that there is a stratigraphic interface or fault at this location.

[0079] (2) First, based on the type of key geological features and the purpose of the geological model, the selection criteria for key points are formulated. For stratigraphic interfaces, key points can be points of change in stratigraphic thickness, points of abrupt changes in lithology, points of stratigraphic contact relationships, etc.; for faults, key points can be points on fault lines, fault points on fault planes, points where faults intersect stratigraphic interfaces, etc. These key points should have obvious geometric features (such as location, direction, curvature, etc.) and property features (such as porosity, permeability, lithology, etc.). Next, through geometric morphology analysis of key geological features, points with significant geometric features are extracted. For example, in the stratigraphic interface model, the curvature of the stratigraphic interface is calculated, and points with larger curvature are selected as key points. These points are usually located at the bends or abrupt changes in the stratigraphy; in the fault model, points on the fault lines are extracted, and representative points are selected as key points based on the direction and length of the fault lines, such as the starting point, ending point, and inflection point of the fault lines.

[0080] Then, using the attribute data of key geological features, points with significant attribute characteristics are extracted. Specifically, multiple key points are extracted from the key geological features corresponding to the first geological model to be merged after grid expansion processing, and these key points are designated as multiple first feature points. Similarly, multiple key points are extracted from the key geological features corresponding to the second geological model to be merged, and these key points are designated as multiple second feature points. For example, in an oil and gas reservoir geological model, for key geological features containing oil, gas, and water boundaries (such as oil-water interfaces), points on the intersection line between the oil-water interface and the strata can be extracted as key points based on changes in fluid properties (such as saturation). Similarly, in a lithology model, points on lithology pinch-out lines or points with significant changes in lithology thickness are extracted as key points based on lithology type and lithology thickness data. The attribute characteristics of these key points can reflect the important properties and variation patterns of the geological body.

[0081] Furthermore, in some cases, key points extracted based on geometric and attribute features can be comprehensively analyzed to verify their rationality and accuracy. This involves checking whether the key points fully represent the morphological and attribute changes of key geological features, and whether there are any omissions or misselections. For example, in a fault model, if the extracted key points cannot accurately depict the fault's strike and slip direction, the selection criteria and methods for key points need to be re-examined, and the selection of key points supplemented or adjusted to ensure that the key points truly reflect the geometric and mechanical characteristics of the fault. Simultaneously, the extracted key points can be verified and corrected by combining geological expertise and actual geological conditions to improve their quality and reliability.

[0082] 205. Based on the elastic registration algorithm, multiple first feature points and multiple second feature points, spatial alignment processing is performed on the second geological model to be merged and the first geological model to be merged after grid expansion processing.

[0083] After extracting multiple first feature points corresponding to the first geological model to be merged and multiple second feature points corresponding to the second geological model to be merged after mesh expansion processing, the geological model merging application can perform spatial alignment processing on the second geological model to be merged and the first geological model to be merged after mesh expansion processing based on the elastic registration algorithm, multiple first feature points and multiple second feature points.

[0084] Specifically, in this step, the spatial alignment process for the second geological model to be merged and the first geological model to be merged after mesh expansion, based on the elastic registration algorithm, multiple first feature points, and multiple second feature points, is as follows: (1) Calculate the spatial distance and geometric relationship between multiple first feature points and multiple second feature points.

[0085] Specifically, the Euclidean distance between a first feature point and a second feature point can be calculated based on the Euclidean distance formula, or the Manhattan distance can be calculated based on the Manhattan distance formula. This application does not specifically limit the specific calculation method.

[0086] The geometric relationship between two feature points includes: relative position, relative direction, and relative angle.

[0087] (2) Establish multiple feature point matching pairs based on the spatial distance and geometric relationship between multiple first feature points and multiple second feature points.

[0088] If the spatial distance between two feature points is less than a preset spatial distance threshold and their geometric relationship matches, then these two feature points are combined into a matching pair.

[0089] (3) With the goal of minimizing the spatial error between models, elastic deformation adjustment is performed on the second geological model to be merged and the first geological model to be merged after mesh expansion based on the matching of multiple feature points.

[0090] First, an elastic deformation model is established based on the elastic registration algorithm. Establishing a suitable elastic deformation model facilitates subsequent merging operations, such as Thin Plate Spline (TPS) or Free-Form Deformation (FFD). The TPS model assumes that deformation is caused by the bending and stretching of an elastic thin plate, making it suitable for handling geological models with large local deformations. The FFD model divides the model into multiple control points, achieving elastic deformation by adjusting the positions of these control points, making it suitable for handling models with complex deformations.

[0091] Then, with the goal of minimizing the spatial error between models, elastic deformation adjustments are performed on the second geological model to be merged and the first geological model to be merged after mesh expansion, based on multiple feature point matching pairs and an elastic deformation model. That is, an objective function is first established (an optimization function designed to minimize the spatial error between models). By minimizing the objective function, the feature points of the first and second geological models to be merged can be aligned as much as possible in space. The objective function can be defined as the sum of the squares of the distances between all feature point matching pairs, i.e., E = ∑(di). 2 , where di is the distance between the matching pairs of the i-th feature points.

[0092] 206. Perform attribute merging processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing to obtain the target geological model.

[0093] After spatially aligning the second geological model to be merged and the first geological model to be merged after mesh expansion, the geological model merging application can perform attribute merging operations on the second geological model to be merged and the first geological model to be merged after mesh expansion to obtain the target geological model.

[0094] Specifically, in this step, the attribute merging operation is performed on the second geological model to be merged and the first geological model to be merged after mesh expansion processing to obtain the target geological model. The specific process is as follows: (1) Establish attribute mapping relationship: First, the attribute data in the first and second geological models to be merged, after grid expansion processing, are classified and standardized to ensure consistency and comparability of attribute types. For example, the definitions, units, and dimensions of attributes such as porosity, permeability, and saturation in the two geological models are unified to facilitate subsequent attribute fusion operations. If the porosity in the first geological model to be merged is expressed as a percentage (0-100%), while that in the second geological model is expressed as a decimal (0-1), they need to be converted to the same representation.

[0095] Secondly, based on the spatial grid structure aligned in the preceding steps, a spatial correspondence between the attributes of the two geological models to be merged is established. For each grid node, its corresponding spatial location is found in different models, thereby determining the mapping relationship between attribute values.

[0096] (2) For the same attribute, according to the correspondence between multiple first grid nodes and multiple second grid nodes, the attribute values ​​corresponding to multiple first grid nodes and multiple second grid nodes are merged. The merging methods include the following two: 1. Arithmetic mean method: Calculate the arithmetic mean of the attribute values ​​at the corresponding grid nodes.

[0097] 2. Weighted average method: Based on factors such as model reliability, data accuracy, or priority, weights are assigned to the attribute data of different models, and a weighted average is performed.

[0098] (3) For different attributes, establish attribute conversion functions or attribute conversion rules, and convert the attribute values ​​corresponding to multiple first grid nodes and multiple second grid nodes into a unified attribute space according to the attribute conversion functions or attribute conversion rules; and merge the attribute values ​​corresponding to multiple first grid nodes and multiple second grid nodes according to the correspondence between multiple first grid nodes and multiple second grid nodes.

[0099] The establishment of attribute conversion functions or attribute conversion rules includes the following three methods: 1. Based on physical model transformation, establish transformation functions between different attributes using known physical or geological relationships.

[0100] 2. Empirical Formula Method: This method involves converting properties based on empirical formulas derived from statistical analysis of actual geological data. For example, in a specific oil reservoir area, the empirical relationship between porosity and crude oil saturation can be derived from the analysis of numerous core samples: Soil = a × φ + b; (where a and b are empirical coefficients). This formula is then used to convert porosity into crude oil saturation for merging.

[0101] 3. Machine learning methods: When physical models and empirical formulas are difficult to establish, machine learning algorithms (such as neural networks and support vector machines) can be used to train attribute transformation models. For example, sample pairs of known porosity and permeability data can be collected to construct a training dataset, and a neural network model can be trained to learn the nonlinear mapping relationship from porosity to permeability. The trained model can be used to convert porosity attributes in different models into permeability attributes, or to perform other related attribute transformations.

[0102] 207. Based on multi-model joint calculation and fitting algorithm, the target geological model is fitted and optimized.

[0103] In oil and gas reservoir modeling, the interactions and coupling relationships between various physical fields (such as pressure, saturation, and temperature fields) are considered. A multiphysics coupling model can be established based on the steps described above. For example, reservoir numerical simulation software (such as Eclipse and CMG) can be used to simulate multiphase flow, taking the porosity, permeability, and lithology of the geological model as input parameters. Simultaneously, dynamic production data of the reservoir (such as well production, pressure, and water cut) can be combined. By solving the reservoir seepage equations, the seepage process of oil and gas in the reservoir can be simulated, achieving dynamic coupling calculation of the pressure and saturation fields.

[0104] Then, based on the degree of fit between the simulation results and actual production data, the parameters of the target geological model are adjusted. For example, if there is a deviation between the simulated oil well production and the actual production, parameters such as permeability and porosity in the target geological model can be adjusted through sensitivity analysis, or parameters such as the relative permeability curve and formation coefficient in the reservoir numerical simulation can be optimized to make the simulation results closer to the actual production situation, thereby improving the fitting accuracy of the target geological model to the actual characteristics of the oil and gas reservoir.

[0105] In addition, by fitting target parameters such as pressure and saturation into the target geological model, the target geological model can accurately reflect the distribution and flow characteristics of underground fluids.

[0106] Furthermore, as a response to the above Figure 1 and Figure 2 In addition to the implementation of the method shown, another embodiment of this application also provides a device for merging geological models of different dimensions. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. This device is used to ensure that the merging efficiency and accuracy of geological models of different dimensions are improved during oil and gas exploration, specifically as follows... Figure 3 As shown, the device includes: The acquisition unit 31 is used to acquire a first geological model to be merged and a second geological model to be merged, wherein the first geological model to be merged is a low-dimensional geological model and the second geological model to be merged is a high-dimensional geological model. The first processing unit 32 is used to perform data preprocessing operations on the first geological model to be merged and the second geological model to be merged, respectively. The second processing unit 33 is used to perform mesh expansion processing on the first geological model to be merged based on an adaptive mesh expansion algorithm, wherein the mesh structure of the first geological model to be merged after mesh expansion processing is matched with that of the second geological model to be merged. Extraction unit 34 is used to extract multiple first feature points corresponding to the first geological model to be merged after the grid expansion process, and to extract multiple second feature points corresponding to the second geological model to be merged. The third processing unit 35 is used to perform spatial alignment processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing, based on the elastic registration algorithm, multiple first feature points and multiple second feature points. The fourth processing unit 36 ​​is used to perform attribute merging processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing, so as to obtain the target geological model.

[0107] Furthermore, such as Figure 4 As shown, the first processing unit 32 is specifically used to: perform data cleaning processing on the first geological model to be merged and the second geological model to be merged, respectively; The basic grid parameters corresponding to the first geological model to be merged and the basic grid parameters corresponding to the second geological model to be merged are statistically analyzed.

[0108] Furthermore, such as Figure 4 As shown, the second processing unit 33 is specifically used to: generate target grid nodes in the missing dimension direction of the first geological model to be merged based on the grid basic parameters corresponding to the first geological model to be merged, the grid basic parameters corresponding to the second geological model to be merged, and the geometric topology principle; The attribute value corresponding to each target grid node is determined based on the attribute values ​​of adjacent grid nodes.

[0109] Furthermore, such as Figure 4 As shown, extraction unit 34 is specifically used to: determine the key geological features corresponding to the first geological model to be merged after grid expansion processing, and determine the key geological features corresponding to the second geological model to be merged. Multiple key points are extracted from the key geological features corresponding to the first geological model to be merged after grid expansion processing, and the multiple key points are determined as multiple first feature points. Multiple key points are extracted from the key geological features corresponding to the second geological model to be merged, and the multiple key points are determined as multiple second feature points.

[0110] Furthermore, such as Figure 4 As shown, the third processing unit 35 is specifically used to: calculate the spatial distance and geometric relationship between multiple first feature points and multiple second feature points; Based on the spatial distance and geometric relationship between multiple first feature points and multiple second feature points, multiple feature point matching pairs are established; With the goal of minimizing the spatial error between models, elastic deformation adjustment is performed on the second geological model to be merged and the first geological model to be merged after mesh expansion processing based on the matching of multiple feature points.

[0111] Furthermore, such as Figure 4 As shown, the first geological model to be merged after grid expansion processing contains multiple first grid nodes, and the second geological model to be merged contains multiple second grid nodes; the fourth processing unit 36 ​​is specifically used to: for the same attribute, according to the correspondence between the multiple first grid nodes and the multiple second grid nodes, merge the attribute values ​​corresponding to the multiple first grid nodes and the attribute values ​​corresponding to the multiple second grid nodes. For different attributes, an attribute conversion function or attribute conversion rule is established. Based on the attribute conversion function or attribute conversion rule, the attribute values ​​corresponding to multiple first grid nodes and multiple second grid nodes are converted into a unified attribute space. Based on the correspondence between multiple first grid nodes and multiple second grid nodes, the attribute values ​​corresponding to multiple first grid nodes and multiple second grid nodes are merged.

[0112] Furthermore, such as Figure 4 As shown, the device also includes: The fifth processing unit 37 is used to perform fitting optimization processing on the target geological model based on multi-model joint calculation and fitting algorithm.

[0113] This application provides a method and apparatus for merging geological models of different dimensions. The method involves obtaining a first geological model to be merged and a second geological model to be merged through a geological model merging application. After performing data preprocessing operations on both models, the application first performs mesh expansion processing on the first model based on an adaptive mesh expansion algorithm. Then, it extracts multiple first feature points corresponding to the first and second geological models after the mesh expansion processing. Based on an elastic registration algorithm, multiple first feature points, and multiple second feature points, it performs spatial alignment processing on the second geological model and the first geological model after mesh expansion processing. Finally, it performs attribute merging processing on the second geological model and the first geological model after mesh expansion processing to obtain the target geological model. In this embodiment, by adaptively expanding the low-dimensional geological model to match the grid structure of the high-dimensional geological model, and using feature point matching and elastic registration algorithms to accurately align the two geological models spatially, geological models of different dimensions can be merged quickly and accurately. This reduces tedious manual adjustment work, greatly shortens the time and cost of model merging, and improves work efficiency.

[0114] This application provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the above-described method for merging geological models of different dimensions.

[0115] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for merging geological models of different dimensions.

[0116] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for merging geological models of different dimensions.

[0117] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0121] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0122] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0123] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0124] It should also be noted that 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 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.

[0125] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for merging geological models of different dimensions, characterized in that, The method includes: Obtain a first geological model to be merged and a second geological model to be merged, wherein the first geological model to be merged is a low-dimensional geological model and the second geological model to be merged is a high-dimensional geological model; Data preprocessing operations are performed on the first geological model to be merged and the second geological model to be merged, respectively. The first geological model to be merged is subjected to mesh expansion processing based on an adaptive mesh expansion algorithm, wherein the mesh structure of the first geological model to be merged after mesh expansion processing is matched with that of the second geological model to be merged. Extract multiple first feature points corresponding to the first geological model to be merged after the grid expansion process, and extract multiple second feature points corresponding to the second geological model to be merged; Based on the elastic registration algorithm, multiple first feature points and multiple second feature points, spatial alignment processing is performed on the second geological model to be merged and the first geological model to be merged after grid expansion processing. An attribute merging operation is performed on the second geological model to be merged and the first geological model to be merged after grid expansion processing to obtain the target geological model.

2. The method according to claim 1, characterized in that, The data preprocessing operations performed on the first and second geological models to be merged respectively include: Data cleaning processes were performed on the first geological model to be merged and the second geological model to be merged, respectively. The basic grid parameters corresponding to the first geological model to be merged and the basic grid parameters corresponding to the second geological model to be merged are statistically analyzed.

3. The method according to claim 2, characterized in that, The mesh expansion process for the first geological model to be merged based on the adaptive mesh expansion algorithm includes: In the missing dimension direction of the first geological model to be merged, target grid nodes are generated based on the grid basic parameters corresponding to the first geological model to be merged, the grid basic parameters corresponding to the second geological model to be merged, and geometric topology principles. The attribute value corresponding to each target grid node is determined based on the attribute values ​​of adjacent grid nodes.

4. The method according to claim 1, characterized in that, The step of extracting multiple first feature points corresponding to the first geological model to be merged after grid expansion processing, and extracting multiple second feature points corresponding to the second geological model to be merged, includes: The key geological features corresponding to the first geological model to be merged after grid expansion processing are determined, and the key geological features corresponding to the second geological model to be merged are determined. Multiple key points are extracted from the key geological features corresponding to the first geological model to be merged after grid expansion processing, and the multiple key points are determined as multiple first feature points. Multiple key points are extracted from the key geological features corresponding to the second geological model to be merged, and the multiple key points are determined as multiple second feature points.

5. The method according to claim 1, characterized in that, The spatial alignment process, based on the elastic registration algorithm, multiple first feature points, and multiple second feature points, for the second geological model to be merged and the first geological model to be merged after mesh expansion processing, includes: Calculate the spatial distance and geometric relationship between multiple first feature points and multiple second feature points; Based on the spatial distance and geometric relationship between multiple first feature points and multiple second feature points, multiple feature point matching pairs are established; With the goal of minimizing the spatial error between models, elastic deformation adjustment is performed on the second geological model to be merged and the first geological model to be merged after mesh expansion processing based on the matching of multiple feature points.

6. The method according to claim 1, characterized in that, The first geological model to be merged after mesh expansion processing contains multiple first mesh nodes, and the second geological model to be merged contains multiple second mesh nodes; the attribute merging operation on the second geological model to be merged and the first geological model to be merged after mesh expansion processing to obtain the target geological model includes: For the same attribute, the attribute values ​​corresponding to the multiple first grid nodes and the multiple second grid nodes are merged according to the correspondence between the multiple first grid nodes and the multiple second grid nodes; For different attributes, an attribute conversion function or attribute conversion rule is established. Based on the attribute conversion function or attribute conversion rule, the attribute values ​​corresponding to multiple first grid nodes and multiple second grid nodes are converted into a unified attribute space. Based on the correspondence between multiple first grid nodes and multiple second grid nodes, the attribute values ​​corresponding to multiple first grid nodes and multiple second grid nodes are merged.

7. The method according to any one of claims 1-6, characterized in that, After performing attribute merging processing on the second geological model to be merged and the first geological model to be merged after mesh expansion processing to obtain the target geological model, the method further includes: The target geological model is optimized by combining multi-model calculations and fitting algorithms.

8. A device for merging geological models of different dimensions, characterized in that, The device includes: The acquisition unit is used to acquire a first geological model to be merged and a second geological model to be merged, wherein the first geological model to be merged is a low-dimensional geological model and the second geological model to be merged is a high-dimensional geological model. The first processing unit is used to perform data preprocessing operations on the first geological model to be merged and the second geological model to be merged, respectively. The second processing unit is used to perform mesh expansion processing on the first geological model to be merged based on an adaptive mesh expansion algorithm, wherein the mesh structure of the first geological model to be merged after mesh expansion processing is matched with that of the second geological model to be merged. The extraction unit is used to extract multiple first feature points corresponding to the first geological model to be merged after the grid expansion process, and to extract multiple second feature points corresponding to the second geological model to be merged. The third processing unit is used to perform spatial alignment processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing, based on the elastic registration algorithm, multiple first feature points and multiple second feature points. The fourth processing unit is used to perform attribute merging processing on the second geological model to be merged and the first geological model to be merged after grid expansion processing, so as to obtain the target geological model.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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