Multi-source data fusion shallow geothermal energy development modeling method, system and equipment

Abstract

The invention relates to the technical field of multi-source data fusion and grid modeling, and discloses a shallow geothermal energy development modeling method, system and equipment based on multi-source data fusion. The method comprises the following steps: performing joint interpolation on shallow geothermal data, engineering geological data and hydrogeological data to generate a virtual drill hole; constructing a ground temperature grid model according to shallow geothermal data, virtual drilling and anisotropic interpolation constraints; performing step-by-step color setting and rendering on the shallow geothermal attribute grade of the geothermal grid model to obtain a visual geothermal grid model; and generating a stepped temperature distribution profile map and a temperature contour line according to the ground temperature grid model so as to display stepped features of temperature distribution. According to the method, the high-precision and high-reliability ground temperature grid model is constructed through multi-source data fusion, virtual drilling generation and anisotropic modeling technologies, and the model not only can generate a stepped temperature distribution profile map and a temperature contour line, but also can improve the geological parameter inversion precision and the thermal reservoir prediction reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source data fusion and grid modeling, and in particular to a multi-source data fusion shallow geothermal energy development modeling method, system and equipment. Background Art

[0002] In shallow geothermal energy development, the acquisition of ground temperature data relies on borehole measurements, but traditional methods have the following problems:

[0003] Data sparsity: The limited number of geothermal boreholes results in low model resolution and large prediction errors.

[0004] Insufficient model reliability: Traditional interpolation methods (such as ordinary kriging) cannot reflect the distribution of geothermal fields under complex geological conditions.

[0005] Existing technologies mostly rely on single data source modeling and lack the ability to dynamically fusion data and generate virtual boreholes, which limits the accuracy and efficiency of geothermal energy resource evaluation. Summary of the Invention

[0006] The main purpose of the present invention is to provide a shallow geothermal energy development modeling method, system and equipment with multi-source data fusion, aiming to solve at least one of the above-mentioned technical problems.

[0007] To achieve the above objectives, the present invention provides a shallow geothermal energy development modeling method based on multi-source data fusion, comprising:

[0008] Jointly interpolate shallow geothermal data with engineering geological data and hydrogeological data to generate virtual boreholes;

[0009] Constructing a geothermal grid model based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints;

[0010] Performing graded coloring and rendering on the shallow geothermal attribute levels of the geothermal grid model to obtain a visual geothermal grid model;

[0011] A stepped temperature distribution profile and temperature contour lines are generated according to the geothermal grid model to demonstrate the stepped characteristics of the temperature distribution.

[0012] In some embodiments, the interpolating shallow geothermal data with engineering geological data and hydrogeological data to generate a virtual borehole includes:

[0013] Obtain initial engineering geological data, initial hydrogeological data, and initial shallow geothermal data;

[0014] Performing coordinate system alignment and outlier removal on the initial engineering geological data, initial hydrogeological data, and initial shallow geothermal data to obtain engineering geological data, hydrogeological data, and shallow geothermal data;

[0015] A virtual borehole is constructed based on the joint interpolation of the engineering geological data, hydrogeological data and shallow geothermal data.

[0016] In some embodiments, the engineering geological data includes borehole number, borehole type, longitude, latitude and lithology name; the hydrogeological data includes borehole number, borehole type, longitude, latitude, permeability coefficient and aquifer category; the shallow geothermal data includes borehole number, longitude, latitude, formation depth and thermal response test data.

[0017] In some embodiments, the constructing a virtual borehole by joint interpolation based on the engineering geological data, hydrogeological data, and shallow geothermal data includes:

[0018] Determine the division standard of the bottom plate depth of each layer according to the shallow geothermal data to obtain stratification information;

[0019] Extracting shallow geothermal data of the same depth layer of each well based on the layered information to obtain an original shallow geothermal data set of each layer;

[0020] Interpolation is performed based on the original shallow geothermal data set, the engineering geological data, and the hydrogeological data based on a preset interpolation method to obtain a virtual borehole.

[0021] In some embodiments, interpolating the original shallow geothermal data set, the engineering geological data, and the hydrogeological data based on a preset interpolation method to obtain a virtual borehole includes:

[0022] interpolating the original shallow geothermal data set of each layer with the engineering geological data and the hydrogeological data based on a preset interpolation method to obtain an interpolation result;

[0023] Extracting the interpolated temperature of the target location according to the interpolation result to obtain temperature data of the engineering hole and the hydrological hole at the same depth;

[0024] According to the temperature data and the engineering geological information and hydrogeological information of the target location, a virtual borehole based on the engineering hole and the hydrogeological hole of the shallow geothermal is obtained.

[0025] In some embodiments, the preset interpolation method includes an inverse distance weighted method, a Kriging interpolation method, and a radial basis function interpolation method.

[0026] In some embodiments, constructing a geothermal grid model based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints includes:

[0027] Determine target areas based on horizontal boundaries, elevation, and depth data;

[0028] Importing the data of the target area into Zgis software to obtain a spatial grid framework;

[0029] Using an octree method to partition the spatial grid framework to obtain a partitioned spatial grid;

[0030] The shallow geothermal properties of the virtual borehole and the shallow geothermal data are superimposed and constrained into the divided spatial grid by using anisotropic interpolation combined with Kriging interpolation to obtain a geothermal grid model.

[0031] In some embodiments, the method of combining anisotropic interpolation with Kriging interpolation to constrain the shallow geothermal attributes of the virtual borehole and the shallow geothermal data to the subdivided spatial grid to obtain a geothermal grid model includes:

[0032] According to the variation characteristics of shallow geothermal properties in the horizontal and vertical directions, the main axis of variation is determined to be the vertical direction;

[0033] Set the range parameters in the horizontal and vertical directions;

[0034] Performing coordinate transformation based on the variation principal axis and the variation range parameter to transform the anisotropic space into an isotropic space;

[0035] In the isotropic space, the shallow geothermal attributes of the virtual borehole and the shallow geothermal data are superimposed and constrained to the divided spatial grid using a Kriging interpolation method to obtain a geothermal grid model.

[0036] In addition, to achieve the above objectives, the present invention also proposes a shallow geothermal energy development modeling system based on multi-source data fusion, comprising:

[0037] Data integration module, used to jointly interpolate shallow geothermal data with engineering geological data and hydrogeological data to generate virtual boreholes;

[0038] A grid model construction module, configured to construct a geothermal grid model based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints;

[0039] A visualization module is used to color and render the shallow geothermal attribute levels of the geothermal grid model in different levels to obtain a visualized geothermal grid model;

[0040] The analysis module is used to generate a stepped temperature distribution profile and temperature contour lines according to the geothermal grid model to show the stepped characteristics of the temperature distribution.

[0041] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, which includes: a memory, a processor, and a multi-source data fusion shallow geothermal energy development modeling program stored on the memory and runnable on the processor, wherein the multi-source data fusion shallow geothermal energy development modeling program is configured to implement the multi-source data fusion shallow geothermal energy development modeling method as described above.

[0042] The present invention provides a shallow geothermal energy development modeling method based on multi-source data fusion, comprising: jointly interpolating shallow geothermal data with engineering geological data and hydrogeological data to generate a virtual borehole; constructing a geothermal grid model based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints; performing graded coloring and rendering on the shallow geothermal attribute levels of the geothermal grid model to obtain a visual geothermal grid model; and generating a stepped temperature distribution profile and temperature contour lines based on the geothermal grid model to demonstrate the stepped characteristics of the temperature distribution. In the present invention, based on the collection of multi-source geological attribute data such as engineering geology, hydrogeology, and shallow geothermal energy, sparse geothermal well data are jointly interpolated with engineering and hydrological borehole data to generate a virtual borehole. Combined with parameters such as stratum lithology, a high-precision and high-reliability geothermal grid model is constructed using anisotropic interpolation. This model can not only generate stepped temperature distribution profiles and temperature contour lines, but also improve the accuracy of geological parameter inversion and the reliability of thermal reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the structure of an electronic device in the hardware operating environment involved in an embodiment of the present invention;

[0044] Figure 2 This is a flow chart of an embodiment of a shallow geothermal energy development modeling method based on multi-source data fusion according to the present invention;

[0045] Figure 3 This is an example diagram of the steps of a grid modeling tool based on multi-source data to assist shallow geothermal energy development involved in an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a grid model construction method according to an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of interpolation parameter settings involved in an embodiment of the present invention;

[0048] Figure 6 A schematic diagram showing the rendering of a geothermal grid model according to an embodiment of the present invention;

[0049] Figure 7 This is a schematic diagram of the visualization results of the temperature distribution involved in the embodiment of the present invention;

[0050] Figure 8 This is a structural block diagram of an embodiment of a shallow geothermal energy development modeling system based on multi-source data fusion according to the present invention.

[0051] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0054] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0055] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiment of the present invention.

[0056] like Figure 1As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also be a storage device independent of the processor 1001.

[0057] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0058] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a shallow geothermal energy development modeling program for multi-source data fusion.

[0059] exist Figure 1 In the electronic device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls the multi-source data fusion shallow geothermal energy development modeling program stored in the memory 1005 through the processor 1001, and executes the multi-source data fusion shallow geothermal energy development modeling method provided by the embodiment of the present invention.

[0060] The present invention proposes a shallow geothermal energy development modeling method, system and equipment based on multi-source data fusion.

[0061] The embodiment of the present invention provides a shallow geothermal energy development modeling method based on multi-source data fusion, referring to Figure 2 , Figure 2 This is a flow chart of an embodiment of a shallow geothermal energy development modeling method based on multi-source data fusion according to the present invention.

[0062] like Figure 2 As shown, the shallow geothermal energy development modeling method based on multi-source data fusion includes:

[0063] Step S100: Jointly interpolating shallow geothermal data with engineering geological data and hydrogeological data to generate a virtual borehole;

[0064] Step S200: constructing a geothermal grid model according to the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints;

[0065] Step S300: performing graded coloring and rendering on the shallow geothermal attribute levels of the geothermal grid model to obtain a visual geothermal grid model;

[0066] Step S400: generating a stepped temperature distribution profile and temperature contour lines according to the geothermal grid model to display the stepped characteristics of the temperature distribution.

[0067] It should be noted that the execution subject in this embodiment may be an electronic device, which may be a computer device with data processing capabilities, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment, a computer device is used as an example for illustration.

[0068] It is understandable that this embodiment uses the grid modeling tool usage process based on multi-source data fusion to assist shallow geothermal energy development as an example to illustrate, including steps such as multi-source data standardization and integration, grid model construction, and real-time analysis. By integrating engineering geology, hydrogeology, and geothermal data, a high-precision three-dimensional geothermal field grid model is constructed, thereby solving the problem of geothermal data sparsity. Specifically, this embodiment, under the premise of collecting thematic geological attribute parameters such as engineering geology, hydrogeology, and shallow geothermal energy, jointly interpolates sparse geothermal well data with engineering and hydrological borehole data to generate virtual geothermal points. Combined with parameters such as stratum lithology, aquifers, and thermal conductivity, the model is constrained by anisotropic interpolation according to different variogram parameters in the vertical and horizontal directions, and a high-precision, high-reliability geothermal grid model is constructed. It can generate real-time analysis methods such as stepped temperature distribution profiles and temperature contour lines, thereby improving the accuracy of geological parameter inversion and the reliability of thermal reservoir prediction. The following is an explanation in conjunction with specific steps.

[0069] In one embodiment, shallow geothermal data is jointly interpolated with engineering geological data and hydrogeological data to generate a virtual borehole, including: obtaining initial engineering geological data, initial hydrogeological data and initial shallow geothermal data; performing coordinate system alignment and outlier removal on the initial engineering geological data, initial hydrogeological data and initial shallow geothermal data to obtain engineering geological data, hydrogeological data and shallow geothermal data; and constructing a virtual borehole based on the joint interpolation of the engineering geological data, hydrogeological data and shallow geothermal data.

[0070] In one embodiment, the engineering geological data includes borehole number, borehole type, longitude, latitude and lithology name; the hydrogeological data includes borehole number, borehole type, longitude, latitude, permeability coefficient and aquifer category; the shallow geothermal data includes borehole number, longitude, latitude, formation depth and thermal response test data.

[0071] Specifically, if Figure 3 As shown in the figure, multi-source data standardization and integration: collect thematic drilling data such as engineering geology, hydrogeology and shallow geothermal, first unify the coordinate system of the data and remove outliers of the values ​​to ensure data quality and participate in subsequent interpolation, then obtain the temperature data of each layer according to the layered information of the shallow geothermal data, interpolate the points of the engineering holes and hydrological holes, obtain the temperature values ​​of the engineering holes and hydrological holes at the same depth, generate the corresponding shallow geothermal virtual boreholes, organize and merge the data into the shallow geothermal drilling data, and realize the expansion of shallow geothermal drilling data.

[0072] For example, multi-source data collection includes: Engineering geological data: borehole number, borehole type, longitude, latitude, lithology name, etc. Hydrogeological data: borehole number, borehole type, longitude, latitude, permeability coefficient, aquifer type, etc. Shallow geothermal data: borehole number, longitude, latitude, formation depth, and thermal response test data.

[0073] For example, in engineering geological data, the borehole number is used to uniquely identify each borehole, facilitating data management and query. For example, in a large geological exploration project, there are hundreds of boreholes, and the numbers can be used to quickly locate and distinguish different boreholes. Borehole type: Different borehole types (such as exploration holes, test holes, etc.) reflect the use and purpose of the borehole, which helps to classify and analyze the data. Longitude and latitude: Determine the geographical location of the borehole and are used to locate and display the borehole in geographic space. Lithology name: Records the rock type of the stratum through which the borehole passes, such as sandstone, shale, etc., which is used to understand the underground geological structure and the basic conditions for engineering construction. In hydrogeological data, the borehole number, borehole type, longitude, latitude, etc. have similar functions to the corresponding fields in engineering geological data, and are used to identify the borehole and determine its location. Permeability coefficient: Reflects the ease with which groundwater flows in rock or soil. It is a very important parameter in hydrogeological research and is of great significance for groundwater resource evaluation and engineering waterproofing design. Aquifer type: For example, phreatic aquifers and confined aquifers. Different aquifer types have different hydrogeological characteristics, which have a significant impact on the development and management of water resources. In shallow geothermal data, borehole number, longitude, and latitude are also used to identify boreholes and determine locations. Formation depth: Recording the depth information of different formations helps to understand how underground temperature changes with depth. Thermal response test data: Data obtained through thermal response tests, such as thermal conductivity and thermal diffusivity, are used to evaluate the development potential and utilization efficiency of shallow geothermal energy.

[0074] Specifically, if Figure 3 Data preprocessing is shown in the figure. Coordinate System 1: Use a unified coordinate system, such as the CGCS2000 coordinate system. The 1985 National Elevation Standard can be used as the elevation datum. Stratigraphic Coding Standardization: Lithological strata are classified and coded according to the ISO 19136 standard (e.g., Q4 represents Quaternary alluvial deposits). Outlier Removal: Data outside the geologically reasonable range is removed. Outlier detection methods include statistical methods (such as the Z-score method and the interquartile range method). By setting a reasonable threshold, data exceeding the threshold is considered an outlier and removed.

[0075] In this embodiment, by unifying the coordinate system of all data, the spatial consistency of the data is ensured, which facilitates subsequent data processing and analysis. Outliers are detected and removed from the data to improve the accuracy of subsequent data analysis.

[0076] In one embodiment, a virtual borehole is constructed based on the joint interpolation of the engineering geological data, hydrogeological data, and shallow geothermal data, including: determining a division standard for the bottom plate depth of each layer based on the shallow geothermal data to obtain stratification information; extracting shallow geothermal data of the same depth layer of each hole based on the stratification information to obtain an original shallow geothermal data set for each layer; and interpolating the original shallow geothermal data set, the engineering geological data, and the hydrogeological data based on a preset interpolation method to obtain a virtual borehole.

[0077] In one embodiment, interpolating the original shallow geothermal dataset with the engineering geological data and hydrogeological data based on a preset interpolation method to obtain a virtual borehole includes: interpolating the original shallow geothermal dataset of each layer with the engineering geological data and hydrogeological data based on the preset interpolation method to obtain an interpolation result; extracting the interpolated temperature at the target location based on the interpolation result to obtain temperature data of the engineering borehole and the hydrogeological borehole at the same depth; and obtaining a shallow geothermal virtual borehole based on the engineering borehole and the hydrogeological borehole based on the temperature data and the engineering geological information and hydrogeological information at the target location. Preset interpolation methods include inverse distance weighting, kriging, and radial basis function interpolation.

[0078] Specifically, if Figure 3 As shown, a virtual borehole is generated. Geological layer data evaluation: Based on the original shallow geothermal data, the floor depth criteria for each layer are determined (e.g., two-meter increments per layer, Layer 1: 0m, Layer 2: 2m, Layer 3: 4m, etc.). Geological layer data integration: The original shallow geothermal data for each layer at the same depth in each well is extracted to generate the original shallow geothermal dataset for each layer (as shown in Table 1 below). Spatial interpolation: Each extracted shallow geothermal dataset is interpolated with the engineering geology data and hydrogeology data. The interpolation method is selected based on the actual situation. Examples include inverse distance weighting (IDW), which assumes that neighboring points have a greater influence and is suitable for uniform data distribution; Kriging, which considers spatial autocorrelation and generates error estimates and is suitable for geological data; and radial basis function (RBF), which provides smooth interpolation and is suitable for complex variations. Virtual borehole generation: The interpolated temperature is extracted at the target location (e.g., a specific coordinate point) to obtain temperature data for the engineering borehole and hydrogeology borehole at the same depth, thus generating a virtual borehole for the shallow geothermal system based on the engineering borehole and hydrogeology borehole.

[0079] Table 1 Original shallow geothermal data set

[0080]

[0081] It's important to note that the interpolation method described above can be used to obtain engineering geology, hydrogeology, and shallow geothermal properties at the same depth at any location within the study area (target location). For example, by extracting the interpolated temperature value at a specific coordinate point (target location), we can obtain the temperature data for the engineering and hydrological wells at that depth, and then generate virtual boreholes for shallow geothermal energy based on these wells. These virtual boreholes are used to supplement actual borehole data, providing more comprehensive and detailed information for shallow geothermal energy assessment, geological analysis of engineering construction, and hydrogeological research.

[0082] In one example, the information of a virtual borehole may include basic information and layered information, wherein the basic information may include the borehole number, borehole type (such as a virtual shallow geothermal borehole), longitude and latitude, etc., and the layered information may include, for each stratum depth layer, recording the temperature, lithology name, permeability coefficient, aquifer category and other information of the layer.

[0083] Understandably, in actual geological structures, temperatures at different depths can vary. Therefore, temperature data is organized based on geological stratification. For example, the underground can be divided into different strata, with each stratum assigned a specific temperature value. This provides a more accurate representation of the underground temperature distribution. Since the distribution of engineering boreholes and hydrological boreholes can be discrete, interpolation methods are required to obtain temperature values ​​at more locations at the same depth. This interpolation allows temperature estimates to be made for the areas between the engineering and hydrological boreholes. Based on the interpolated temperature values ​​of the engineering and hydrological boreholes at the same depth, corresponding shallow geothermal virtual boreholes are generated. These virtual boreholes represent shallow geothermal information at locations between the actual boreholes, effectively creating "virtual" borehole data in locations where no actual measurements were taken. By organizing and merging the generated virtual borehole data into the shallow geothermal borehole data, the shallow geothermal borehole data is expanded. This increases data density and coverage, providing richer and more accurate data support for subsequent shallow geothermal energy assessment, development, and utilization.

[0084] In one embodiment, a geothermal grid model is constructed based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints, including: determining a target area based on horizontal boundaries, elevation, and depth data; importing the data of the target area into Zgis software to obtain a spatial grid framework; segmenting the spatial grid framework using an octree method to obtain a segmented spatial grid; and superimposing and constraining the shallow geothermal properties of the virtual borehole and the shallow geothermal data onto the segmented spatial grid using anisotropic interpolation combined with Kriging interpolation to obtain a geothermal grid model.

[0085] In one embodiment, anisotropic interpolation is combined with Kriging interpolation to superimpose and constrain the shallow geothermal properties of the virtual borehole and the shallow geothermal data into the segmented spatial grid to obtain a geothermal grid model, including: determining the principal axis of variation as the vertical direction based on the change characteristics of the shallow geothermal properties in the horizontal and vertical directions; setting the range parameters in the horizontal and vertical directions; performing coordinate transformation based on the principal axis of variation and the range parameters to convert the anisotropic space into an isotropic space; and in the isotropic space, using Kriging interpolation to superimpose and constrain the shallow geothermal properties of the virtual borehole and the shallow geothermal data into the segmented spatial grid to obtain a geothermal grid model.

[0086] Specifically, if Figure 3 As shown in the figure, the grid model is constructed: first, the spatial scope of the target area is determined by the horizontal boundary, elevation, and depth, and then it is imported into the Zgis software to construct a spatial grid framework. The appropriate segmentation method is selected to segment the spatial grid framework. After the segmentation is completed, the model is constrained by anisotropic interpolation using Zgis. According to the different variogram parameters in the vertical and horizontal directions, the shallow geothermal properties of the borehole are superimposed and constrained to the spatial grid. Then, the grid shallow geothermal properties are graded and colored according to the level of the grid, and the evaluation grid model is rendered and displayed.

[0087] For example, Figure 4 As shown, grid framework construction: Based on horizontal boundary, elevation, depth and other data, the target area is delineated, and the grid resolution (for example, X: 10m; Y: 10m; Z: 1m) is selected as a unit (grid). The data is then imported into ZGIS software. Grid subdivision: Based on the constructed grid framework, ZGIS software can be used to subdivide the grid framework using an octree method.

[0088] For example, anisotropic interpolation: Considering that shallow geothermal properties may have different ranges and variogram parameters in the horizontal and vertical directions, this example uses anisotropic interpolation combined with kriging interpolation to assign shallow geothermal properties using ZGIS software. The shallow geothermal properties of the boreholes are overlaid and constrained onto a spatial grid.

[0089] Specifically, refer to Figure 5 The interpolation parameter settings shown in the figure are as follows: first, the principal axis of variation is determined to be in the vertical direction; then, the range parameters in the horizontal and vertical directions are set according to the actual situation; then, a coordinate transformation is performed to convert the anisotropic space into an isotropic one; and then, the ordinary Kriging method is applied.

[0090] For example, based on the horizontal and vertical variation characteristics of shallow geothermal properties, the main axis of variation is determined to be the vertical direction, and the vertical variation will be given priority consideration in the subsequent interpolation process. Based on the actual geological conditions, existing research results, and preliminary analysis of the data, the horizontal range parameters and vertical range parameters are set. In order to convert the anisotropic space into an isotropic space, a coordinate transformation is required. Under the new coordinate system, the spatial correlation has the same range in all directions, that is, isotropy is achieved. In the transformed coordinate system, the experimental variation function is calculated. A suitable theoretical variation function model (such as a spherical model, an exponential model, a Gaussian model, etc.) is selected to fit the experimental variation function. For each grid node to be interpolated, a kriging equation system is constructed based on the surrounding known borehole data to solve the weight coefficient. The kriging equation system can be solved using linear algebra methods to obtain the weight coefficient and Lagrange multiplier. The shallow geothermal property estimates for the interpolated grid nodes are calculated based on the weight coefficients obtained. The interpolation results for each grid node are assigned to the corresponding spatial grid node, completing the shallow geothermal property interpolation process. This results in a continuous shallow geothermal property distribution across the entire spatial grid, which can be used for subsequent analysis and visualization.

[0091] In this embodiment, when constructing the geothermal grid model, considering that underground geological conditions often vary differently in the vertical and horizontal directions, anisotropy is introduced. Combined with parameters such as stratum lithology, aquifers, and thermal conductivity, different variogram parameters are determined for the vertical and horizontal directions. A variogram is a function that describes the spatial variability of a regionalized variable, reflecting the characteristics of the variable's variation at different distances and directions. For example, in the horizontal direction, the stratum lithology may be relatively uniform, and the ground temperature may vary relatively slowly; however, in the vertical direction, due to factors such as the layered structure of the stratum and the vertical flow of groundwater, the ground temperature may vary significantly. By setting the variogram parameters for the vertical and horizontal directions separately, the variation patterns of ground temperature in different directions can be more accurately reflected. These anisotropic variogram parameters are used to constrain the interpolation model, making the constructed geothermal grid model more consistent with actual geological conditions and improving the model's accuracy and reliability. A geothermal grid model is constructed based on the generated virtual geothermal points and the anisotropic interpolation constraints. The geothermal grid model divides the study area into a number of regular grid cells, each of which is assigned a geothermal value. These geothermal values ​​are calculated using an interpolation algorithm based on surrounding virtual geothermal points and known geothermal well data. In this way, discrete geothermal data can be converted into a continuous geothermal distribution model, providing a more intuitive display of the geothermal distribution across the entire region.

[0092] Specifically, model rendering display: Based on the interpolation results, attribute intervals are assigned corresponding colors for graded coloring, and the shallow geothermal grid model is loaded and displayed, such as Figure 6 shown.

[0093] In practical applications, after assigning grid attributes, the grid is visualized to more intuitively display and analyze the distribution of shallow geothermal properties. For example, the grid is colored in a hierarchical manner based on the shallow geothermal attribute level of the grid, with different attribute value ranges divided into several levels and each level assigned a specific color. For example, areas with higher ground temperatures can be represented by red, areas with lower ground temperatures by blue, and different temperature ranges in between by transitional colors of varying shades. This embodiment uses this hierarchical coloring method to intuitively display the attribute information of the grid in the form of color. The grid model is then processed using the software's rendering function to give it a three-dimensional and realistic feel, ultimately displaying a visual evaluation grid model. By observing this model, one can quickly understand the distribution characteristics and variation patterns of the shallow geothermal attributes in the target area, providing an intuitive basis for decision-making in areas such as the development and utilization of shallow geothermal energy and geological engineering.

[0094] In one embodiment, the shallow geothermal property levels of the geothermal grid model are colored and rendered in different levels to obtain a visual geothermal grid model; a stepped temperature distribution profile and temperature contour lines are generated based on the geothermal grid model to demonstrate the stepped characteristics of the temperature distribution.

[0095] Specifically, if Figure 3 As shown, real-time analysis: Based on the constructed geothermal grid model, ZGIS software can quickly generate step temperature distribution profiles and temperature contour lines in real time, and visualize the results to show the step characteristics of temperature distribution, such as Figure 7 shown.

[0096] It should be noted that after the geothermal grid model is constructed, real-time analysis can be performed and the results can be presented in a visual manner. The visualization methods include stepped temperature distribution profiles and temperature contour maps. The stepped temperature distribution profile shows the changes in geothermal temperature at different depths underground along a specific direction (such as the profile line). The profile can clearly show the trend of geothermal changes with depth, as well as the differences in geothermal temperature between different strata, which helps to analyze the impact of geological structure on temperature distribution. Points with equal geothermal values ​​are connected to form contour lines, and different geothermal intervals are represented by different colors or lines. The temperature contour map can intuitively display the geothermal distribution characteristics of the entire area, such as the distribution location and range of high-temperature and low-temperature zones, making it easy to quickly identify the spatial variation pattern of geothermal temperature.

[0097] The beneficial effects of the method described in this embodiment include: systematically solving the three core problems of data sparsity, model reliability and decision lag in shallow geothermal energy development through multi-source data fusion, virtual borehole generation and anisotropic modeling technology, and providing efficient and reliable technical support for resource exploration, urban planning and carbon neutrality goals. Regarding the problem of data sparsity: This embodiment expands the sparse geothermal wells (for example, 10) to 50-100 virtual points (virtual boreholes), and the data coverage rate is increased by 3-5 times, which solves the problem of model distortion caused by insufficient data in traditional methods. Regarding the problem of model reliability: This embodiment uses high-precision and multi-data source data to accurately predict the distribution of geothermal fields and optimize the design of heat exchange systems. Regarding the problem of decision lag: This embodiment supports real-time data updates and dynamic adjustment of multi-source weights, and is suitable for new area exploration and complex geological conditions.

[0098] It can be understood that this embodiment, through the comprehensive use of various geological data, innovatively interpolates sparse geothermal hole data with engineering and hydrological drilling data to generate virtual geothermal points, combines parameters such as stratum lithology, and uses anisotropic interpolation to construct a high-precision, high-reliability geothermal grid model. This model can not only generate stepped temperature distribution profiles and temperature contours, but also improve the accuracy of geological parameter inversion and the reliability of thermal reservoir prediction, providing an accurate basis for engineering design and geothermal energy development, and has important technical value. Specifically, in terms of improving the scientificity and accuracy of engineering design, optimizing the design of ground source heat pump systems: the geothermal grid model of this embodiment can accurately reflect the distribution characteristics of the underground temperature field, provide a scientific basis for the design parameters of the ground source heat pump system such as the buried pipe depth and spacing, and ensure the efficient operation of the system. Guiding the design of building thermal structures: During the building design stage, the geothermal grid model of this embodiment can help determine the reasonable size and material selection of thermal structures such as building foundations and walls, and improve the thermal insulation performance of the building. Assist in the design and construction of underground projects: For underground tunnels, pipelines and other projects, the geothermal grid model of this embodiment can predict changes in the thermal environment during the construction process, and provide data support for ventilation, cooling and other measures. In terms of improving the efficiency and sustainability of geothermal energy development, accurately locate the heat reservoir: Through the high-precision geothermal grid model, the location and range of the heat reservoir can be accurately identified, improving the efficiency of geothermal energy extraction. Optimize the layout of production and injection wells: The geothermal grid model of this embodiment can simulate the temperature and pressure response of the heat reservoir under different production and injection scenarios, help optimize the layout of production and injection wells, avoid thermal breakthroughs, and extend the life of the thermal field. Assess the potential of geothermal energy resources: Based on the precise description of the geothermal field by the geothermal grid model of this embodiment, the geothermal energy resource potential of a certain area can be more accurately assessed, providing a scientific basis for the development and utilization of geothermal energy.

[0099] This embodiment provides a shallow geothermal energy development modeling method based on multi-source data fusion, including: jointly interpolating shallow geothermal data with engineering geological data and hydrogeological data to generate a virtual borehole; constructing a geothermal grid model based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints; coloring and rendering the shallow geothermal attribute levels of the geothermal grid model in a graded manner to obtain a visual geothermal grid model; and generating a stepped temperature distribution profile and temperature contour lines based on the geothermal grid model to demonstrate the stepped characteristics of the temperature distribution. In this embodiment, based on the collection of multi-source geological attribute data such as engineering geology, hydrogeology, and shallow geothermal energy, sparse geothermal well data is jointly interpolated with engineering and hydrological borehole data to generate a virtual borehole. Combined with parameters such as stratum lithology, a high-precision and high-reliability geothermal grid model is constructed using anisotropic interpolation. This model not only generates stepped temperature distribution profiles and temperature contour lines, but also improves the accuracy of geological parameter inversion and the reliability of thermal reservoir prediction.

[0100] In addition, an embodiment of the present invention also proposes a storage medium, on which a shallow geothermal energy development modeling program with multi-source data fusion is stored. When the shallow geothermal energy development modeling program with multi-source data fusion is executed by a processor, the steps of the shallow geothermal energy development modeling method with multi-source data fusion as described above are implemented.

[0101] Reference Figure 8 , Figure 8 This is a structural block diagram of an embodiment of a shallow geothermal energy development modeling system based on multi-source data fusion according to the present invention.

[0102] like Figure 8 As shown, the multi-source data fusion shallow geothermal energy development modeling system includes:

[0103] The data integration module 10 is used to interpolate the shallow geothermal data with the engineering geological data and the hydrogeological data to generate a virtual borehole;

[0104] A grid model construction module 20 is used to construct a geothermal grid model based on the shallow geothermal data, the virtual borehole and anisotropic interpolation constraints;

[0105] A visualization module 30 is used to color and render the shallow geothermal attribute levels of the geothermal grid model in different levels to obtain a visualized geothermal grid model;

[0106] The analysis module 40 is used to generate a stepped temperature distribution profile and temperature contour lines according to the geothermal grid model to display the stepped characteristics of the temperature distribution.

[0107] This embodiment provides a shallow geothermal energy development modeling system that integrates multi-source data. Based on the collection of multi-source geological attribute data such as engineering geology, hydrogeology, and shallow geothermal energy, sparse geothermal well data is jointly interpolated with engineering and hydrological borehole data to generate virtual boreholes. Combined with parameters such as stratum lithology, a high-precision and high-reliability geothermal grid model is constructed using anisotropic interpolation. This model not only generates stepped temperature distribution profiles and temperature contour lines, but also improves the accuracy of geological parameter inversion and the reliability of thermal reservoir prediction.

[0108] It should be noted that the technical details that are not fully described in the embodiment of the shallow geothermal energy development modeling system for multi-source data fusion can be found in the shallow geothermal energy development modeling method for multi-source data fusion as described above provided in any embodiment of the present invention, and will not be repeated here.

[0109] It should be understood that the above is only an example and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any limitation on this.

[0110] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the embodiment scheme, and no limitation is made here.

[0111] In addition, it should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0112] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0113] Through the above description of the embodiments, those skilled in the art will clearly understand that the methods of the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is the more preferred implementation method. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (such as a mobile phone, computer, server, or network device) to execute the methods described in the various embodiments of the present invention.

[0114] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A shallow geothermal energy development modeling method based on multi-source data fusion, characterized in that: include: Jointly interpolate shallow geothermal data with engineering geological data and hydrogeological data to generate virtual boreholes; Constructing a geothermal grid model based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints; Performing graded coloring and rendering on the shallow geothermal attribute levels of the geothermal grid model to obtain a visual geothermal grid model; A stepped temperature distribution profile and temperature contour lines are generated according to the geothermal grid model to demonstrate the stepped characteristics of the temperature distribution.

2. The method according to claim 1, wherein The method of interpolating shallow geothermal data with engineering geological data and hydrogeological data to generate a virtual borehole includes: Obtain initial engineering geological data, initial hydrogeological data, and initial shallow geothermal data; Performing coordinate system alignment and outlier removal on the initial engineering geological data, initial hydrogeological data, and initial shallow geothermal data to obtain engineering geological data, hydrogeological data, and shallow geothermal data; A virtual borehole is constructed based on the joint interpolation of the engineering geological data, hydrogeological data and shallow geothermal data.

3. The method according to claim 2, wherein The engineering geological data includes borehole number, borehole type, longitude, latitude and lithology name; the hydrogeological data includes borehole number, borehole type, longitude, latitude, permeability coefficient and aquifer category; the shallow geothermal data includes borehole number, longitude, latitude, formation depth and thermal response test data.

4. The method according to claim 2, wherein The method of constructing a virtual borehole by joint interpolation based on the engineering geological data, hydrogeological data and shallow geothermal data includes: Determine the division standard of the bottom plate depth of each layer according to the shallow geothermal data to obtain stratification information; Extracting shallow geothermal data of the same depth layer of each well based on the layered information to obtain an original shallow geothermal data set of each layer; Interpolation is performed based on the original shallow geothermal data set, the engineering geological data, and the hydrogeological data based on a preset interpolation method to obtain a virtual borehole.

5. The method according to claim 4, wherein The interpolation is performed based on the original shallow geothermal data set, the engineering geological data, and the hydrogeological data in a preset interpolation manner to obtain a virtual borehole, including: interpolating the original shallow geothermal data set of each layer with the engineering geological data and the hydrogeological data based on a preset interpolation method to obtain an interpolation result; Extracting the interpolated temperature of the target location according to the interpolation result to obtain temperature data of the engineering hole and the hydrological hole at the same depth; According to the temperature data and the engineering geological information and hydrogeological information of the target location, a virtual borehole based on the engineering hole and the hydrogeological hole of the shallow geothermal is obtained.

6. The method according to claim 5, wherein The preset interpolation methods include inverse distance weighted method, Kriging interpolation method and radial basis function interpolation method.

7. The method according to any one of claims 1 to 6, characterized in that The step of constructing a geothermal grid model based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints includes: Determine target areas based on horizontal boundaries, elevation, and depth data; Importing the data of the target area into Zgis software to obtain a spatial grid framework; Using an octree method to partition the spatial grid framework to obtain a partitioned spatial grid; The shallow geothermal properties of the virtual borehole and the shallow geothermal data are superimposed and constrained into the divided spatial grid by using anisotropic interpolation combined with Kriging interpolation to obtain a geothermal grid model.

8. The method according to claim 7, wherein The method of combining anisotropic interpolation with Kriging interpolation is used to superimpose and constrain the shallow geothermal attributes of the virtual borehole and the shallow geothermal data onto the subdivided spatial grid to obtain a geothermal grid model, including: According to the variation characteristics of shallow geothermal properties in the horizontal and vertical directions, the main axis of variation is determined to be the vertical direction; Set the range parameters in the horizontal and vertical directions; Performing coordinate transformation based on the variation principal axis and the variation range parameter to transform the anisotropic space into an isotropic space; In the isotropic space, the shallow geothermal attributes of the virtual borehole and the shallow geothermal data are superimposed and constrained to the divided spatial grid using a Kriging interpolation method to obtain a geothermal grid model.

9. A shallow geothermal energy development modeling system based on multi-source data fusion, characterized by: include: Data integration module, used to jointly interpolate shallow geothermal data with engineering geological data and hydrogeological data to generate virtual boreholes; A grid model construction module, configured to construct a geothermal grid model based on the shallow geothermal data, the virtual borehole, and anisotropic interpolation constraints; A visualization module is used to color and render the shallow geothermal attribute levels of the geothermal grid model in different levels to obtain a visualized geothermal grid model; The analysis module is used to generate a stepped temperature distribution profile and temperature contour lines according to the geothermal grid model to show the stepped characteristics of the temperature distribution.

10. An electronic device, characterized in that: The electronic device includes: a memory, a processor, and a multi-source data fusion shallow geothermal energy development modeling program stored in the memory and runnable on the processor. The multi-source data fusion shallow geothermal energy development modeling program is configured to implement the multi-source data fusion shallow geothermal energy development modeling method according to any one of claims 1 to 8.

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