Shallow geothermal energy resource assessment method based on geographic information data

Abstract

The invention discloses a shallow geothermal energy resource assessment method based on geographic information data, and the method comprises the steps: collecting multi-source data, and carrying out the fusion of the collected multi-source data; arranging the fused data, and adjusting and controlling the arranged data; extracting data features according to the obtained arranged data set, constructing a prediction model based on the extracted data features, and dividing development areas according to the prediction model; and adjusting the divided development area, and outputting the adjusted development area. By dividing a development area into a plurality of sub-areas and setting dense sampling points and sampling thresholds in each sub-area, the accuracy and reliability of shallow geothermal energy assessment are improved, the risk of assessment deviation is reduced, and by setting the sampling thresholds, sampling can be ensured at a critical moment or under a critical condition, so that the accuracy and reliability of shallow geothermal energy assessment are improved. The pertinence and effectiveness of data are improved, and the dynamic change characteristics of geothermal energy resources can be captured more accurately.

Description

Technical Field

[0001] The invention relates to the technical field of dynamic assessment, and in particular to a shallow geothermal energy resource assessment method based on geographic information data. Background Art

[0002] Shallow geothermal energy, as a clean and renewable energy source, has received extensive attention and development in recent years. With the continuous growth of global energy demand and the enhancement of environmental protection awareness, shallow geothermal energy is increasingly used in heating, cooling and power generation. However, how to accurately and efficiently evaluate and develop shallow geothermal energy resources has always been a challenging topic.

[0003] For example, Chinese patent announcement number CN118261454B discloses a method for dynamic evaluation of deep geothermal energy, including the following steps: based on the temperature and pressure data of geothermal wells, calculating the temperature difference and pressure difference between the data, adjusting the time series format of the data, and denoising the data to obtain a standard data configuration, based on the standard data configuration, applying the least squares method to optimize and correct the parameters of the partial differential equation, and matching the geological and geothermal conditions to generate a parameter fine-tuning set. The present invention ensures the accuracy and practicality of various types of data collected from geothermal wells by optimizing the data processing, improves the adaptability to geological conditions by adjusting the parameters of the partial differential equations and combining the least squares method, so that the performance of geothermal wells can be simulated and predicted more accurately, and the finite element method is used to conduct an in-depth analysis of the changes in geothermal wells, providing a scientific prediction for the future state of geothermal energy, and optimizing the long-term management and mining process of resources.

[0004] The deep geothermal energy resource assessment methods in existing patent documents often rely on a single data source, such as geological exploration data, meteorological data or hydrological data, etc. Although these methods can provide certain information, they often have assessment biases and limitations due to the single data source. Summary of the invention

[0005] The present application provides a shallow geothermal energy resource assessment method based on geographic information data. By fusion of multi-source data and introduction of data streams, the assessment bias caused by a single data source is avoided, the comprehensiveness and accuracy of the assessment are improved, and the degree of improvement in assessment accuracy is quantified by comparison with actual exploration and development data. Through real-time or near real-time data processing and analysis, the possibility of dynamic assessment of shallow geothermal energy is provided, thereby improving the timeliness and flexibility of the assessment.

[0006] This application provides a shallow geothermal energy resource assessment method based on geographic information data, including: S101, collecting multi-source data and fusing the collected multi-source data; S102, arranging the fused data, and adjusting and controlling the arranged data; S103, extracting data features according to the arranged data set obtained in step S102, building a prediction model based on the extracted data features, and dividing the development area according to the prediction model; S104, adjusting the divided development area, and outputting the adjusted development area.

[0007] Preferably, the prediction model predicts a rectangular area, and the actual geographical geology is a circular area. The coordinates of the four vertices of the rectangle in the two-dimensional coordinate system are identified, the coordinates of the center of the circle and the value of the radius are identified, the boundaries of the rectangle and the circle are compared, and it is determined whether the rectangle and the circle have overlapping parts. If any vertex of the rectangle is within the circle, or the center of the circle is within the rectangle, then there is an overlapping part, otherwise, there is no overlapping part.

[0008] Preferably, the equation is used to calculate whether there is an intersection between the rectangle and the circle. The equation of the circle is , where (h, k) is the center of the circle, r is the radius, and one vertex of the rectangle is , the equation of the rectangle is x=x1, y=y1, substitute x=x1 into the equation of the circle: ,like , then the equation has a solution, the side of the rectangle intersects the circle, otherwise, there is no intersection.

[0009] Preferably, dividing the development area according to the prediction model is: dividing the development area into a plurality of sub-areas, including the following steps: S201, collecting formation temperatures at different depths in the development area, and calculating geothermal gradients based on the collected formation temperatures; S202, identifying the characteristics of the geothermal gradient according to the calculated geothermal gradient; S203, dividing the development area into a plurality of sub-areas according to the value of the geothermal gradient; S204, setting the density of sampling points in the sub-regions according to the divided sub-regions; S205, setting a sampling threshold according to the density of sampling points; S206, collecting data at the set sampling points.

[0010] Preferably, the development area is divided into multiple sub-areas according to the value of the geothermal gradient, including: a geothermal gradient value ≥3.5 °C / 100m is a high thermal gradient area, a geothermal gradient value ranging from 2.0 °C / 100m to 3.5 °C / 100m is a medium thermal gradient area, and a geothermal gradient value <2.0 °C / 100m is a low thermal gradient area. According to the high thermal gradient area, medium thermal gradient area and low thermal gradient area, the development area is spatially divided into high thermal sub-areas, medium thermal sub-areas and low thermal sub-areas.

[0011] Preferably, the types of sampling points are temperature, pressure and flow rate. The preset temperature threshold is 200°C-50°C. When the temperature of the geothermal resource reaches or exceeds 200°C, it indicates that the geothermal activity in the area is intense. When the temperature is lower than 50°C, it indicates that the geothermal resources are scarce. The preset pressure threshold is 10MPa-0.5MPa. When the pressure of the geothermal fluid reaches or exceeds 10MPa, it indicates that the geothermal reservoir has a higher pressure energy. When the pressure of the geothermal fluid is lower than 0.5MPa, it indicates that the pressure energy of the geothermal reservoir is lower. The preset flow rate threshold is 100m³ / h-5m³ / h. When the flow rate of the geothermal fluid reaches or exceeds 100m³ / h, it indicates that the geothermal fluid migration in the area is active. When the flow rate of the geothermal fluid is lower than 5m³ / h, it indicates that the fluid supply of the geothermal reservoir is insufficient or there is blockage.

[0012] Preferably, the specific steps for introducing boundary values ​​into the partial differential equation are: S301, constructing a partial differential equation model based on the influence of formation temperature on shallow geothermal energy in steps S201 to S206; S302, based on the partial differential equation model established in step S301, adjusting the partial differential equation model using the boundary conditions of the development area; S303, verifying the adjusted partial differential equation model according to field measurement data.

[0013] Preferably, the temperature T is taken as an unknown function, and the temperature T is a function of the spatial position x, y and time t, that is, T(x, y, t), wherein the spatial position x represents the lateral width of the formation, the spatial position y represents the longitudinal depth of the formation, and T(x, y, t) represents the temperature at the position (x, y) at time t, and a partial differential equation is constructed: ,in, is the thermal diffusion coefficient, which describes the speed of heat propagation in the medium, ρ is the density of the medium, c is the specific heat capacity of the medium, k is the thermal conductivity, the left side of the equation is the partial derivative of temperature T with respect to time t, which represents the rate of change of temperature with time, and the right side of the equation is the sum of the second-order partial derivatives of temperature T with respect to spatial positions x and y, multiplied by the thermal diffusion coefficient α. The right side of the equation represents the change in the spatial distribution of temperature due to heat conduction.

[0014] Preferably, according to the established partial differential equation model, in the x direction from a to b, that is, x∈[a, b], where a and b represent the boundary values ​​of the lateral width of the formation, in the y direction from the surface, that is, y=0, to a preset depth underground, that is, y=H, y∈[0,H]; for the boundary conditions in the x direction, when on the x=a boundary, the temperature changes with time, and the boundary condition expression is: T(a,y, t) = T_a(t), on the left boundary of the region, the temperature of all points in the y direction at each moment is the same, and this temperature changes with time, which is described by the T_a(t) function; when on the x=b boundary, the temperature also changes with time, and the boundary condition expression is: T(b, y, t) = T_b(t), on the right boundary of the area, the temperature of all points in the y direction at each moment is the same, and this temperature varies with time, which is described by the T_b(t) function; for the boundary conditions in the y direction, when on the y=0 boundary, the temperature varies with time, which is given by the function T_surface(t), and the boundary condition expression is: T(x, 0, t) = T_surface(t), which means that on the surface, the temperature of all points in the x direction at each moment is the same, and this temperature varies with time, which is described by the T_surface(t) function; when on the y=H boundary, the temperature is a function T_bottom(t) that varies with time, and the boundary condition expression is: T(x, H, t) = T_bottom(t), which means that at the preset depth underground, that is, y=H, the temperature of all points in the x direction at each moment is the same, and the temperature is constant or varies with time.

[0015] Preferably, the boundary condition is combined with the partial differential equation to obtain the equation: , which indicates that the rate of change of temperature T with time is equal to the product of the second-order derivative of temperature in space and the thermal diffusion coefficient α.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: by fusion of multi-source data and introduction of data streams, the evaluation bias caused by a single data source is avoided, the comprehensiveness and accuracy of the evaluation are improved, the degree of improvement in evaluation accuracy is quantified by comparison with actual exploration and development data, and the possibility of dynamic evaluation of shallow geothermal energy is provided through real-time or near real-time data processing and analysis, thereby improving the timeliness and flexibility of the evaluation; By dividing the development area into several sub-areas and setting dense sampling points and sampling thresholds in each sub-area, the accuracy and reliability of shallow geothermal energy assessment can be improved, and the risk of assessment bias can be reduced. By setting sampling thresholds, sampling can be ensured at critical moments or under critical conditions, which improves the pertinence and effectiveness of data and helps to more accurately capture the dynamic characteristics of geothermal energy resources. By introducing boundary values, partial differential equations can dynamically reflect the changes of geothermal energy and other physical quantities over time; by adjusting the parameters and form of boundary conditions, the model can more accurately describe the distribution and changes of geothermal energy in the development area; ultimately, a geothermal energy distribution model that can be updated in real time, described dynamically and with high accuracy is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of a shallow geothermal energy resource assessment method based on geographic information data of the present invention; Figure 2 A schematic diagram of a flow chart of dividing a development area into a plurality of sub-areas according to an embodiment of the present invention; Figure 3 The figure is a flow chart of introducing boundary values ​​into a partial differential equation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0019] It should be noted that the terms “vertical”, “horizontal”, “up”, “down”, “left”, “right” and similar expressions used in this document are only for illustrative purposes and do not represent the only implementation method.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0021] Embodiment 1: Figure 1 1 is a flow chart of a shallow geothermal energy resource assessment method based on geographic information data according to an embodiment of the present invention, comprising the following steps: S101, collecting multi-source data and fusing the collected multi-source data; Specifically, meteorological data (temperature, humidity, precipitation, etc.), geological data (stratum structure, lithology, soil thermal conductivity, etc.) and hydrological data (groundwater level, water flow velocity, etc.) are collected, and the collected multi-source data are converted into formats and standardized; the multi-source data are integrated using the Geographic Information System (GIS), which converts the collected multi-source data into a unified geographic coordinate system to ensure the spatial consistency of the multi-source data. After completing the spatial alignment, the GIS platform integrates various pre-processed data (such as meteorological data, geological data, hydrological data, etc.), and superimposes different data layers to form a comprehensive data set; by superimposing the geological structure map and the groundwater level distribution map, the impact of the geological structure on the groundwater flow can be obtained, and the distribution of shallow geothermal energy can be inferred.

[0022] S102, arranging the fused data, and adjusting and controlling the arranged data; Furthermore, obtain the timestamp in each fused data, convert the obtained timestamp into a unified time format UTC time, sort the data in the data stream according to the order of the timestamps, store the sorted data in the database, use machine learning to process and analyze the data according to the characteristics of the data, plot the analysis results into a table, monitor the update frequency of the data source in real time, adjust the data for parallel processing according to the scale and complexity of the data, speed up the data processing, adjust the threshold and number of iterations of the algorithm according to the changes in the scene, and improve the accuracy of the data.

[0023] S103, extracting data features according to the arranged data set obtained in step S102, building a prediction model based on the extracted data features, and dividing the development area according to the prediction model; Specifically, the arranged data set is input into the prediction model to obtain the shallow geothermal resource distribution results predicted by the model, and the geographic information system GIS is used to spatially superimpose the model prediction results and the actual geographical and geological conditions. In some embodiments, the prediction result of the prediction model is a rectangular area, and the actual geographical geology is a circular area. The spatial positions of the rectangle and the circle are clarified, and the coordinates of the four vertices of the rectangle in the two-dimensional coordinate system are identified. At the same time, the coordinates of the center of the circle and the radius value are identified, and the boundaries of the rectangle and the circle are compared to determine whether the rectangle and the circle have an intersection. If any vertex of the rectangle is in the circle, or the center of the circle is in the rectangle, then there is an overlap. Otherwise, there is no overlap. The equation is used to calculate whether there is an intersection between the rectangle and the circle. The equation of the circle is: , where (h, k) is the center of the circle, r is the radius, and one vertex of the rectangle is , the equation of the rectangle is x=x1, y=y1, substitute x=x1 into the equation of the circle: ,like , then the equation has a solution, the side of the rectangle has an intersection with the circle, otherwise, there is no intersection; determine the inclusion relationship between the circle and the rectangle, if the radius of the circle is small enough, the circle is completely inside the rectangle; otherwise, if the rectangle is small enough, the rectangle is completely inside the circle. Based on the intersection and inclusion relationship, determine the overlapping area between the rectangle and the circle, which is the development area. The development area contains all high-probability geothermal resource areas.

[0024] S104, adjusting the divided development area, and outputting the adjusted development area; Furthermore, the designated development area is compared with the actual exploration area to verify the accuracy of the designated area. Based on the comparison results, the boundaries of the designated area are corrected, and the corrected designated area is output in the form of a chart or report.

[0025] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: through the fusion of multi-source data and the introduction of data streams, the evaluation bias caused by a single data source is avoided, the comprehensiveness and accuracy of the evaluation are improved, and the degree of improvement in evaluation accuracy is quantified by comparison with actual exploration and development data. Through real-time or near real-time data processing and analysis, it provides the possibility for dynamic evaluation of shallow geothermal energy, thereby improving the timeliness and flexibility of the evaluation.

[0026] Embodiment 2: Based on the development area divided in Embodiment 1, in this embodiment, the development area is divided into several sub-areas, and a sampling threshold is set in each divided sub-area to improve the accuracy and reliability of shallow geothermal energy assessment.

[0027] like Figure 2 As shown, the development area is divided according to the prediction model as follows: the development area is divided into several sub-areas, including the following steps: S201, collecting formation temperatures at different depths in the development area, and calculating geothermal gradients based on the collected formation temperatures; Furthermore, within the development area, extending downward to 200 meters, a temperature measuring point is set every 20 meters to measure the temperature, so that the temperature data of the formation at different depths can be obtained. The geothermal gradient = temperature difference ÷ depth difference, wherein the temperature difference refers to the difference in temperature at two different depths, and the depth difference refers to the vertical distance between the two depths. In some embodiments, the temperature measured at 10 meters underground is 15°C, and the temperature measured at 100 meters underground is 20°C. The temperature difference = 20°C - 15°C = 5°C, the depth difference = 100 meters - 10 meters = 90 meters, and the geothermal gradient = 5°C ÷ 90 meters ≈ 0.056°C / meter.

[0028] S202, identifying the characteristics of the geothermal gradient according to the calculated geothermal gradient; Specifically, according to the geothermal gradient calculated in step S201, the maximum and minimum values ​​of the geothermal gradient are determined, and the average value is calculated based on the geothermal gradient data. The geothermal gradient data in the development area is compared with the data of the surrounding areas or similar areas to find out the areas where the geothermal gradient values ​​are significantly higher or lower than the surrounding areas. These areas that are higher or lower than the surrounding areas are called abnormal areas, and the identified geothermal gradient abnormal areas are marked on the map.

[0029] S203, dividing the development area into a plurality of sub-areas according to the value of the geothermal gradient; According to the value of geothermal gradient, the development area is divided into multiple sub-areas, including: geothermal gradient value ≥ 3.5 °C / 100m is a high thermal gradient area. The high thermal gradient area is rich in geothermal resources and has a high geothermal gradient. It is an ideal area for geothermal resource development and utilization. The geothermal gradient value ranges from 2.0 °C / 100m to 3.5 °C / 100m (excluding 3.5) is a medium thermal gradient area. The medium thermal gradient area has a moderate geothermal gradient and has certain geothermal resource development potential, but requires more technical investment and cost to control the risks in the mining process. The geothermal gradient value <2.0 °C / 100m is a low thermal gradient area. The geothermal gradient in the low thermal gradient area is low, and the geothermal resources are relatively scarce. It is not suitable for direct large-scale geothermal resource development, but it can be used as a reserve area for geothermal resources or consider other types of land use planning. According to the high thermal gradient area, medium thermal gradient area and low thermal gradient area, the development area is spatially divided into high thermal sub-area, medium thermal sub-area and low thermal sub-area.

[0030] S204, setting the density of sampling points in the sub-regions according to the divided sub-regions; For the high-heat sub-area, since geothermal resources are abundant and vary dramatically, a higher sampling density is needed to accurately capture geothermal characteristics. Dozens or even hundreds of sampling points are set up in each square kilometer to ensure the comprehensiveness and accuracy of the data. For the medium-heat sub-area, geothermal resources are relatively moderate, and the sampling density can be appropriately reduced. Dozens of sampling points are set up in each square kilometer, which can both meet the assessment needs and control costs. For the low-heat sub-area, geothermal resources are relatively scarce, and the sampling density can be further reduced. Several to a dozen sampling points are set up in each square kilometer to obtain basic geothermal data.

[0031] In some embodiments, a geothermal development area has a total area of ​​100 square kilometers. According to the results of geological exploration and geothermal resource assessment, the area is divided into a high-thermal sub-area (30 square kilometers), a medium-thermal sub-area (50 square kilometers) and a low-thermal sub-area (20 square kilometers). High-thermal sub-area: In this area, 100 sampling points are set per square kilometer, so a total of 3,000 sampling points need to be set, and these sampling points will be evenly distributed in the entire high-thermal sub-area to ensure the comprehensiveness and accuracy of the data; Medium-thermal sub-area: In this area, 50 sampling points are set per square kilometer, so a total of 2,500 sampling points need to be set, and these sampling points are evenly distributed in the medium-thermal sub-area to obtain medium-density geothermal data; Low-thermal sub-area: In this area, 10 sampling points are set per square kilometer, so a total of 200 sampling points need to be set, and these sampling points will cover the main geological and hydrological characteristic points of the low-thermal sub-area to obtain basic geothermal data.

[0032] S205, setting a sampling threshold according to the density of sampling points; Furthermore, after the sampling density is determined, before sampling, the sampling parameters are determined as temperature, pressure and flow. The preset temperature threshold is 200°C-50°C. When the temperature of the geothermal resources reaches or exceeds 200°C, it indicates that the geothermal activity in the area may be more intense, and the sampling frequency needs to be increased to obtain more detailed data. However, if the temperature is lower than 50°C, it means that the geothermal resources are relatively scarce or the geothermal activity in the area is weak; the preset pressure threshold is 10MPa-0.5MPa. When the pressure of the geothermal fluid reaches or exceeds 10MPa , indicating that the geothermal reservoir has a high pressure energy, but we should also pay attention to possible safety risks. When the pressure of the geothermal fluid is lower than 0.5MPa, it means that the pressure energy of the geothermal reservoir is low, or there is a risk of fluid leakage; the preset flow threshold is 100m³ / h-5m³ / h. When the flow rate of the geothermal fluid reaches or exceeds 100m³ / h, it indicates that the geothermal fluid migration in the area is active and has a high potential for geothermal energy extraction. When the flow rate of the geothermal fluid is lower than 5m³ / h, it may indicate that the fluid supply of the geothermal reservoir is insufficient or there is a blockage.

[0033] S206, collecting data at the set sampling points.

[0034] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: by dividing the development area into several sub-areas and setting dense sampling points and sampling thresholds in each sub-area, the accuracy and reliability of shallow geothermal energy assessment are improved, and the risk of assessment bias is reduced. By setting the sampling threshold, it is possible to ensure that sampling is carried out at critical moments or under critical conditions, thereby improving the pertinence and effectiveness of the data and helping to more accurately capture the dynamic change characteristics of geothermal energy resources.

[0035] Embodiment 3: Based on Embodiment 1 and Embodiment 2, this embodiment introduces a time derivative term and sets a boundary value to the partial differential equation, so that the equation can dynamically describe the change of geothermal energy.

[0036] like Figure 3 As shown, the specific steps for introducing boundary values ​​for partial differential equations are: S301, constructing a partial differential equation model based on the effects of temperature, time and space on shallow geothermal energy in steps S201 to S206; Specifically, the temperature T is taken as an unknown function. The temperature T is a function of the spatial position x, y and time t, that is, T(x, y, t), where the spatial position x represents the lateral width of the formation, the spatial position y represents the vertical depth of the formation, and T(x, y, t) represents the temperature at the position (x, y) at time t. The partial differential equation is constructed: ,in, is the thermal diffusion coefficient, which describes the speed of heat propagation in the medium, ρ is the density of the medium, c is the specific heat capacity of the medium, k is the thermal conductivity, the left side of the equation is the partial derivative of temperature T with respect to time t, which represents the rate of change of temperature with time, and the right side of the equation is the sum of the second-order partial derivatives of temperature T with respect to spatial positions x and y, multiplied by the thermal diffusion coefficient α. The right side of the equation represents the change in the spatial distribution of temperature due to heat conduction.

[0037] S302, based on the partial differential equation model established in step S301, adjusting the partial differential equation model using the boundary conditions of the development area; Further, according to the established partial differential equation model, in the x direction from a to b, that is, x∈[a, b], in the y direction from the surface (y=0) to the preset depth underground (y=H), that is, y∈[0, H], in the present application, the maximum value of H is 200; for the boundary conditions in the x direction, on the x=a boundary: the temperature changes with time, given by the function T_a(t), and the boundary condition expression is: T(a, y, t) = T_a(t), on the left boundary of the region, the temperature of all points in the y direction at each moment is the same, and this temperature changes with time, described by the T_a(t) function, on the x=b boundary: the temperature also changes with time, given by the function T_b(t), and the boundary condition expression is: T(b, y, t) = T_b(t), on the right boundary of the region, the temperature of all points in the y direction at each moment is the same, and this temperature varies with time, described by the T_b(t) function; for the boundary conditions in the y direction, on the y=0 boundary (surface): the temperature varies with time, given by the function T_surface(t), and the boundary condition expression is: T(x, 0, t) = T_surface(t), which means that on the surface (i.e. y=0), the temperature of all points in the x direction at each moment is the same, and this temperature varies with time, described by the T_surface(t) function, on the y=H boundary (preset depth underground): the temperature can be a constant T_bottom, or a function T_bottom(t) that varies with time, and the boundary condition expression is: T(x, H, t) = T_bottom (or T(x, H, t) =T_bottom(t)), which means that at a preset depth underground (i.e. y=H), the temperature of all points in the x direction at each moment is the same. This temperature can be constant or change with time.

[0038] Combining the boundary conditions with the partial differential equation yields the following equation: , which indicates that the rate of change of temperature T with time is equal to the product of the second-order derivative of temperature in space and the thermal diffusion coefficient α. The initial condition T(x, y, 0) = T_initial(x, y) indicates the distribution of the temperature field at the initial time (t=0). Numerical solution methods (such as finite difference method, finite element method, etc.) are used to solve partial differential equations, and the model is verified using actual measurement data or simulation data to evaluate the accuracy and reliability of the model.

[0039] In some embodiments, there is a rectangular shallow geothermal energy development area, whose range in the x direction is [0, 100] meters, and the range in the y direction is [0, 200] meters (i.e., from the surface to a depth of 200 meters underground), and the medium parameters are: density ρ = 1800 kg / m³, specific heat capacity c = 1000 J / (kg·K), thermal conductivity k = 1.5 W / (m·K), thermal diffusion coefficient: ; Construct the partial differential equation: ; Boundary conditions in the x direction: On the x=0 boundary: T(0, y, t) = T_a(t); T_a(t) = (represents a sinusoidal fluctuation with a period of 24 hours and an average temperature of 20°C); On the x=100 boundary: T(100, y, t) = T_b(t); T_b(t) = (Also with a 24-hour period, but with different average temperature and fluctuation amplitude), boundary conditions in the y direction: On the y=0 boundary (surface): T(x, 0, t) = T_surface(t); T_surface(t) = (Surface temperature is greatly affected by the temperature difference between day and night), on the y=200 boundary (200 meters underground): T(x, 200, t) = T_bottom = 18°C ​​(assuming that the temperature is constant at 200 meters underground), initial conditions: at the initial moment (t=0), the temperature field is uniformly distributed, T(x, y, 0) = T_initial(x, y) = 19°C (assuming that the initial temperature of the entire area is the same). The original partial differential equation is adjusted according to the values ​​calculated based on the above boundary conditions and initial conditions to obtain the adjusted equation: ; The finite difference method or the finite element method is selected to numerically solve the partial differential equation. By discretizing the space and time domains, the continuous equation is converted into a system of algebraic equations for solution.

[0040] S303, verifying the adjusted partial differential equation model according to field measurement data; Furthermore, the adjusted partial differential equation model is used to predict the collected data and generate prediction results, which are compared with the actual measured data or simulated data to evaluate the accuracy of the model in predicting temperature distribution, heat flow changes, etc.

[0041] The technical solutions in the above-mentioned embodiments of the present application have at least the following technical effects or advantages: by introducing boundary values, the partial differential equations can dynamically reflect the changes of physical quantities such as geothermal energy over time; by adjusting the parameters and forms of the boundary conditions, the model can more accurately describe the distribution and changes of geothermal energy in the development area; and finally, a geothermal energy distribution model that can be updated in real time, dynamically described and with high accuracy is obtained.

[0042] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A shallow geothermal energy resource assessment method based on geographic information data, characterized in that: include: S101, collecting multi-source data and fusing the collected multi-source data; S102, arranging the fused data, and adjusting and controlling the arranged data; S103, extracting data features according to the arranged data set obtained in step S102, building a prediction model based on the extracted data features, and dividing the development area according to the prediction model; S104, adjusting the divided development area, and outputting the adjusted development area.

2. The shallow geothermal energy resource assessment method based on geographic information data according to claim 1, characterized in that: The prediction result of the prediction model is a rectangular area, and the actual geographical geology is a circular area. The coordinates of the four vertices of the rectangle in the two-dimensional coordinate system are identified, the coordinates of the center of the circle and the value of the radius are identified, the boundaries of the rectangle and the circle are compared, and it is determined whether the rectangle and the circle have overlapping parts. If any vertex of the rectangle is in the circle, or the center of the circle is in the rectangle, then there is an overlapping part, otherwise, there is no overlapping part.

3. The shallow geothermal energy resource assessment method based on geographic information data according to claim 2, characterized in that: Use the equation to calculate whether there is an intersection between the rectangle and the circle. The equation of the circle is , where (h, k) is the center of the circle, r is the radius, and one vertex of the rectangle is , the equation of the rectangle is x=x1, y=y1, substitute x=x1 into the equation of the circle: ,like , then the equation has a solution, the side of the rectangle intersects the circle, otherwise, there is no intersection.

4. The shallow geothermal energy resource assessment method based on geographic information data according to claim 1, characterized in that: Dividing the development area according to the prediction model is as follows: Dividing the development area into several sub-areas includes the following steps: S201, collecting formation temperatures at different depths in the development area, and calculating geothermal gradients based on the collected formation temperatures; S202, identifying the characteristics of the geothermal gradient according to the calculated geothermal gradient; S203, dividing the development area into a plurality of sub-areas according to the value of the geothermal gradient; S204, setting the density of sampling points in the sub-regions according to the divided sub-regions; S205, setting a sampling threshold according to the density of sampling points; S206, collecting data at the set sampling points.

5. The shallow geothermal energy resource assessment method based on geographic information data according to claim 4, characterized in that: According to the value of geothermal gradient, the development area is divided into multiple sub-areas, including: geothermal gradient value ≥ 3.5 °C / 100m is a high thermal gradient area, geothermal gradient value range is 2.0 °C / 100m to 3.5 °C / 100m is a medium thermal gradient area, geothermal gradient value < 2.0°C / 100m is a low thermal gradient area. According to the high thermal gradient area, medium thermal gradient area and low thermal gradient area, the development area is spatially divided into high thermal sub-area, medium thermal sub-area and low thermal sub-area.

6. The shallow geothermal energy resource assessment method based on geographic information data according to claim 4, characterized in that: The types of sampling points are temperature, pressure and flow. The preset temperature threshold is 200°C-50°C. When the temperature of the geothermal resources reaches or exceeds 200°C, it indicates that the geothermal activity in the area is intense. When the temperature is below 50°C, it indicates that the geothermal resources are scarce. The preset pressure threshold is 10MPa-0.5MPa. When the pressure of the geothermal fluid reaches or exceeds 10MPa, it indicates that the geothermal reservoir has a high pressure energy. When the pressure of the geothermal fluid is below 0.5MPa, it indicates that the pressure energy of the geothermal reservoir is low. The preset flow threshold is 100m³ / h-5m³ / h. When the flow of the geothermal fluid reaches or exceeds 100m³ / h, it indicates that the geothermal fluid migration in the area is active. When the flow of the geothermal fluid is below 5m³ / h, it indicates that the fluid supply of the geothermal reservoir is insufficient or there is blockage.

7. The shallow geothermal energy resource assessment method based on geographic information data according to claim 4, characterized in that: The specific steps for introducing boundary values ​​for partial differential equations are: S301, constructing a partial differential equation model based on the influence of formation temperature on shallow geothermal energy in steps S201 to S206; S302, based on the partial differential equation model established in step S301, adjusting the partial differential equation model using the boundary conditions of the development area; S303, verifying the adjusted partial differential equation model according to field measurement data.

8. The shallow geothermal energy resource assessment method based on geographic information data according to claim 7, characterized in that: Take temperature T as an unknown function. Temperature T is a function of spatial position x, y and time t, that is, T(x, y, t), where spatial position x represents the lateral width of the formation, spatial position y represents the longitudinal depth of the formation, and T(x, y, t) represents the temperature at position (x, y) at time t. Construct a partial differential equation: ,in, is the thermal diffusion coefficient, which describes the speed of heat propagation in the medium, ρ is the density of the medium, c is the specific heat capacity of the medium, k is the thermal conductivity, the left side of the equation is the partial derivative of temperature T with respect to time t, which represents the rate of change of temperature with time, and the right side of the equation is the sum of the second-order partial derivatives of temperature T with respect to spatial positions x and y, multiplied by the thermal diffusion coefficient α. The right side of the equation represents the change in the spatial distribution of temperature due to heat conduction.

9. The shallow geothermal energy resource assessment method based on geographic information data according to claim 7, characterized in that: According to the established partial differential equation model, in the x direction from a to b, that is, x∈[a, b], where a and b represent the boundary values ​​of the lateral width of the stratum, in the y direction from the surface, that is, y=0, to the preset depth underground, that is, y=H, y∈[0, H]; for the boundary conditions in the x direction, when on the x=a boundary, the temperature changes with time, and the boundary condition expression is: T(a, y, t) = T_a(t), on the left boundary of the region, the temperature of all points in the y direction at each moment is the same, and this temperature changes with time, which is described by the T_a(t) function; when on the x=b boundary, the temperature also changes with time, and the boundary condition expression is: T(b, y, t) = T_b(t), on the right boundary of the area, the temperature of all points in the y direction at each moment is the same, and this temperature varies with time, which is described by the T_b(t) function; for the boundary conditions in the y direction, when on the y=0 boundary, the temperature varies with time, which is given by the function T_surface(t), and the boundary condition expression is: T(x, 0, t) = T_surface(t), which means that on the surface, the temperature of all points in the x direction at each moment is the same, and this temperature varies with time, which is described by the T_surface(t) function; when on the y=H boundary, the temperature is a function T_bottom(t) that varies with time, and the boundary condition expression is: T(x, H, t) = T_bottom(t), which means that at the preset depth underground, that is, y=H, the temperature of all points in the x direction at each moment is the same, and the temperature is constant or varies with time.

10. The shallow geothermal energy resource assessment method based on geographic information data according to claim 9, characterized in that: Combining the boundary conditions with the partial differential equation yields the following equation: , which indicates that the rate of change of temperature T with time is equal to the product of the second-order derivative of temperature in space and the thermal diffusion coefficient α.

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