Automatic modeling method for salt cavern hydrogen storage and related equipment

Abstract

This invention discloses an automatic modeling method and related equipment for salt cavern hydrogen storage reservoirs, comprising: constructing a first digital image model based on point cloud data of the salt cavern hydrogen storage reservoir, wherein the point cloud data is acquired based on sonar detection, and the first digital image model is used to characterize the cavity morphology of the salt cavern hydrogen storage reservoir; fusing the interpretation results of well logging data of the salt cavern hydrogen storage reservoir into the first digital image model to determine a second digital image model, wherein the interpretation results of well logging data are used to characterize the distribution of rock strata properties of the salt cavern hydrogen storage reservoir, and the second digital image model characterizes the cavity morphology and rock strata property distribution of the salt cavern hydrogen storage reservoir, wherein different rock strata property distributions are distinguished by different pixel label values; converting the second digital image model into a target computational grid model based on the equivalent transformation between image pixels and grid cells, wherein the grid cells of the target computational grid model are classified according to the pixel label values ​​in the second digital image model and stored in different component sets respectively.

Description

Technical Field

[0001] This invention relates to the field of underground energy storage engineering, and in particular to an automatic modeling method and related equipment for salt cavern hydrogen storage facilities. Background Technology

[0002] Hydrogen, as a clean secondary energy source with high energy density and zero carbon emissions, is of strategic significance to my country's energy security and energy transition. Its large-scale safe storage is a key issue that urgently needs to be addressed. Among various hydrogen storage technologies, underground hydrogen storage is considered the best way to achieve large-scale hydrogen storage due to its advantages such as large capacity, long cycle, low cost, and high safety. Salt caverns, with their good creep properties, low porosity, and strong self-healing ability, are considered ideal geological carriers for hydrogen storage. However, my country's salt rock layers have unfavorable geological conditions such as thin salt layers, high impurity content, and many interlayers, which bring theoretical and technical challenges to the construction of salt cavern hydrogen storage facilities. Compared to costly and complex physical experiments, numerical simulation technology provides a low-cost, efficient, and accurate research method for evaluating the storage potential, stability, effectiveness, and operational conditions of salt cavern hydrogen storage facilities. However, existing modeling methods typically simplify salt cavern hydrogen storage facilities into ideal geometric shapes such as spheres, cylinders, or pears, or construct them based solely on limited detection parameters. These methods fail to accurately reflect the irregular cavity morphology of underground salt caverns. Therefore, numerical simulation results obtained based on such simplified models are difficult to accurately support a reliable assessment of the storage potential, long-term stability, sealing effectiveness, and injection-production operational conditions of salt cavern hydrogen storage facilities. Currently, there is a lack of a better method for modeling salt cavern hydrogen storage facilities. Summary of the Invention

[0003] In view of the above problems, the present invention provides an automatic modeling method and related equipment for salt cavern hydrogen storage reservoirs, the main purpose of which is to solve the problem of the lack of a better modeling method for salt cavern hydrogen storage reservoirs.

[0004] To address at least one of the aforementioned technical problems, in a first aspect, the present invention provides an automatic modeling method for salt cavern hydrogen storage reservoirs, the method comprising: A first digital image model is constructed based on point cloud data of a salt cavern hydrogen storage reservoir, wherein the point cloud data is acquired based on sonar detection, and the first digital image model is used to characterize the cavity morphology of the salt cavern hydrogen storage reservoir. The well logging data interpretation results of the salt cavern hydrogen storage reservoir are fused into the first digital image model to determine the second digital image model. The well logging data interpretation results are used to characterize the rock layer property distribution of the salt cavern hydrogen storage reservoir. The second digital image model characterizes the cavity morphology and rock layer property distribution of the salt cavern hydrogen storage reservoir. Different rock layer property distributions are distinguished by different pixel label values. The second digital image model is converted into a target computational grid model based on the equivalent conversion between image pixels and grid cells. The grid cells of the target computational grid model are classified according to the pixel label values ​​in the second digital image model and stored in different component sets.

[0005] Optionally, the construction of the first digital image model based on the point cloud data of the salt cavern hydrogen storage reservoir includes: The salt cavern hydrogen storage reservoir is subjected to sonar detection to obtain raw point cloud data represented in cylindrical coordinates, wherein the raw point cloud data includes distance, angle and depth; Convert the original point cloud data into a Cartesian coordinate system; Determine the maximum and minimum values ​​in the x, y, and z directions of the Cartesian coordinate system, respectively; The dimensions of the salt cavern hydrogen storage reservoir in the x, y, and z directions are determined based on the difference between the maximum and minimum values ​​in each direction, respectively. A grid array with predefined intervals and uniformly distributed grid points is used, wherein the size of the grid array is larger than the size of the salt cavern hydrogen storage tank.

[0006] Optionally, the construction of the first digital image model based on the point cloud data of the salt cavern hydrogen storage reservoir includes: Connect all wall points measured on each depth horizontal plane of the salt cavern hydrogen storage reservoir to form a closed polygon, wherein the closed polygon is used to characterize the outline shape of the salt cavern hydrogen storage reservoir on the depth horizontal plane. Linear interpolation technology is used to complete the closed polygons between every two adjacent depth horizontal planes to determine the three-dimensional morphology of the salt cavern hydrogen storage reservoir. Using the three-dimensional shape of the cavity as the boundary, each grid point in the grid is divided into external and internal points of the salt cavern hydrogen storage reservoir to construct a first digital image model. The external points are the surrounding rock areas outside the cavity of the salt cavern hydrogen storage reservoir, and the internal points are the salt cavern cavity of the salt cavern hydrogen storage reservoir. The pixel label values ​​of the external and internal points of the first digital image model are different.

[0007] Optionally, the step of fusing the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model includes: The rock layer property distribution of the salt cavern hydrogen storage reservoir was determined based on the interpretation results of the well logging data. A rock strata data relationship table for the first digital image model is constructed based on the distribution of rock strata attributes, wherein different rock strata have different pixel label values. The rock strata data relationship table includes: the top and bottom depths and thicknesses of each rock stratum, the pixel layer index of each rock stratum in the z-direction, the pixel label values ​​of each rock stratum, and the physical properties of each rock stratum.

[0008] Optionally, the step of fusing the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model includes: Iterate through every pixel in the first digital image model; When the pixel is an external point, the rock layer to which the pixel belongs is determined by referring to the rock layer data relationship table; The pixel label value of the pixel is changed to the pixel label value of the rock stratum to construct the second digital image model.

[0009] Optionally, the step of converting the second digital image model into the target computational grid model based on the equivalent transformation between image pixels and grid cells includes: Each pixel in the second digital image model is converted into a finite element mesh element; In the case of converting each pixel in the second digital image model into the finite element mesh unit, the converted finite element mesh units are classified based on the pixel label value and stored in different component sets to form the target computational mesh model.

[0010] Optionally, the classification includes the salt cavern cavity and rock stratum type of the salt cavern hydrogen storage reservoir.

[0011] Secondly, embodiments of the present invention also provide an automatic modeling device for salt cavern hydrogen storage reservoirs, comprising: A construction unit is used to construct a first digital image model based on point cloud data of a salt cavern hydrogen storage reservoir, wherein the point cloud data is acquired based on sonar detection, and the first digital image model is used to characterize the cavity morphology of the salt cavern hydrogen storage reservoir. The determining unit is used to integrate the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model. The well logging data interpretation results are used to characterize the rock layer property distribution of the salt cavern hydrogen storage reservoir, and the second digital image model characterizes the cavity morphology and rock layer property distribution of the salt cavern hydrogen storage reservoir. Different rock layer property distributions are distinguished by different pixel label values. A conversion unit is used to convert the second digital image model into a target computational grid model based on the equivalent conversion between image pixels and grid cells, wherein the grid cells of the target computational grid model are classified according to the pixel label values ​​in the second digital image model and stored in different component sets respectively.

[0012] To achieve the above objectives, according to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein, when the program is executed by a processor, the steps of the above-described automatic modeling method for salt cavern hydrogen storage reservoirs are implemented.

[0013] To achieve the above objectives, according to a fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to invoke program instructions in the memory to execute the steps of the above-described automatic modeling method for salt cavern hydrogen storage reservoirs.

[0014] By employing the above technical solution, the automatic modeling method and related equipment for salt cavern hydrogen storage provided by this invention address the current lack of a better modeling method for salt cavern hydrogen storage. This invention constructs a first digital image model based on point cloud data of the salt cavern hydrogen storage, wherein the point cloud data is acquired based on sonar detection. The first digital image model is used to characterize the cavity morphology of the salt cavern hydrogen storage. The interpretation results of well logging data of the salt cavern hydrogen storage are fused into the first digital image model to determine a second digital image model. The interpretation results of the well logging data are used to characterize the rock layer property distribution of the salt cavern hydrogen storage. The second digital image model characterizes both the cavity morphology and rock layer property distribution of the salt cavern hydrogen storage, wherein different rock layer property distributions are distinguished by different pixel label values. Based on the equivalent conversion between image pixels and grid cells, the second digital image model is converted into a target computational grid model. The grid cells of the target computational grid model are classified according to the pixel label values ​​in the second digital image model and stored in different component sets. In the above scheme, a three-dimensional digital image model is first constructed using point cloud data of salt cavern cavities directly acquired by sonar detection. This model is generated directly based on real detection data, ensuring that the represented cavity morphology originates from actual scanning rather than geometric assumptions, reducing bias caused by shape simplification. Then, the rock layer property distribution information obtained from well logging data interpretation is integrated into this model, which already possesses a realistic geometric shape. By assigning label values ​​representing different rock layers to the surrounding rock areas in the model, geological attributes are correlated with spatial location, transforming the model from a simple geometric entity into a comprehensive carrier encompassing both cavity morphology and stratigraphic property distribution. Finally, based on the equivalent conversion between image pixels and finite element mesh units, the information-rich digital image is automatically converted into a computational mesh model, and the mesh units are classified according to pixel label values. This directly generates a model with predefined material groupings, which can be directly used for numerical simulation, providing a model foundation with higher consistency with real conditions for subsequent accurate analysis.

[0015] Correspondingly, the automatic modeling device, equipment, and computer-readable storage medium for salt cavern hydrogen storage provided in the embodiments of the present invention also have the above-mentioned technical effects.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating an automatic modeling method for a salt cavern hydrogen storage reservoir provided by an embodiment of the present invention is shown. Figure 2 This diagram illustrates a sonar detection method for a salt cavern hydrogen storage reservoir according to an embodiment of the present invention. Figure 3 This diagram illustrates the outline shape of a salt cavern cavity on a single-depth horizontal plane according to an embodiment of the present invention. Figure 4 This diagram shows a cross-sectional view of a first digital image model at x=100 according to an embodiment of the present invention. Figure 5 This diagram illustrates a volume rendering of a first digital image model provided by an embodiment of the present invention. Figure 6 This shows a schematic cross-sectional view of a second digital image model provided in an embodiment of the present invention at x=100; Figure 7 This figure shows a volume rendering illustration of a second digital image model provided in an embodiment of the present invention; Figure 8 This diagram shows a cross-sectional view of a target computational grid model at x=100 according to an embodiment of the present invention. Figure 9 This diagram shows an overall representation of a target computational grid model provided by an embodiment of the present invention. Figure 10 This diagram shows a schematic block diagram of the composition of an automatic modeling device for a salt cavern hydrogen storage facility provided in an embodiment of the present invention; Figure 11 This diagram illustrates the composition of an electronic device for automatic modeling of a salt cavern hydrogen storage reservoir, as provided in an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0019] To address the current lack of a better modeling method for salt cavern hydrogen storage reservoirs, this invention provides an automatic modeling method for salt cavern hydrogen storage reservoirs, such as... Figure 1 As shown, the method includes: S101. Construct a first digital image model based on point cloud data of the salt cavern hydrogen storage reservoir, wherein the point cloud data is acquired based on sonar detection, and the first digital image model is used to characterize the cavity morphology of the salt cavern hydrogen storage reservoir. In one embodiment, constructing the first digital image model based on point cloud data from a salt cavern hydrogen storage reservoir includes: The salt cavern hydrogen storage reservoir is subjected to sonar detection to obtain raw point cloud data represented in cylindrical coordinates, wherein the raw point cloud data includes distance, angle and depth; Convert the original point cloud data into a Cartesian coordinate system; Determine the maximum and minimum values ​​in the x, y, and z directions of the Cartesian coordinate system, respectively; The dimensions of the salt cavern hydrogen storage reservoir in the x, y, and z directions are determined based on the difference between the maximum and minimum values ​​in each direction, respectively. A grid array with predefined intervals and uniformly distributed grid points is used, wherein the size of the grid array is larger than the size of the salt cavern hydrogen storage tank.

[0020] like Figure 2 As shown, the sonar detection device begins scanning from the top of the salt cavern cavity, descending layer by layer at fixed vertical intervals (which can be 2m). At each depth plane, the detector performs a 360-degree rotation scan at specific angular intervals. The system records the distance d from the sonar detection device to the cavity wall, the scanning angle θ, and the current detection depth h, thus obtaining raw point cloud data represented in cylindrical coordinates (d, θ, h). This detection method can completely capture the three-dimensional spatial information of the salt cavern cavity wall, providing a data foundation for subsequent reconstruction of the realistic cavity morphology.

[0021] For example, the above steps utilize sonar detection technology to acquire three-dimensional spatial information of the walls of the salt cavern hydrogen storage chamber. This process involves deploying sonar detection equipment, scanning layer by layer from the top of the salt cavern chamber at fixed vertical intervals, rotating the detector at specific angular intervals on each depth plane, and recording its distance to the chamber wall, the corresponding horizontal angle, and the current detection depth, thereby obtaining a raw point cloud dataset represented in cylindrical coordinates. This point cloud data, containing distance, angle, and depth information, completely captures the spatial positional characteristics of the chamber wall. To facilitate subsequent computational processing, this cylindrical coordinate data needs to be converted to three-dimensional coordinates in Cartesian coordinates. Then, by analyzing the spatial distribution of all point cloud data, its boundary positions in the x, y, and z directions—that is, the maximum and minimum values—are determined, and the actual dimensions of the salt cavern chamber itself in these three dimensions are calculated. Based on this, a larger, regular three-dimensional spatial model that can completely surround the salt cavern chamber needs to be established. By generating a uniformly distributed grid of points within this space at preset intervals, a basic spatial framework is provided for the subsequent construction of a digital image model.

[0022] This application acquires raw spatial data of a salt cavern cavity through sonar detection. Specifically, the sonar device descends layer by layer within the salt cavern at fixed depth intervals (e.g., every 2 meters), performing a 360-degree rotational scan at constant angular intervals on each depth plane, completely recording the coordinate data of each point on the cavity contour at that depth. After obtaining the raw point cloud, the point cloud data in the cylindrical coordinate system is converted to the Cartesian coordinate system, which is more suitable for 3D modeling, through mathematical coordinate transformation. This conversion allows the point cloud data to be represented in a more intuitive x, y, z coordinate form. Next, all the transformed point cloud data are analyzed to determine its spatial distribution range in the x, y, and z directions, i.e., to find the minimum and maximum values ​​in each direction. By calculating the difference between the maximum and minimum values ​​in each direction, the actual physical dimensions of the salt cavern cavity in the three dimensions are accurately obtained. Based on this dimensional information, a cubic spatial model larger than the actual size of the cavity is created. Within this space, a uniformly distributed array of grid points is generated at preset intervals to ensure that the entire salt cavern cavity is completely contained within the grid space and located at the center, laying the foundation for subsequent cavity morphology reconstruction.

[0023] The steps for converting the original point cloud data into a Cartesian coordinate system are as follows:

[0024]

[0025]

[0026] In this transformation, the x-coordinate in the Cartesian coordinate system is calculated by multiplying the distance d in the cylindrical coordinate system by the cosine of the angle θ; the y-coordinate in the Cartesian coordinate system is calculated by multiplying the distance d by the sine of the angle θ; and the z-coordinate in the Cartesian coordinate system is directly taken as the depth h in the cylindrical coordinate system. Here, d is the distance from the sonar detector to the wall of the salt cavern, θ is the detector's scanning angle, and h is the detection depth. Through this coordinate transformation, the original point cloud data (containing distance, angle, and depth) represented in the cylindrical coordinate system is converted into Cartesian coordinate system point cloud data (containing x, y, and z coordinates), which is more suitable for 3D modeling, providing a foundation for subsequent processing.

[0027] The steps described above for determining the dimensions of the salt cavern hydrogen storage reservoir in the x, y, and z directions based on the difference between the maximum and minimum values ​​in each direction are as follows:

[0028]

[0029]

[0030] Where, x max and x min These represent the maximum and minimum values ​​of the point cloud data in the x-direction of the Cartesian coordinate system, respectively, and the y-direction values ​​are respectively... max and y min These represent the maximum and minimum values ​​of the point cloud data in the y-direction, respectively, and the z-direction... max and z min These represent the maximum and minimum values ​​of the point cloud data in the z-direction, respectively. The size of the salt cavern hydrogen storage reservoir in the x-direction is calculated from the difference between the maximum and minimum values ​​in the x-direction; the size in the y-direction is calculated from the difference between the maximum and minimum values ​​in the y-direction; and the size in the z-direction is calculated from the difference between the maximum and minimum values ​​in the z-direction. Through these calculations, the actual physical dimensions of the salt cavern cavity in the three directions are obtained, providing an accurate spatial reference for subsequent creation of the grid and construction of the digital image model.

[0031] Specifically, to ensure the feasibility of the numerical simulation and balance computational accuracy with resource consumption, this application uniformly sets the dimensions of the reconstructed salt cavern hydrogen storage model in the x, y, and z directions to Lx=Ly=Lz=200m. To ensure that the salt cavern cavity remains centrally located in the model space, uniformly distributed background grid points are generated at 1m intervals in the x, y, and z directions, respectively, thus forming the basic spatial framework for the subsequent construction of the digital image model.

[0032] The steps for predefining a grid of uniformly distributed grid points at preset intervals are as follows:

[0033]

[0034]

[0035] Among them, L x L y and L z These represent the dimensions of the grid lattice in the x, y, and z directions, respectively. x l y and l z These represent the actual dimensions of the salt cavern cavity in the x, y, and z directions, respectively. The distribution range of the grid points in the x-direction is determined by the range from the minimum value of the salt cavern cavity in the x-direction minus half the size difference to the maximum value plus half the size difference, ensuring that the size of the grid point array is larger than the cavity size and that the cavity is centered. The distribution range of the grid points in the y-direction is determined by the range from the minimum value of the salt cavern cavity in the y-direction minus half the size difference to the maximum value plus half the size difference. The distribution range of the grid points in the z-direction is determined by the range from the minimum value of the salt cavern cavity in the z-direction minus half the size difference to the maximum value plus half the size difference.

[0036] By employing the aforementioned technical solutions, through sonar detection and data conversion processing, it is possible to ensure that the obtained point cloud data fully reflects the true three-dimensional morphology of the salt cavern cavity, reducing data bias caused by insufficient sampling. Converting cylindrical coordinates to Cartesian coordinates facilitates subsequent meshing and spatial analysis, improving the efficiency and accuracy of data processing. By accurately calculating the cavity's spatial dimensions and creating an appropriate background mesh, a reliable spatial benchmark is provided for subsequently constructing a digital image model that accurately reflects the cavity's morphology. This establishes a sound geometric foundation for subsequent rock layer attribute assignment and mesh conversion, contributing to improved reliability of the final numerical simulation results.

[0037] In one embodiment, constructing the first digital image model based on point cloud data from a salt cavern hydrogen storage reservoir includes: Connect all wall points measured on each depth horizontal plane of the salt cavern hydrogen storage reservoir to form a closed polygon, wherein the closed polygon is used to characterize the outline shape of the salt cavern hydrogen storage reservoir on the depth horizontal plane. Linear interpolation technology is used to complete the closed polygons between every two adjacent depth horizontal planes to determine the three-dimensional morphology of the salt cavern hydrogen storage reservoir. Using the three-dimensional shape of the cavity as the boundary, each grid point in the grid is divided into external and internal points of the salt cavern hydrogen storage reservoir to construct a first digital image model. The external points are the surrounding rock areas outside the cavity of the salt cavern hydrogen storage reservoir, and the internal points are the salt cavern cavity of the salt cavern hydrogen storage reservoir. The pixel label values ​​of the external and internal points of the first digital image model are different.

[0038] For example, the above steps reconstruct the shape and spatially divide the acquired point cloud data. Here, a closed polygon refers to a continuous boundary line formed by connecting all wall points at a single detection depth horizontal plane in sequence according to the detection angle, used to accurately represent the actual contour shape of the cross-section; the cavity's three-dimensional morphology refers to a continuous closed surface formed by completing the contours of all adjacent depths, fully reflecting the true spatial morphology of the salt cave cavity; external points specifically refer to grid points located in the surrounding rock area outside the salt cave cavity, and internal points specifically refer to grid points located inside the salt cave cavity; pixel label values ​​refer to the distinguishing identifiers assigned to grid points, used to clearly represent spatial location attributes in the digital image model.

[0039] like Figure 3 The diagram shows the outline of a salt cavern cavity on a single-depth horizontal plane. On this depth plane, all wall points obtained by sonar detection are connected in the order of detection to form a closed polygon, which accurately represents the actual outline shape at this depth. The scatter plots represent the distribution of sonar detector sampling points, showing that the salt cavern cavity exhibits a distinctly irregular geometric shape, consistent with actual geological conditions. Furthermore, the diagram clearly shows that the salt cavern cavity possesses a significantly irregular and complex geometric shape. This indicates that existing modeling methods that simplify salt cavern hydrogen storage cavities into regular, ideal geometric shapes such as spheres, cylinders, or pears are insufficient to accurately reflect the actual, irregular spatial distribution characteristics of underground salt caverns.

[0040] This application uses point cloud data obtained from sonar detection to accurately construct the three-dimensional topography of a cavity. Specifically, at each detected depth level, all wall points acquired at that depth are first connected according to the sequence of detector scans, forming a closed polygonal outline reflecting the true shape of the cross-section. Since sonar detection uses fixed vertical interval sampling, there are numerous unscanned depth regions between adjacent detection layers. This application employs linear interpolation technology to calculate the outline shape of the missing depth based on the outline shape characteristics of two adjacent detected depths, thereby constructing a continuous and complete three-dimensional topography of the salt cavern cavity. Based on this, using the reconstructed three-dimensional cavity topography as a boundary, the spatial relationships of the created background grid are precisely determined: grid points located outside the cavity surface are marked as external points, representing the surrounding rock region; grid points located inside the cavity surface are marked as internal points, representing the hydrogen storage cavity space. Finally, by assigning different pixel label values ​​to external and internal points, a first digital image model that accurately reflects the true shape of the cavity is generated.

[0041] Specifically, a function f(x, y, z) is defined, which takes a value of 255 at grid points inside the salt cavern cavity and a value of 0 at grid points outside the cavity. By converting this function into a three-dimensional digital image, pixels inside the salt cavern cavity are labeled with a value of 255, and pixels outside the cavity are labeled with a value of 0, thus constructing a first digital image model to characterize the cavity's morphology.

[0042] like Figure 4 As shown, the first digital image model is plotted at x=100, clearly demonstrating the three-dimensional morphology of the salt cavern cavity reconstructed from sonar point cloud data. The red area represents the internal cavity space of the salt cavern hydrogen storage reservoir, while the gray area represents the surrounding rock area. This modeling method based on actual detection data can accurately reflect the true geometric characteristics of underground salt caverns.

[0043] like Figure 5 As shown, the volume rendering of the first digital image model, using 3D visualization technology, fully presents the reconstructed spatial morphology of the salt cavern cavity. The image uses transparency to show the surrounding rock area outside the cavity, making the internal structure more clearly visible. This display method helps to intuitively understand the spatial configuration of the salt cavern hydrogen storage reservoir.

[0044] By employing the aforementioned technical solution, connecting detection points to form a cross-sectional profile and using interpolation techniques to complete the three-dimensional morphology, morphological distortion caused by sampling intervals can be effectively reduced, ensuring that the reconstructed cavity shape highly matches the actual detection data. The method of classifying grid points based on continuous three-dimensional boundaries enables the generated digital image model to accurately reflect the complex geometric features of the salt cavern cavity, providing a reliable geometric basis for subsequent rock layer attribute assignment and grid conversion, thereby improving the accuracy of the final numerical simulation results. This layer-by-layer construction and interpolation completion method ensures that the model retains the accuracy of the measured data while achieving the integrity of the three-dimensional morphology, laying a solid technical foundation for subsequent analysis work.

[0045] S102. The well logging data interpretation results of the salt cavern hydrogen storage reservoir are fused into the first digital image model to determine the second digital image model. The well logging data interpretation results are used to characterize the rock layer property distribution of the salt cavern hydrogen storage reservoir. The second digital image model characterizes the cavity morphology and rock layer property distribution of the salt cavern hydrogen storage reservoir. Different rock layer property distributions are distinguished by different pixel label values. In one embodiment, fusing the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model includes: The rock layer property distribution of the salt cavern hydrogen storage reservoir was determined based on the interpretation results of the well logging data. A rock strata data relationship table for the first digital image model is constructed based on the distribution of rock strata attributes, wherein different rock strata have different pixel label values. The rock strata data relationship table includes: the top and bottom depths and thicknesses of each rock stratum, the pixel layer index of each rock stratum in the z-direction, the pixel label values ​​of each rock stratum, and the physical properties of each rock stratum.

[0046] For example, the above steps integrate geological attribute information into a digital model that already possesses cavity morphology. The rock strata attribute distribution refers to the sequence of different rock strata types and their physical characteristic parameters in the vertical direction of the salt cavern hydrogen storage reservoir, obtained through well logging data interpretation. These parameters are important data reflecting the characteristics of underground rock strata, obtained through professional interpretation of well logging curves. The rock strata data relationship table is a structured data table that establishes a mapping relationship between geological information and model coordinates. It includes top and bottom depths (the vertical distances from the top and bottom of a specific rock stratum to the reference surface), thickness (the difference between the top and bottom depths of the rock stratum), z-direction pixel layer index (the range of pixel layer numbers corresponding to the rock stratum in the vertical direction of the digital image model), pixel label values ​​(specific identifier values ​​assigned to distinguish different rock strata), and physical properties including inherent parameters such as porosity and permeability of the rock strata. These parameters together constitute a complete rock strata characteristic description system.

[0047] This application achieves geological attribute fusion by integrating well logging interpretation data with digital image models. Specifically, the well logging data of the salt cavern hydrogen storage reservoir is first professionally interpreted. Through comprehensive analysis of well logging curves such as natural gamma, sonic transit time, and resistivity, different rock strata sequences within the depth range are identified. The top and bottom burial depths of each rock stratum are accurately determined, and its thickness is calculated. Simultaneously, the physical properties parameters corresponding to each rock stratum, such as rock type, porosity, and permeability, are recorded. Based on this, a rock stratum data relationship table is constructed. This table establishes a precise mapping relationship from actual geological depth to model pixel layers: the top and bottom depths of the rock strata obtained from the actual well logging interpretation are converted into the pixel layer index range in the z-direction of the digital image model. A unique pixel label value is assigned to each rock stratum for subsequent image recognition, and the corresponding rock stratum physical property parameters are associated with it. For example, when well logging interpretation reveals the presence of a pure salt rock layer at a specific depth, the top and bottom depths, thickness, and corresponding pixel layer index range of that layer are recorded in the relationship table. Specific pixel label values ​​are assigned, and the porosity and permeability parameters of the salt rock layer are associated with it. Similarly, for different lithological layers such as argillaceous salt rock layers and mudstone interlayers, corresponding data records are established, forming a complete depth-lithology-attribute correspondence table. This relationship table effectively constructs a bridge from the actual geological space to the digital model space, enabling the geological attributes at each depth location to accurately correspond to a specific pixel layer in the digital image model.

[0048] Specifically, the rock strata data relationship table is shown below: Table 1. Statistics on rock strata properties in salt cavern hydrogen storage sites

[0049] By employing the aforementioned technical solution and establishing a complete rock stratum data relationship table, a precise correspondence between actual geological information and the spatial location of the digital image model is achieved, providing mapping rules for subsequent attribute assignment. This structured data organization ensures that the attribute parameters of different rock strata are accurately transmitted to the corresponding areas of the model, reducing the risk of data misalignment caused by human intervention. The binding of rock stratum attributes to the model space provides a reliable geological parameter foundation for subsequent numerical simulations, enabling the generated second digital image model to not only retain cavity morphological features but also possess realistic geological attribute connotations, thus creating the necessary conditions for accurately simulating the actual working conditions of underground hydrogen storage reservoirs. This attribute fusion method based on a strict depth and pixel mapping relationship effectively maintains the authenticity and integrity of the geological profile, allowing the final model to simultaneously reflect the geometric and lithological distribution characteristics of the salt cavern hydrogen storage reservoir, providing a more reliable geological basis for subsequent numerical simulation analysis. Through this attribute fusion method, the engineering practical value of the model can be significantly improved, providing more accurate technical support for the stability analysis, sealing assessment, and operational optimization of salt cavern hydrogen storage reservoirs.

[0050] In one embodiment, fusing the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model includes: Iterate through every pixel in the first digital image model; When the pixel is an external point, the rock layer to which the pixel belongs is determined by referring to the rock layer data relationship table; The pixel label value of the pixel is changed to the pixel label value of the rock stratum to construct the second digital image model.

[0051] For example, the above steps involve reclassifying the attributes of each pixel in the first digital image model. Traversal refers to accessing pixels at every spatial location in the model, ensuring no spatial unit is missed; external points specifically refer to pixels in the first digital image model marked as the surrounding rock area outside the cavity, these points initially have a uniform identifier value; the corresponding rock stratum refers to determining the geological rock stratum type based on the pixel's spatial location information, particularly its depth coordinates, through matching with a rock stratum data relationship table; changing the pixel label value refers to replacing the original uniform identifier value representing the surrounding rock area with a unique identifier value specific to the rock stratum type, achieving the conversion from geometric attributes to geological attributes.

[0052] This application achieves geological attribute fusion through pixel-by-pixel attribute redistribution. In practice, each pixel in the first digital image model is scanned comprehensively according to a predetermined sequence. This scanning is performed layer by layer, row by row, and column by column to ensure accurate access to each three-dimensional spatial location. For each accessed pixel, its stored current attribute identifier is first read, and its classification status is determined based on this identifier: if the pixel belongs to the internal structure of the salt cavern cavity, its original label value remains unchanged, as these points have correctly represented the cavity space; if the pixel belongs to the external rock area of ​​the cavity, detailed attribute reclassification processing is required. The depth coordinates of the pixel in three-dimensional space are extracted and precisely matched with the top and bottom depth ranges of each rock layer recorded in the rock layer data relationship table. The actual geological rock layer type of the pixel is determined through the depth correspondence. After identifying the rock layer, the corresponding exclusive pixel label value is obtained by querying the rock layer data relationship table, and the original label value of the pixel is updated to the new rock layer identifier value. For example, when traversing to a pixel in the surrounding rock region at a certain depth, the system searches the rock strata data table based on its depth coordinates. If the depth corresponds to a argillaceous salt rock stratum, the pixel's label value is changed from the initial surrounding rock identifier to the specific label value corresponding to the argillaceous salt rock stratum. Similarly, when a pixel's depth coordinates are encountered within the range of pure salt rock strata, its label value is updated to the identifier value corresponding to the pure salt rock strata. This process is performed on all pixels in the model marked as surrounding rock regions until each surrounding rock pixel is assigned a corresponding rock strata attribute identifier, ultimately forming a complete geological attribute distribution model.

[0053] like Figure 6 As shown, this is a schematic cross-sectional view of the second digital image model at x=100. While preserving the cavity morphology, the model uses color cloud maps to demonstrate the distribution of different rock strata properties. The red areas still represent the salt cavern cavity space, while the colored areas correspond to the distribution of rock strata with different physical properties, reflecting the precise correspondence between geological properties and spatial location.

[0054] like Figure 7 As shown, this is a volume rendering of the second digital image model. This image uses a 3D visualization method to simultaneously display the morphology of salt caverns and the distribution characteristics of rock strata properties. Different colors represent different rock strata types determined through well logging data interpretation. This comprehensive display provides a complete geological model foundation for subsequent numerical simulations.

[0055] By employing the aforementioned technical solutions, and through traversal and conditional judgment mechanisms, the accurate assignment of geological attributes at each spatial location can be ensured, reducing classification errors that may arise from human intervention. The depth-matching-based stratum identification method guarantees the correct correspondence between geological attributes and spatial locations, enabling the realistic reproduction of stratum distribution patterns in the digital model. This point-by-point assignment process maintains the continuity and integrity of the geological profile, avoiding unreasonable abrupt changes or discontinuities in attribute allocation. The resulting second digital image model not only retains the precise cavity geometry but also possesses realistic geological connotations, providing a comprehensive data foundation encompassing both geometric features and lithological attributes for subsequent numerical simulations, thus significantly enhancing the reference value of the simulation results. This refined attribute assignment process ensures that the model accurately reflects the spatial distribution characteristics of different strata under actual geological conditions, providing a reliable geological basis for the stability analysis and sealing assessment of salt cavern hydrogen storage facilities. This attribute fusion method significantly improves the model's engineering practical value, providing more accurate technical support for the safe operation and optimized design of salt cavern hydrogen storage facilities.

[0056] S103. Based on the equivalent conversion between image pixels and grid cells, the second digital image model is converted into a target computational grid model, wherein the grid cells of the target computational grid model are classified according to the pixel label values ​​in the second digital image model and stored in different component sets respectively.

[0057] In one embodiment, the conversion of the second digital image model into a target computational grid model based on the equivalent transformation between image pixels and grid cells includes: Each pixel in the second digital image model is converted into the finite element mesh element; In the case of converting each pixel in the second digital image model into the finite element mesh unit, the converted finite element mesh units are classified based on the pixel label value and stored in different component sets to form the target computational mesh model.

[0058] For example, the above steps represent a complete conversion and classification process for transforming the second digital image model into a target computational grid model. The equivalent conversion between image pixels and grid cells refers to a technique that directly maps each image pixel to a finite element grid cell based on the correspondence of discretized spatial representations, maintaining the consistency of spatial geometry. Pixel label values ​​are identifiers in the second digital image model used to distinguish the distribution of different rock strata properties and cavity regions; these label values ​​are assigned based on the interpretation results of well logging data during the initial modeling process. Classification refers to the process of categorizing grid cells according to their attribute characteristics, managing grid cells with the same attributes by grouping them. Component sets refer to containers that store grouped grid cells with the same attributes; each component set corresponds to a specific rock strata type or cavity region.

[0059] This application achieves automatic generation of computational grid models through pixel-to-mesh conversion and attribute classification mechanisms. During the point-by-point conversion from image pixels to finite element mesh units, the spatial coordinates and corresponding pixel label values ​​of each pixel in the second digital image model are read. Each cubic pixel is directly converted into a hexahedral finite element mesh unit, ensuring accurate geometric correspondence. After conversion, the pixel label values ​​inherited by each newly generated mesh unit are read. These label values ​​are assigned based on the interpretation results of well logging data during the initial modeling process and accurately reflect the geological strata type or cavity region attribute corresponding to each mesh unit. According to preset classification rules, mesh units with the same pixel label values ​​are automatically categorized into the corresponding component sets. For example, all mesh units with label values ​​corresponding to salt cavern cavities are stored in the component set representing cavities; mesh units with label values ​​corresponding to pure salt rock layers are categorized into the pure salt rock layer component set; mesh units with label values ​​corresponding to argillaceous salt rock layers are categorized into the argillaceous salt rock layer component set; and mesh units with label values ​​corresponding to mudstone interlayers are assigned to the mudstone component set. This classification process is carried out in real time while the mesh is being generated. Each newly transformed mesh cell is automatically assigned to the corresponding component set according to its label value. This process continues until all mesh cells have completed attribute classification and storage, ultimately forming a computational mesh model with clear material groupings.

[0060] like Figure 8 As shown in the figure, the target computational grid model is a cross-sectional view at x=100. This model is generated through the equivalent conversion of image pixels to grid cells, with different colors representing automatically classified sets of grid cell components. Each grid cell inherits the rock layer attribute information of the corresponding pixel, achieving accurate conversion from digital image to computational grid.

[0061] like Figure 9As shown, this diagram presents the overall target computational grid model, fully showcasing the final generated grid. Different colored areas represent salt cavern cavities and various rock strata assemblies, respectively. This automatically classified grid model can be directly used for numerical simulation analysis, providing a reliable computational foundation for the engineering evaluation of salt cavern hydrogen storage facilities.

[0062] By employing the aforementioned technical solutions and a synchronized conversion and classification mechanism, the attribute information of grid cells can be fully preserved and managed, reducing the risk of information loss during data processing. The automatic classification method based on pixel label values ​​reduces the complexity and error potential of manual grouping, improving model preparation efficiency and processing consistency. This attribute-preserving conversion process allows the generated computational grid model to directly possess material grouping information, facilitating material parameter assignment in subsequent numerical simulations and saving model preprocessing time. Independent storage management of each component facilitates the application of corresponding boundary conditions and load settings for different geological units, enhancing the flexibility of numerical analysis. The entire conversion and classification process maintains a high degree of consistency between geometric morphology and geological properties, ensuring that the computational model accurately reflects actual geological conditions and providing a reliable model foundation for accurate numerical analysis of salt cavern hydrogen storage reservoirs. This automated processing significantly improves modeling efficiency and provides high-quality technical support for engineering applications.

[0063] In one embodiment, the classification includes the salt cavern cavity and rock stratum type of the salt cavern hydrogen storage reservoir.

[0064] For example, the above classification involves the criteria for dividing different geological units in the salt cavern hydrogen storage reservoir model. The salt cavern cavity refers to the internal space of the hydrogen storage reservoir obtained through sonar detection, identified by a specific pixel label value in the digital image model; the stratum type refers to the different lithological layers surrounding the salt cavern determined through well logging data interpretation, including different types such as pure salt rock layers, argillaceous salt rock layers, and mudstone interlayers, each with its own unique pixel label value in the model.

[0065] This application achieves precise classification of different geological units in a salt cavern hydrogen storage model using pixel label values. Specifically, the grid units are automatically identified and classified based on the pixel label value carried by each pixel in the second digital image model. For grid units whose label values ​​correspond to salt cavern cavities, they are all assigned to the component set representing the cavity space. For grid units whose label values ​​correspond to different rock strata types, they are classified according to a preset mapping relationship; for example, grid units whose label values ​​correspond to pure salt rock strata are assigned to the pure salt rock strata component set, and grid units whose label values ​​correspond to argillaceous salt rock strata are assigned to the argillaceous salt rock strata component set. This classification method ensures that each geological unit has an independent component set for storage and management, while maintaining the spatial distribution pattern of the rock strata.

[0066] By employing the aforementioned technical solutions and an automatic classification method based on pixel tag values, it is possible to ensure clear differentiation and systematic management of different geological units. This classification mechanism facilitates the assignment of material parameters in subsequent numerical simulations, allowing each rock type and cavity region to independently set its corresponding physical parameters. Independent storage and management of component sets facilitates the application of specific boundary conditions and load settings for different geological units during subsequent analysis, enhancing the flexibility and accuracy of numerical simulations. Clear classification results contribute to improving the model's interpretability and engineering applicability, providing reliable technical support for the stability analysis and operational optimization of salt cavern hydrogen storage facilities.

[0067] In summary, the automatic modeling technology for salt cavern hydrogen storage provided in this application integrates sonar detection point cloud data and well logging data interpretation results to achieve an integrated process from reconstructing the actual cavity morphology to automatically generating a computational grid model. First, based on the original point cloud data obtained from sonar detection in cylindrical coordinates, the dimensions of the cavity in the x, y, and z directions are determined after coordinate transformation to Cartesian coordinates, and a uniform grid lattice with dimensions larger than the cavity is predefined. Next, all wall points are connected on each depth plane to form a closed polygon to represent the contour shape. Linear interpolation technology is used to complete the polygons between adjacent depths to determine the three-dimensional morphology of the cavity. Using this morphology as a boundary, the grid points are divided into internal and external points. Internal points represent the salt cavern cavity, and external points represent the surrounding rock area. A first digital image model is constructed by assigning different pixel label values. Subsequently, based on the interpretation results of well logging data, the distribution of rock strata properties was determined, and a rock strata data relationship table was constructed, containing the top and bottom depths, thicknesses, pixel layer indices, pixel label values, and physical properties of each rock stratum. Each pixel in the first digital image model was traversed, and after determining the rock stratum to which it belonged by referring to the relationship table, the pixel label value was changed. For example, when the depth of a pixel corresponds to a pure salt rock stratum, its label value was changed to a unique identifier specific to that rock stratum, thereby generating a second digital image model that simultaneously represents the cavity morphology and the distribution of rock strata properties. Finally, through the equivalent conversion between image pixels and mesh units, each pixel was directly converted into a finite element mesh unit, and the mesh units were automatically classified into different component sets based on the pixel label values, such as salt cavern cavity sets, pure salt rock stratum sets, or argillaceous salt rock stratum sets, forming the target computational mesh model. By employing the above technical solutions, the shape deviation caused by geometric simplification is reduced by directly reconstructing the real cavity morphology through sonar point clouds. The fusion of well logging data enables the model to have the actual rock layer property distribution, improving the realism of geological conditions. The automated pixel-to-mesh conversion and classification mechanism reduces manual intervention and improves modeling efficiency and consistency. The final generated computational grid model directly contains material grouping information, providing an accurate basis for numerical simulation and helping to improve the reliability of salt cavern hydrogen storage potential assessment and stability analysis.

[0068] Furthermore, as a response to the above Figure 1In addition to the implementation of the method shown, this embodiment of the invention also provides an automatic modeling device for salt cavern hydrogen storage reservoirs, used for the above-mentioned... Figure 1 The method shown is implemented accordingly. 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. Figure 10 As shown, the device includes: a construction unit 21, a determination unit 22, and a conversion unit 23, wherein... Construction unit 21 is used to construct a first digital image model based on point cloud data of the salt cavern hydrogen storage reservoir, wherein the point cloud data is acquired based on sonar detection, and the first digital image model is used to characterize the cavity morphology of the salt cavern hydrogen storage reservoir. The determining unit 22 is used to integrate the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model. The well logging data interpretation results are used to characterize the rock layer property distribution of the salt cavern hydrogen storage reservoir, and the second digital image model characterizes the cavity morphology and rock layer property distribution of the salt cavern hydrogen storage reservoir. Different rock layer property distributions are distinguished by different pixel label values. The conversion unit 23 is used to convert the second digital image model into a target computational grid model based on the equivalent conversion between image pixels and grid cells, wherein the grid cells of the target computational grid model are classified according to the pixel label values ​​in the second digital image model and stored in different component sets respectively.

[0069] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, an automatic modeling method for salt cavern hydrogen storage reservoirs can be implemented, addressing the current lack of a better modeling method for salt cavern hydrogen storage reservoirs.

[0070] This invention provides a computer-readable storage medium including a stored program that, when executed by a processor, implements an automatic modeling method for a salt cavern hydrogen storage reservoir.

[0071] This invention provides a processor for running a program, wherein the program executes an automatic modeling method for a salt cavern hydrogen storage reservoir.

[0072] This invention provides an electronic device, which includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the automatic modeling method for salt cavern hydrogen storage tanks as described above. This invention provides an electronic device 30, such as... Figure 11 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and bus 303 connected to the processor; wherein, the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call program instructions in the memory to execute the above-mentioned automatic modeling method for salt cavern hydrogen storage.

[0073] The smart electronic devices mentioned in this article can be PCs, tablets, mobile phones, etc.

[0074] This application also provides a computer program product that, when executed on a process management electronic device, is suitable for executing a program that initializes the above-described automatic modeling method steps for a salt cavern hydrogen storage reservoir.

[0075] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0076] 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.

[0077] 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 computer, 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, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] 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.

[0079] 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.

[0080] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The control flow of the memory in the corresponding embodiment.

[0081] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0082] Those skilled in the art will clearly 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.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, 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, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0084] 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.

[0085] Furthermore, 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0086] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it 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 all or part 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 of 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, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An automatic modeling method for salt cavern hydrogen storage reservoirs, characterized in that, include: A first digital image model is constructed based on point cloud data of a salt cavern hydrogen storage reservoir, wherein the point cloud data is acquired based on sonar detection, and the first digital image model is used to characterize the cavity morphology of the salt cavern hydrogen storage reservoir. The well logging data interpretation results of the salt cavern hydrogen storage reservoir are fused into the first digital image model to determine the second digital image model. The well logging data interpretation results are used to characterize the rock layer property distribution of the salt cavern hydrogen storage reservoir. The second digital image model characterizes the cavity morphology and rock layer property distribution of the salt cavern hydrogen storage reservoir. Different rock layer property distributions are distinguished by different pixel label values. The second digital image model is converted into a target computational grid model based on the equivalent conversion between image pixels and grid cells. The grid cells of the target computational grid model are classified according to the pixel label values ​​in the second digital image model and stored in different component sets.

2. The method according to claim 1, characterized in that, The construction of the first digital image model based on point cloud data from the salt cavern hydrogen storage reservoir includes: The salt cavern hydrogen storage reservoir is subjected to sonar detection to obtain raw point cloud data represented in cylindrical coordinates, wherein the raw point cloud data includes distance, angle and depth; Convert the original point cloud data into a Cartesian coordinate system; Determine the maximum and minimum values ​​in the x, y, and z directions of the Cartesian coordinate system, respectively; The dimensions of the salt cavern hydrogen storage reservoir in the x, y, and z directions are determined based on the difference between the maximum and minimum values ​​in each direction, respectively. A grid array with predefined intervals and uniformly distributed grid points is used, wherein the size of the grid array is larger than the size of the salt cavern hydrogen storage tank.

3. The method according to claim 2, characterized in that, The construction of the first digital image model based on point cloud data from the salt cavern hydrogen storage reservoir includes: Connect all wall points measured on each depth horizontal plane of the salt cavern hydrogen storage reservoir to form a closed polygon, wherein the closed polygon is used to characterize the outline shape of the salt cavern hydrogen storage reservoir on the depth horizontal plane. Linear interpolation technology is used to complete the closed polygons between every two adjacent depth horizontal planes to determine the three-dimensional morphology of the salt cavern hydrogen storage reservoir. Using the three-dimensional shape of the cavity as the boundary, each grid point in the grid is divided into external and internal points of the salt cavern hydrogen storage reservoir to construct a first digital image model. The external points are the surrounding rock areas outside the cavity of the salt cavern hydrogen storage reservoir, and the internal points are the salt cavern cavity of the salt cavern hydrogen storage reservoir. The pixel label values ​​of the external and internal points of the first digital image model are different.

4. The method according to claim 1, characterized in that, The step of fusing the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model includes: The rock layer property distribution of the salt cavern hydrogen storage reservoir was determined based on the interpretation results of the well logging data. A rock strata data relationship table for the first digital image model is constructed based on the distribution of rock strata attributes, wherein different rock strata have different pixel label values. The rock strata data relationship table includes: the top and bottom depths and thicknesses of each rock stratum, the pixel layer index of each rock stratum in the z-direction, the pixel label values ​​of each rock stratum, and the physical properties of each rock stratum.

5. The method according to claim 4, characterized in that, The step of fusing the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model includes: Iterate through every pixel in the first digital image model; When the pixel is an external point, the rock layer to which the pixel belongs is determined by referring to the rock layer data relationship table; The pixel label value of the pixel is changed to the pixel label value of the rock stratum to construct the second digital image model.

6. The method according to claim 1, characterized in that, The conversion of the second digital image model into the target computational grid model based on the equivalent transformation of image pixels and grid cells includes: Each pixel in the second digital image model is converted into a finite element mesh element; In the case of converting each pixel in the second digital image model into the finite element mesh unit, the converted finite element mesh units are classified based on the pixel label value and stored in different component sets to form the target computational mesh model.

7. The method according to claim 6, characterized in that, The classification includes the salt cavern cavity and rock stratum type of the salt cavern hydrogen storage reservoir.

8. An automatic modeling device for a salt cavern hydrogen storage reservoir, characterized in that, Also includes: A construction unit is used to construct a first digital image model based on point cloud data of a salt cavern hydrogen storage reservoir, wherein the point cloud data is acquired based on sonar detection, and the first digital image model is used to characterize the cavity morphology of the salt cavern hydrogen storage reservoir. The determining unit is used to integrate the well logging data interpretation results of the salt cavern hydrogen storage reservoir into the first digital image model to determine the second digital image model. The well logging data interpretation results are used to characterize the rock layer property distribution of the salt cavern hydrogen storage reservoir, and the second digital image model characterizes the cavity morphology and rock layer property distribution of the salt cavern hydrogen storage reservoir. Different rock layer property distributions are distinguished by different pixel label values. A conversion unit is used to convert the second digital image model into a target computational grid model based on the equivalent conversion between image pixels and grid cells, wherein the grid cells of the target computational grid model are classified according to the pixel label values ​​in the second digital image model and stored in different component sets respectively.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed by a processor, it implements the steps of the automatic modeling method for a salt cavern hydrogen storage reservoir as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is configured to call program instructions in the memory to execute the steps of the automatic modeling method for a salt cavern hydrogen storage reservoir as described in any one of claims 1 to 7.

Images