Urban area shallow geothermal energy resource assessment method and system

Abstract

This invention discloses a method and system for assessing shallow geothermal energy resources in urban areas, relating to the field of geothermal energy resource technology. The method includes: constructing a basic dataset and performing spatial grid division to construct multiple geothermal evaluation units; deploying multiple thermal response test wells within the multiple geothermal evaluation units, conducting cyclic heat injection tests, constructing multiple formation temperature response sequences, constructing multiple temperature response feature vectors, and performing formation thermal conductivity inversion to obtain multiple formation thermal conductivity parameters; calculating multiple theoretical heat transfer capacities for the multiple geothermal evaluation units; constructing multiple groundwater heat transfer capacity feature vectors and performing heat transfer analysis to obtain multiple engineering heat transfer capacities; and combining the multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities to conduct thermal energy resource assessment and obtain regional assessment results. This invention solves the technical problem of insufficient accuracy in assessing shallow geothermal energy resources in urban areas in existing technologies, achieving the technical effect of improving the accuracy of shallow geothermal energy resource assessment in urban areas.

Description

Technical Field

[0001] This invention relates to the field of geothermal energy resources technology, specifically to a method and system for assessing shallow geothermal energy resources in urban areas. Background Technology

[0002] With the optimization of urban energy structures and the increasing demand for clean energy utilization, shallow geothermal energy, as a stable and renewable energy form, is widely used in urban building heating and cooling. In the development and utilization of shallow geothermal energy, it is usually necessary to assess the underground heat exchange capacity and geothermal resource potential of urban areas to provide a basis for the planning and deployment of ground source heat pump projects. The assessment process typically uses borehole data, geotechnical thermal property parameters, and buried pipe heat exchange theoretical models to estimate underground thermal conductivity and heat exchange capacity, thereby calculating the potential of shallow geothermal energy resources in the region. However, factors such as groundwater flow conditions and reinjection capacity have a significant impact on underground heat exchange capacity. If the assessment mainly relies on theoretical calculation parameters without fully integrating the dynamic characteristics of groundwater and the engineering heat exchange capacity, the assessment results may deviate from the actual usable geothermal resources, thus affecting the accuracy of the assessment of shallow geothermal energy resources in urban areas. Summary of the Invention

[0003] This application provides a method and system for assessing shallow geothermal energy resources in urban areas, which addresses the technical problem of insufficient accuracy in assessing shallow geothermal energy resources in urban areas in existing technologies.

[0004] In view of the above problems, this application provides a method and system for assessing shallow geothermal energy resources in urban areas.

[0005] A first aspect of this application provides a method for assessing shallow geothermal energy resources in urban areas, the method comprising:

[0006] Data on borehole formations, geotechnical thermal properties, groundwater dynamics, and building heating and cooling loads in urban areas are acquired to construct a basic dataset. Based on this dataset, the urban area is spatially gridded to create multiple geothermal evaluation units. Multiple thermal response test wells are deployed within these units, and cyclic heat injection tests are conducted to continuously collect temperature change data within the wells, constructing multiple formation temperature response sequences. These sequences are then iterated through to calculate time-series slopes and temperature increments, resulting in multiple temperature response features. The system generates vectors and performs formation thermal conductivity inversion to obtain multiple formation thermal conductivity parameters. Based on these parameters, as well as the borehole depth and well spacing in the basic dataset, it calculates multiple theoretical heat transfer capacities for multiple geothermal evaluation units. Using the groundwater dynamic data, it performs groundwater flow velocity and reinjection capacity calculations for the multiple geothermal evaluation units, constructing multiple groundwater heat transfer capacity feature vectors. It then performs engineering heat transfer analysis to obtain multiple engineering heat transfer capacities. Finally, it combines the multiple theoretical and engineering heat transfer capacities to conduct a thermal energy resource assessment, obtaining regional assessment results.

[0007] A second aspect of this application provides a system for assessing shallow geothermal energy resources in urban areas, the system comprising:

[0008] The grid generation module is used to acquire borehole formation data, geotechnical thermal property data, groundwater dynamic data, and building heating and cooling load data for urban areas, construct a basic dataset, and perform spatial grid generation on the urban area based on the basic dataset to construct multiple geothermal evaluation units. The heat injection testing module is used to deploy multiple thermal response test wells in the multiple geothermal evaluation units, conduct cyclic heat injection tests on the multiple thermal response test wells, continuously collect temperature change data within the wells, and construct multiple formation temperature response sequences. The inversion module is used to traverse the multiple formation temperature response sequences, calculate the time series slope and temperature increment, and construct multiple temperature response feature vectors. The system performs formation thermal conductivity inversion to obtain multiple formation thermal conductivity parameters; a heat transfer capacity calculation module is used to calculate multiple theoretical heat transfer capacities for multiple geothermal evaluation units based on the multiple formation thermal conductivity parameters and the borehole depth and well spacing in the basic data; a heat transfer analysis module is used to perform groundwater flow velocity calculation and reinjection capacity calculation for the multiple geothermal evaluation units based on the groundwater dynamic data, construct multiple groundwater heat transfer capacity feature vectors, perform engineering heat transfer analysis, and obtain multiple engineering heat transfer capacities; a resource assessment module is used to perform thermal energy resource assessment by combining the multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities to obtain regional assessment results.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application acquires borehole formation data, geotechnical thermal property data, groundwater dynamic data, and building heating and cooling load data for urban areas to construct a basic dataset. Based on this basic dataset, spatial grid division is performed on the urban area to construct multiple geothermal evaluation units. Multiple thermal response test wells are deployed within these geothermal evaluation units, and cyclic heat injection tests are conducted on these wells to continuously collect temperature change data within the wells, constructing multiple formation temperature response sequences. The time series slope and temperature increment are calculated by traversing these multiple formation temperature response sequences to construct multiple temperature response sequences. The invention generates feature vectors and performs formation thermal conductivity inversion to obtain multiple formation thermal conductivity parameters. Based on these parameters, along with the borehole depth and well spacing in the basic dataset, it calculates multiple theoretical heat transfer capacities for multiple geothermal evaluation units. Using groundwater dynamic data, it calculates groundwater flow velocity and reinjection capacity for each geothermal evaluation unit, constructing multiple groundwater heat transfer capacity feature vectors. It then performs engineering heat transfer analysis to obtain multiple engineering heat transfer capacities. Finally, it combines these theoretical and engineering heat transfer capacities to conduct a thermal energy resource assessment, obtaining regional assessment results. This invention addresses the technical problem of insufficient accuracy in assessing shallow geothermal energy resources in urban areas in existing technologies. By combining theoretical and engineering heat transfer capacities for comprehensive assessment, it improves the accuracy of shallow geothermal energy resource assessment in urban areas. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of a method for assessing shallow geothermal energy resources in urban areas provided in this application embodiment;

[0013] Figure 2 This is a schematic diagram of a shallow geothermal energy resource assessment system for urban areas, provided as an embodiment of this application.

[0014] Figure labeling: 11 Mesh generation module, 12 heat injection test module, 13 inversion module, 14 heat transfer capacity calculation module, 15 heat transfer analysis module, 16 resource assessment module. Detailed Implementation

[0015] This application provides a method and system for assessing shallow geothermal energy resources in urban areas. It addresses the technical problem of insufficient accuracy in assessing shallow geothermal energy resources in urban areas in existing technologies by combining theoretical heat exchange capacity with engineering heat exchange capacity for comprehensive assessment, thereby improving the technical effect of assessing shallow geothermal energy resources in urban areas.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a method for assessing shallow geothermal energy resources in urban areas, the method comprising:

[0019] Step S100: Obtain borehole strata data, soil thermal property data, groundwater dynamic data, and building heating and cooling load data for the urban area, construct a basic dataset, and perform spatial grid division on the urban area based on the basic dataset to construct multiple geothermal evaluation units.

[0020] In this embodiment, existing geological survey data, hydrogeological data, and urban building energy consumption data within the urban area are first retrieved and organized to obtain borehole stratigraphic data, soil and rock thermal property data, groundwater dynamic data, and building heating and cooling load data for the urban area. Specifically, borehole stratigraphic data is obtained by retrieving borehole columnar sections, borehole stratification records, and borehole depth records from geological survey reports, reflecting the stratigraphic structure and lithological distribution at different depths at each borehole location; soil and rock thermal property data is obtained by retrieving soil and rock thermal parameter test data or existing engineering thermal property parameter data, characterizing the thermal conductivity and heat capacity characteristics of different soil and rock layers; groundwater dynamic data is obtained by collecting groundwater monitoring well water level records, hydrogeological survey data, and long-term groundwater monitoring data, reflecting groundwater level changes and groundwater flow status; building heating and cooling load data is obtained by retrieving pre-stored building heating and cooling load data from urban energy planning data or building energy consumption statistics, characterizing the corresponding heating and cooling load demands of buildings within the urban area during heating and cooling operations. After acquiring the above data, the borehole strata data, soil and rock thermal property data, groundwater dynamic data, and building heating and cooling load data are uniformly organized. This includes unifying the spatial coordinates, data formats, and attribute information of data from different sources, and overlaying and matching them according to a unified spatial reference to construct a basic dataset. The basic dataset is a collection of data that comprehensively reflects the characteristics of the underground strata structure, soil and rock thermal property, groundwater dynamics, and building heating and cooling demands in the urban area.

[0021] Next, spatial gridding is performed on the urban area based on the base dataset to construct multiple geothermal assessment units. Specifically, according to the spatial boundary of the urban area, the urban area is divided into regular grids of preset size, resulting in multiple spatial grid units. Subsequently, borehole stratigraphic data, soil and rock thermal property data, groundwater dynamics data, and building heating and cooling load data from the base dataset are mapped to each spatial grid unit according to their spatial location. This ensures that each spatial grid unit is associated with corresponding stratigraphic structure information, soil and rock thermal property parameter information, groundwater dynamics information, and building heating and cooling load information. Through the above spatial gridding and data mapping process, each spatial grid unit is identified as a geothermal assessment unit, thereby constructing multiple geothermal assessment units.

[0022] Step S200: Deploy multiple thermal response test wells in the multiple geothermal evaluation units, conduct cyclic heat injection tests on the multiple thermal response test wells, continuously collect temperature change data in the wells, and construct multiple formation temperature response sequences.

[0023] In this embodiment, firstly, based on the spatial location and range of multiple geothermal evaluation units, the staff manually selects the location for thermal response test wells within each geothermal evaluation unit according to the on-site construction conditions, and drills are carried out at the selected locations to form thermal response test wells; subsequently, buried pipe heat exchange circuits and temperature acquisition devices are installed in multiple thermal response test wells, wherein the buried pipe heat exchange circuits are used to form circulating heat exchange channels, and the temperature acquisition devices are used to collect data on the temperature changes of the fluid inside the well during the circulating heat injection test.

[0024] After deploying multiple thermal response test wells, a constant thermal power circulating fluid was injected into the underground pipe heat exchange loop corresponding to each thermal response test well. Circulating heat injection tests were then conducted on multiple thermal response test wells. The circulating fluid circulates within the underground pipe heat exchange loop and exchanges heat with the surrounding formation, causing the fluid temperature inside the well to change over time. During the test, fluid temperature data inside the well was continuously collected at preset time intervals and recorded chronologically to obtain multiple formation temperature response sequences. These sequences are used to characterize the temperature response changes of the formation in the area where the corresponding thermal response test well is located in response to the circulating heat injection test.

[0025] Furthermore, in the method provided in the application embodiments, multiple thermal response test wells are deployed in the multiple geothermal evaluation units, cyclic heat injection tests are performed on the multiple thermal response test wells, and temperature change data inside the wells are continuously collected to construct multiple formation temperature response sequences, which also includes:

[0026] Temperature acquisition devices were deployed in multiple thermal response test wells, and circulating fluid with constant thermal power was injected into the corresponding buried pipe heat exchange circuit. During the test, fluid temperature data in the wells were collected at preset time intervals to obtain multiple formation temperature response sequences.

[0027] In this embodiment, a temperature acquisition device is installed along the buried pipe heat exchange circuit in each thermal response test well. The temperature acquisition device is a temperature sensor or temperature recorder used to monitor the temperature of the fluid in the well in real time. It is connected to an external data recording device through a data acquisition line, so that the temperature acquisition device can continuously record the temperature change of the circulating fluid during the flow process in the well. Subsequently, the circulating fluid is injected into the corresponding buried pipe heat exchange circuit, which is a closed heat exchange pipeline structure set in the thermal response test well. The circulating fluid is driven by a circulating pump to form a continuous circulation flow in the buried pipe heat exchange circuit, and a heating device provides constant heat power to the circulating fluid, so that the circulating fluid continuously releases heat to the surrounding strata during the flow of the buried pipe heat exchange circuit, thereby forming a stable thermal disturbance process in the strata around the thermal response test well.

[0028] Next, during the cyclic heat injection test, the temperature of the fluid in the well is automatically collected by the temperature acquisition device at preset time intervals. The preset time interval is a fixed sampling time interval set to ensure that the temperature data has continuous time recording characteristics. The collected fluid temperature data in the well is stored by the data recording device and organized according to the acquisition time sequence so that the fluid temperature data in the well corresponding to each time node forms a continuous time data sequence. Then, the continuous temperature data corresponding to each thermal response test well is arranged in chronological order to obtain multiple formation temperature response sequences. The formation temperature response sequences are used to characterize the temperature change response process of the formation under constant thermal power cyclic heat injection conditions.

[0029] Step S300: Traverse the multiple formation temperature response sequences to calculate the time series slope and temperature increment, construct multiple temperature response feature vectors, and perform formation thermal conductivity inversion to obtain multiple formation thermal conductivity parameters.

[0030] In this embodiment, multiple formation temperature response sequences are sequentially traversed according to the correspondence of thermal response test wells. When calculating the time series slope and temperature increment for each formation temperature response sequence, the well fluid temperature data corresponding to each sampling time and the sampling time data corresponding to the well fluid temperature data are read first. Then, the difference between the well fluid temperature data of two adjacent sampling times is calculated according to the sampling time order to obtain the temperature increment of the corresponding time period. The temperature increment is the temperature change obtained by subtracting the well fluid temperature data of the previous sampling time from the well fluid temperature data of the later sampling time. Then, the time interval between two adjacent sampling times is extracted, and the time series slope of the time period is calculated according to the ratio between the temperature increment and the corresponding time interval, so as to obtain the temperature change rate of the well fluid temperature with time. After completing the calculation of all time periods in the same formation temperature response sequence, the time series slope and temperature increment corresponding to each time period are combined in time order to form the temperature response feature vector corresponding to the formation temperature response sequence. Multiple formation temperature response sequences are processed one by one in the same way to construct multiple temperature response feature vectors.

[0031] After constructing multiple temperature response feature vectors, formation thermal conductivity inversion is performed on each feature vector to obtain the corresponding formation thermal conductivity parameters. Specifically, firstly, the constant thermal power, well depth parameters, and time values ​​at each sampling moment in the cyclic heat injection test corresponding to the temperature response feature vector are read, and the calculation relationship between well temperature change and formation thermal conductivity is established based on the cyclic heat injection test conditions. Then, the candidate formation thermal conductivity value range is set according to the formation thermal conductivity statistical range recorded in the geotechnical thermal property data, and multiple candidate formation thermal conductivity values ​​are generated within this value range according to a preset step size. For each candidate formation thermal conductivity value, the corresponding theoretical temperature value is calculated by combining the constant thermal power, well depth parameters, and time values ​​at each sampling moment. When calculating the theoretical temperature value, the heat release per unit well depth is first calculated, that is, the constant thermal power is divided by the well depth parameters to obtain the heat input per unit length. Then, for each sampling moment, the natural logarithm of the time value at that sampling moment is calculated. The theoretical temperature change value corresponding to the sampling time is obtained by dividing the heat release per unit well depth by (4π multiplied by the candidate formation thermal conductivity value) and then multiplying it by the natural logarithm of time. The theoretical temperature change values ​​corresponding to all sampling times are arranged in chronological order to obtain the theoretical temperature sequence. Then, the theoretical temperature sequence is compared with the actual temperature value at the corresponding sampling time point by point, and the difference between the theoretical temperature value and the actual temperature value at each sampling time is calculated. All differences are squared and summed to obtain the error value. Finally, the error values ​​corresponding to different candidate formation thermal conductivity values ​​are compared, and the candidate formation thermal conductivity value with the smallest error value is determined as the formation thermal conductivity parameter corresponding to the temperature response feature vector. The above steps are then used to invert multiple temperature response feature vectors to obtain multiple formation thermal conductivity parameters.

[0032] Step S400: Calculate the theoretical heat transfer capacity of multiple geothermal evaluation units based on the multiple formation thermal conductivity parameters, the centralized borehole depth and well spacing of the basic data.

[0033] In this embodiment, after determining multiple formation thermal conductivity parameters, the formation thermal conductivity parameters corresponding to each geothermal evaluation unit are read sequentially according to the correspondence of the geothermal evaluation units. The borehole depth and well spacing corresponding to each geothermal evaluation unit are then extracted from the basic dataset. Specifically, the spatial range of each geothermal evaluation unit is first determined, and then the formation thermal conductivity parameters corresponding to that geothermal evaluation unit are retrieved. Next, the borehole depth corresponding to that geothermal evaluation unit is read from the basic dataset. The borehole depth is the depth from the surface to the bottom of the well, used to characterize the effective length of the buried pipe heat exchange circuit underground. Then, the well spacing corresponding to that geothermal evaluation unit is read from the basic dataset. The well spacing is the horizontal distance between adjacent heat exchange wells, used to characterize the density of heat exchange well placement and the degree of thermal interference between wells. After completing the reading of the formation thermal conductivity parameters, borehole depth, and well spacing, the formation thermal conductivity parameters, borehole depth, and well spacing are organized according to the same geothermal evaluation unit to obtain a set of theoretical heat exchange capacity calculation parameters corresponding to each geothermal evaluation unit.

[0034] Subsequently, the theoretical heat transfer capacity is calculated for each geothermal evaluation unit. First, the formation thermal conductivity parameter corresponding to the geothermal evaluation unit is multiplied by the borehole depth to obtain the theoretical heat transfer baseline value of a single well for that geothermal evaluation unit. Then, the heat transfer well density per unit area is calculated based on the well spacing. The heat transfer well density is obtained by multiplying the well spacing by the well spacing to get the area occupied by a single heat transfer well, and then dividing the unit area by the area occupied. The theoretical heat transfer baseline value of a single well is then multiplied by the heat transfer well density to obtain the theoretical heat transfer capacity per unit area. To obtain the theoretical heat transfer capacity for the entire geothermal evaluation unit, the theoretical heat transfer capacity per unit area is multiplied by the unit area of ​​the geothermal evaluation unit to obtain the theoretical heat transfer capacity of that geothermal evaluation unit. The above steps are repeated for multiple geothermal evaluation units to obtain multiple theoretical heat transfer capacities for multiple geothermal evaluation units.

[0035] Step S500: Based on the groundwater dynamic data, perform groundwater flow velocity calculation and reinjection capacity calculation for the multiple geothermal evaluation units, construct multiple groundwater heat exchange capacity feature vectors, perform engineering heat exchange analysis, and obtain multiple engineering heat exchange capacities.

[0036] In this embodiment, when calculating groundwater flow velocity and reinjection capacity for multiple geothermal evaluation units based on groundwater dynamic data, and constructing multiple groundwater heat exchange capacity feature vectors, the groundwater flow velocity and reinjection capacity of multiple geothermal evaluation units at multiple time points are analyzed based on groundwater dynamic data to obtain multiple groundwater flow velocity-reinjection capacity sequences. Subsequently, multi-scale trend overlay analysis is performed on the multiple groundwater flow velocity-reinjection capacity sequences to extract multiple trend overlay features. Then, the trend tolerance interval is identified for the last groundwater flow velocity-reinjection capacity in the multiple groundwater flow velocity-reinjection capacity sequences using the multiple trend overlay features, thereby constructing multiple groundwater heat exchange capacity feature vectors.

[0037] After constructing multiple groundwater heat exchange capacity feature vectors, engineering heat exchange analysis is performed to obtain multiple engineering heat exchange capacities. Specifically, according to the correspondence of geothermal evaluation units, the groundwater heat exchange capacity feature vectors corresponding to each geothermal evaluation unit are read sequentially, and the identifier groundwater flow velocity and identifier reinjection capacity are extracted from each groundwater heat exchange capacity feature vector. The identifier groundwater flow velocity and identifier reinjection capacity are derived from the values ​​determined after the aforementioned groundwater flow velocity-reinjection capacity sequence is labeled with a trend tolerance interval. Subsequently, the identifier groundwater flow velocity and identifier reinjection capacity are multiplied to obtain the groundwater flow heat exchange intensity value, and the water density parameter, water specific heat capacity parameter, heat exchange system design temperature difference, and characteristic length are read. The water density parameter and water specific heat capacity parameter are obtained by looking up the conventional thermal property parameter table of water bodies. The heat exchange system design temperature difference is determined by reading the building's heating and cooling load data and combining it with the operating parameters of the ground source heat pump system. Specifically, first... Based on the building's heating and cooling load data, the supply and return water temperatures of the circulating fluid under the building's cooling or heating operation conditions are determined. Then, the design temperature difference of the heat exchange system is determined by calculating the temperature difference between the supply and return water temperatures. The characteristic length is determined by reading the borehole depth of the corresponding geothermal evaluation unit in the basic dataset. The depth of the borehole from the ground surface to the bottom of the well is taken as the effective heat exchange length of the buried pipe heat exchange loop, and this effective heat exchange length is determined as the characteristic length. After obtaining the above parameters, the groundwater flow heat exchange intensity value is multiplied by the water density parameter, the water specific heat capacity parameter, and the design temperature difference. The result is divided by the characteristic length to obtain the engineering heat exchange capacity value corresponding to the geothermal evaluation unit. The same calculation method is used to calculate multiple geothermal evaluation units to obtain multiple engineering heat exchange capacities.

[0038] Furthermore, in the method provided in the application embodiment, based on the groundwater dynamic data, groundwater flow velocity calculation and reinjection capacity calculation are performed on the multiple geothermal evaluation units to construct multiple groundwater heat exchange capacity feature vectors, which further includes:

[0039] Based on the groundwater dynamic data, the groundwater flow velocity and recharge capacity of the multiple geothermal evaluation units are analyzed at multiple time nodes to obtain multiple groundwater flow velocity-recharge capacity sequences; multi-scale trend overlay analysis is performed on the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple trend overlay features; using the multiple trend overlay features, trend tolerance intervals are identified for multiple last-position groundwater flow velocity-recharge capacity sequences to construct the multiple groundwater heat exchange capacity feature vectors.

[0040] In this embodiment, groundwater flow velocity and reinjection capacity analysis are first performed on multiple geothermal assessment units at multiple time nodes based on groundwater dynamic data. In this process, groundwater dynamic data corresponding to each geothermal assessment unit is read sequentially according to their correspondence. Groundwater level data, groundwater head data, groundwater flow rate data, and groundwater reinjection volume data corresponding to each time node are extracted from the groundwater dynamic data. Then, groundwater flow velocity is calculated for each time node. First, the groundwater head data corresponding to two adjacent monitoring locations at that time node is read, and the difference in groundwater head between the two monitoring locations is calculated. Then, the distance between the two monitoring locations is read, and the difference in groundwater head is divided by the distance to obtain the groundwater hydraulic gradient corresponding to that time node. Next, the groundwater flow rate data corresponding to that time node is read, and the groundwater flow rate data is divided by the corresponding cross-sectional area to obtain the average groundwater flow velocity. Finally, the average groundwater flow velocity is correlated with the groundwater hydraulic gradient to determine the groundwater flow velocity value corresponding to that time node. After calculating the groundwater flow velocity, the reinjection capacity is calculated for the same time point. First, the groundwater reinjection volume data and corresponding reinjection time corresponding to that time point are read. The groundwater reinjection volume data is divided by the corresponding reinjection time to obtain the reinjection volume per unit time corresponding to that time point. The reinjection volume per unit time is determined as the reinjection capacity value corresponding to that time point. After calculating the groundwater flow velocity and reinjection capacity values ​​for each time point in the same geothermal evaluation unit, the groundwater flow velocity and reinjection capacity values ​​corresponding to each time point are combined and arranged in chronological order to form the groundwater flow velocity-reinjection capacity sequence corresponding to that geothermal evaluation unit. Multiple geothermal evaluation units are processed in the same way to obtain multiple groundwater flow velocity-reinjection capacity sequences.

[0041] Next, a multi-scale trend overlay analysis was performed on multiple groundwater flow velocity-recharge capacity sequences. In this process, extreme value analysis was conducted on multiple groundwater flow velocity-recharge capacity sequences to determine multiple sets of analytical scales; then, based on these sets of analytical scales, multi-scale trend analysis was performed on the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple sets of trend features; finally, trend overlay analysis was performed on these multiple sets of trend features to obtain multiple overlay trend features.

[0042] Finally, multiple trend overlay features are used to identify the trend tolerance intervals for multiple last-position groundwater flow velocity-recharge capacity in multiple groundwater flow velocity-recharge capacity sequences. When constructing multiple groundwater heat exchange capacity feature vectors, firstly, multiple groundwater flow velocity-recharge capacity sequences and their corresponding trend overlay features are read sequentially according to the correspondence of geothermal evaluation units. Then, the last-position groundwater flow velocity and last-position recharge capacity corresponding to the last time node are extracted from each groundwater flow velocity-recharge capacity sequence. Subsequently, the trend tolerance interval is calculated based on the corresponding trend overlay features. First, the change amplitude in the trend overlay features is extracted, and then the change amplitude is added to the last-position groundwater flow velocity and last-position recharge capacity respectively to obtain the corresponding upper limit value. At the same time, the change amplitude is subtracted from the last-position groundwater flow velocity and last-position recharge capacity respectively to obtain the corresponding lower limit value. Thus, the trend tolerance intervals corresponding to the last-position groundwater flow velocity and the last-position recharge capacity are formed. Then, it is determined whether the last-position groundwater flow velocity and last-position recharge capacity are within their respective trend tolerance intervals. When the last groundwater flow velocity is within the corresponding trend tolerance interval, it is determined as the marker groundwater flow velocity. When the last groundwater flow velocity is less than the lower limit of the corresponding trend tolerance interval, the lower limit of the corresponding trend tolerance interval is determined as the marker groundwater flow velocity. When the last groundwater flow velocity is greater than the upper limit of the corresponding trend tolerance interval, the upper limit of the corresponding trend tolerance interval is determined as the marker groundwater flow velocity. Similarly, when the last recharge capacity is within the corresponding trend tolerance interval, it is determined as the marker recharge capacity. When the last recharge capacity is less than the lower limit of the corresponding trend tolerance interval, the lower limit of the corresponding trend tolerance interval is determined as the marker recharge capacity. When the last recharge capacity is greater than the upper limit of the corresponding trend tolerance interval, the upper limit of the corresponding trend tolerance interval is determined as the marker recharge capacity. Finally, the marker groundwater flow velocity and the marker recharge capacity are combined according to the same geothermal evaluation unit to construct the corresponding groundwater heat exchange capacity feature vector. The above steps are then applied to multiple groundwater flow velocity-recharge capacity sequences to construct multiple groundwater heat exchange capacity feature vectors.

[0043] Furthermore, the method provided in the application embodiment, which performs multi-scale trend overlay analysis on the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple trend overlay features, also includes:

[0044] Extreme value analysis is performed on the multiple groundwater flow velocity-recharge capacity sequences to determine multiple sets of analysis scales; multi-scale trend analysis is performed on the multiple sets of analysis scales to obtain multiple sets of trend features; trend superposition analysis is performed on the multiple sets of trend features to obtain multiple superposition features.

[0045] In this embodiment of the application, when performing extreme value analysis on multiple groundwater flow velocity-recharge capacity sequences, local extreme values ​​are first extracted from the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple local extreme value sequences; then, the interval duration is extracted by traversing the multiple local extreme value sequences to obtain multiple interval duration sets; then, the maximum value, mean value and minimum value are extracted from the multiple interval duration sets to obtain multiple sets of analysis scales.

[0046] Next, when performing multi-scale trend analysis on multiple groundwater velocity-recharge capacity sequences based on multiple sets of analytical scales, firstly, multiple groundwater velocity-recharge capacity sequences and their corresponding sets of analytical scales are read sequentially according to the correspondence of geothermal evaluation units; then, the groundwater velocity-recharge capacity sequences are segmented according to the values ​​of each analytical scale in the set of analytical scales, that is, each analytical scale value is used as the length of a time window, and data segments of corresponding lengths are extracted sequentially in chronological order from the groundwater velocity-recharge capacity sequences, thus obtaining multiple sequence segments; then, trend calculations are performed on each sequence segment. In each sequence segment, the groundwater flow velocity and recharge capacity values ​​are calculated for trend slope. The trend slope is obtained by dividing the difference between the corresponding values ​​at the beginning and end of the sequence segment by the corresponding time interval, thus obtaining the trend direction and magnitude of the sequence segment. After completing the trend calculation for all sequence segments, the trend slopes obtained at each analysis scale are combined and arranged according to the analysis scale to form a set of trend features corresponding to the trends of groundwater flow velocity and recharge capacity. Multiple groundwater flow velocity-recharge capacity sequences are processed in the same way to obtain multiple sets of trend features.

[0047] Finally, trend overlay analysis is performed on multiple trend feature sets. In this process, multiple trend feature sets are enumerated pairwise to obtain multiple enumeration combination sets. Then, adjacency correlation matrix analysis is performed on each enumeration combination, and graph convolution operations are performed between the obtained adjacency correlation matrix and the two trend features within the enumeration combination. The results are added to the corresponding multiple combined trend overlay feature sets. Finally, the multiple combined trend overlay feature sets are averaged to obtain multiple trend overlay features.

[0048] Furthermore, the method provided in the application embodiment, which performs extreme value analysis on the multiple groundwater flow velocity-recharge capacity sequences to determine multiple sets of analysis scales, also includes:

[0049] Local extreme values ​​are extracted from the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple local extreme value sequences; the interval duration is extracted by traversing the multiple local extreme value sequences to obtain multiple interval duration sets; the maximum, mean and minimum values ​​are extracted from the multiple interval duration sets to obtain multiple analysis scale sets.

[0050] In this embodiment, when extracting local extrema from multiple groundwater velocity-reinjection capacity sequences, the process begins by sequentially reading multiple groundwater velocity-reinjection capacity sequences according to the correspondence of geothermal evaluation units. Then, the groundwater velocity value, reinjection capacity value, and time node data corresponding to each time node are extracted from each sequence. Subsequently, the groundwater velocity value and reinjection capacity value are analyzed separately. The data corresponding to each time node is traversed point-by-point according to time order, and the values ​​corresponding to the current time node are compared with those of the previous and next time nodes. When the value corresponding to the current time node is greater than both the previous and next time nodes, it is determined as a local maximum. When the value corresponding to the current time node is less than both the previous and next time nodes, it is determined as a local minimum. The extracted local maximums, local minimums, and their corresponding time nodes are then arranged in chronological order to form a corresponding local extrema sequence. The same process is then applied to multiple groundwater velocity-reinjection capacity sequences to obtain multiple local extrema sequences.

[0051] Next, the interval duration is extracted by traversing multiple local extremum sequences. The time node data corresponding to two adjacent local extrema in each local extremum sequence are read in turn, and the time difference between the time node corresponding to the next local extremum and the time node corresponding to the previous local extremum is calculated. This time difference is determined as the corresponding interval duration. Then, according to the arrangement order of the local extrema in the local extremum sequence, the time difference between all adjacent local extrema is calculated one by one, and the multiple interval durations obtained are arranged according to the calculation order to form the corresponding interval duration set. The multiple local extremum sequences are processed in the same way to obtain multiple interval duration sets.

[0052] Finally, the maximum, mean, and minimum values ​​are extracted from multiple interval duration sets. All interval duration data are read sequentially from each interval duration set, and the interval durations within the same set are compared. The interval duration with the largest value is selected as the maximum value, and the interval duration with the smallest value is selected as the minimum value. Then, all interval durations in the set are summed, and the sum is divided by the number of interval durations to obtain the mean value for that interval duration set. The maximum, mean, and minimum values ​​are then combined to form a corresponding set of analytical scales. Multiple interval duration sets are processed in the same way to obtain multiple sets of analytical scales, which are used to characterize the scale of change of the corresponding groundwater flow velocity-recharge capacity sequence over different time spans.

[0053] Furthermore, in the method provided in the application embodiments, performing trend overlay analysis on the plurality of trend feature sets to obtain plurality of trend overlay features further includes:

[0054] The multiple trend feature sets are enumerated pairwise to obtain multiple enumeration combination sets; adjacency correlation matrix analysis is performed on each enumeration combination, and graph convolution operation is performed on the obtained adjacency correlation matrix and the two trend features in the enumeration combination respectively, and the operation result is added to the corresponding multiple combined trend superimposed feature sets; the multiple combined trend superimposed feature sets are subjected to set mean processing to obtain multiple trend superimposed features.

[0055] Furthermore, the method provided in the application embodiments also includes:

[0056] Calculate the set of element similarities between two trend features in each enumerated combination, and perform normalization and matrix filling to obtain the adjacency correlation matrix.

[0057] In this embodiment of the application, when enumerating multiple trend feature sets in pairs, the multiple trend feature sets are first read sequentially according to the correspondence of geothermal evaluation units, and the trend feature sets are arranged in the reading order; then, two different trend feature sets are selected sequentially from the multiple trend feature sets as a combination, and the pairing of all trend feature sets is completed in the combination order, thereby obtaining multiple enumeration combination sets, wherein each enumeration combination set contains two trend feature sets, and each trend feature set contains multiple trend features under the corresponding analysis scale.

[0058] Next, adjacency correlation matrix analysis is performed on each enumeration combination, and graph convolution operation is performed on the obtained adjacency correlation matrix and the two trend features in the enumeration combination. The results are then added to the corresponding set of multiple combined trend superposition features. In this process, for each enumerated combination, trend features are extracted sequentially from two trend feature sets according to the same analytical scale. Element similarity is calculated for each pair of trend features. During calculation, element values ​​at the same position in the two trend features are read, the absolute value of the difference between the two element values ​​is calculated, and the element similarity value at the corresponding position is calculated based on the absolute value of the difference, thus forming an element similarity set. Then, the element similarity set is normalized. First, the maximum and minimum values ​​in the element similarity set are extracted. Then, the minimum value is subtracted from each element similarity value, and the result is divided by the difference between the maximum and minimum values ​​to obtain the normalized element similarity value, thus forming the normalized element similarity set. Next, matrix filling is performed based on the normalized element similarity set. First, a square matrix of the corresponding order is built according to the number of elements in the trend features. Then, the normalized element similarity values ​​are filled into the corresponding positions in the square matrix in sequence, and the unfilled positions are... First, fill the positions with zeros to obtain the adjacency correlation matrix. Then, perform graph convolution operations on the adjacency correlation matrix and the two trend features in the enumerated combination. During the operation, multiply the adjacency correlation matrix with the first trend feature, so that each element in the adjacency correlation matrix is ​​multiplied by the corresponding element value in the first trend feature and summed to obtain the graph convolution result for the first trend feature. Then, multiply the adjacency correlation matrix with the second trend feature in the same way to obtain the graph convolution result for the second trend feature. Then, add the graph convolution results for the first and second trend features according to their element positions to obtain the combined trend superposition feature corresponding to the pair of trend features. Add the combined trend superposition feature to the combined trend superposition feature set corresponding to the enumerated combination. Repeat the above steps to traverse all trend feature pairs in each enumerated combination to obtain multiple sets of combined trend superposition features.

[0059] Finally, the average value of multiple combined trend overlay feature sets is processed. All combined trend overlay features in each set are read sequentially, maintaining the consistent element order. Then, the values ​​at the same element position within the same combined trend overlay feature set are summed, and the sum is divided by the number of combined trend overlay features in that set to obtain the average value corresponding to that element position. The average values ​​at each position are then arranged sequentially according to the element position order to form the corresponding trend overlay features. The same method is used to process the average value of multiple combined trend overlay feature sets to obtain multiple trend overlay features.

[0060] Step S600: Combine the multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities to conduct a thermal energy resource assessment and obtain regional assessment results.

[0061] In this embodiment of the application, when assessing thermal energy resources by combining multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities, the differences between the multiple theoretical heat exchange capacities and the multiple engineering heat exchange capacities are calculated to obtain multiple heat exchange capacity deviations. Then, the heat exchange capacity deviations are classified into deviation levels according to preset quantile intervals. Next, the correlation of multiple geothermal evaluation units is divided according to the deviation levels and multiple heat exchange capacity deviations to form multiple correlation groups. Then, with the multiple correlation groups as constraints, thermal energy resources are assessed by combining multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities to obtain regional assessment results.

[0062] Furthermore, the method provided in the application embodiments, which combines the multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities to conduct thermal energy resource assessment and obtain regional assessment results, also includes:

[0063] The differences between multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities are calculated to obtain multiple heat exchange capacity deviations; the deviation levels are divided according to preset quantile intervals; based on the deviation levels and multiple heat exchange capacity deviations, multiple geothermal evaluation units are divided into multiple correlation groups; using the multiple correlation groups as constraints, thermal energy resources are assessed in combination with the multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities to obtain the regional assessment results.

[0064] In this embodiment, the differences between multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities are first calculated. The theoretical heat exchange capacity and engineering heat exchange capacity of each geothermal evaluation unit are read sequentially according to the correspondence of the geothermal evaluation units. The difference between the theoretical heat exchange capacity and the engineering heat exchange capacity of the same geothermal evaluation unit is calculated. The difference between the theoretical heat exchange capacity and the engineering heat exchange capacity is obtained by subtracting the engineering heat exchange capacity from the theoretical heat exchange capacity. The absolute value of the difference is then taken to obtain the heat exchange capacity deviation corresponding to the geothermal evaluation unit. Subsequently, multiple geothermal evaluation units are processed in the same way to obtain multiple heat exchange capacity deviations. Each heat exchange capacity deviation is recorded sequentially according to the order of the geothermal evaluation units.

[0065] Next, the deviation levels are divided according to preset quantile intervals. In this process, firstly, all heat exchange capacity deviations are read and sorted in ascending order of value to obtain a sorted sequence of heat exchange capacity deviations; then, preset quantile intervals are read, where preset quantile intervals are pre-defined interval divisions used to characterize the interval position of the heat exchange capacity deviation within the overall sort; then, based on the preset quantile intervals in the sorted sequence of heat exchange capacity deviations, the corresponding interval boundary values ​​are determined, and all heat exchange capacity deviations are divided into their corresponding quantile intervals using these boundary values ​​as the dividing criteria; finally, each quantile interval is labeled with its corresponding deviation level, thus determining the deviation level corresponding to each heat exchange capacity deviation.

[0066] Subsequently, based on the deviation level and multiple heat exchange capacity deviations, the multiple geothermal evaluation units were divided into multiple related groups. In this process, the heat exchange capacity deviation and its level for each geothermal evaluation unit were read according to the correspondence between the units, and initial grouping was performed according to the deviation level, with geothermal evaluation units of the same deviation level grouped together. Then, within the same deviation level group, the heat exchange capacity deviation values ​​of each geothermal evaluation unit were compared, and geothermal evaluation units with adjacent heat exchange capacity deviation values ​​were grouped sequentially to form multiple related groups. The geothermal evaluation units contained in each related group were recorded in the order of their geothermal evaluation unit numbers.

[0067] Finally, constrained by multiple correlation groups, a thermal energy resource assessment is conducted by combining multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities. Specifically, the assessment weights of multiple correlation groups are determined based on the number of geothermal assessment units in each correlation group; then, the unit thermal energy resource assessment results of multiple geothermal assessment units are determined based on multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities; finally, the unit thermal energy resource assessment results are weighted according to multiple assessment weights and multiple correlation groups to obtain the regional assessment results.

[0068] Furthermore, in the method provided in the application embodiments, the assessment of thermal energy resources, which combines the multiple correlation groups as constraints with the multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities to obtain the regional assessment results, further includes:

[0069] Based on the number of geothermal assessment units in each correlation group, multiple assessment weights are determined for multiple correlation groups; based on the multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities, multiple unit thermal energy resource assessment results for multiple geothermal assessment units are determined; based on the multiple assessment weights and multiple correlation groups, the multiple unit thermal energy resource assessment results are weighted to obtain the regional assessment results.

[0070] In this embodiment of the application, when determining multiple evaluation weights for multiple related groups based on the number of geothermal evaluation units in each related group, the geothermal evaluation unit numbers contained in each related group are read sequentially according to the correspondence of the related groups, and the number of geothermal evaluation units in each related group is counted. At the same time, the total number of geothermal evaluation units in all related groups is counted. Then, the evaluation weight is calculated for each related group. The number of geothermal evaluation units in the related group is divided by the total number of all geothermal evaluation units to obtain the evaluation weight corresponding to the related group. The same calculation method is used to calculate multiple related groups to obtain multiple evaluation weights corresponding to multiple related groups.

[0071] Next, based on multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities, the unit thermal energy resource assessment results for multiple geothermal evaluation units are determined. In this process, the theoretical heat exchange capacity and engineering heat exchange capacity corresponding to each geothermal evaluation unit are read sequentially according to their correspondence, and the theoretical and engineering heat exchange capacities of the same geothermal evaluation unit are combined for calculation. During the calculation, the theoretical and engineering heat exchange capacities are first summed, and then the sum is divided by two to obtain the unit thermal energy resource assessment result corresponding to that geothermal evaluation unit. Subsequently, multiple geothermal evaluation units are processed separately using the same calculation method to obtain multiple unit thermal energy resource assessment results.

[0072] Finally, the assessment results of multiple unit thermal energy resources are weighted according to multiple assessment weights and multiple correlation groups. In this process, the geothermal assessment units contained in each correlation group are read according to the correspondence of the correlation groups, and the corresponding unit thermal energy resource assessment results are extracted; then, the unit thermal energy resource assessment results corresponding to each geothermal assessment unit in the same correlation group are summed, and the summation result is divided by the number of geothermal assessment units in the correlation group to obtain the group thermal energy resource assessment result corresponding to the correlation group; then, the group thermal energy resource assessment results corresponding to each correlation group are multiplied by the corresponding assessment weight, and all product results are summed to obtain the regional assessment result.

[0073] In summary, the embodiments of this application have at least the following technical effects:

[0074] This application acquires borehole formation data, geotechnical thermal property data, groundwater dynamic data, and building heating and cooling load data for urban areas to construct a basic dataset. Based on this basic dataset, spatial grid division is performed on the urban area to construct multiple geothermal evaluation units. Multiple thermal response test wells are deployed within these geothermal evaluation units, and cyclic heat injection tests are conducted on these wells to continuously collect temperature change data within the wells, constructing multiple formation temperature response sequences. The time series slope and temperature increment are calculated by traversing these multiple formation temperature response sequences to construct multiple temperature response sequences. The invention generates feature vectors and performs formation thermal conductivity inversion to obtain multiple formation thermal conductivity parameters. Based on these parameters, along with the borehole depth and well spacing in the basic dataset, it calculates multiple theoretical heat transfer capacities for multiple geothermal evaluation units. Using groundwater dynamic data, it calculates groundwater flow velocity and reinjection capacity for each geothermal evaluation unit, constructing multiple groundwater heat transfer capacity feature vectors. It then performs engineering heat transfer analysis to obtain multiple engineering heat transfer capacities. Finally, it combines these theoretical and engineering heat transfer capacities to conduct a thermal energy resource assessment, obtaining regional assessment results. This invention addresses the technical problem of insufficient accuracy in assessing shallow geothermal energy resources in urban areas in existing technologies. By combining theoretical and engineering heat transfer capacities for comprehensive assessment, it improves the accuracy of shallow geothermal energy resource assessment in urban areas.

[0075] Example 2, based on the same inventive concept as the method for assessing shallow geothermal energy resources in urban areas described in the foregoing examples, such as... Figure 2 As shown, this application provides a system for assessing shallow geothermal energy resources in urban areas. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0076] The grid partitioning module 11 is used to acquire borehole formation data, soil and rock thermal property data, groundwater dynamic data, and building heating and cooling load data of the urban area, construct a basic dataset, and perform spatial grid partitioning on the urban area based on the basic dataset to construct multiple geothermal evaluation units; the heat injection testing module 12 is used to deploy multiple thermal response test wells in the multiple geothermal evaluation units, perform cyclic heat injection tests on the multiple thermal response test wells, continuously collect temperature change data in the wells, and construct multiple formation temperature response sequences; the inversion module 13 is used to traverse the multiple formation temperature response sequences to calculate the time series slope and temperature increment, and construct multiple temperature response feature vectors. The system performs formation thermal conductivity inversion to obtain multiple formation thermal conductivity parameters; the heat transfer capacity calculation module 14 is used to calculate multiple theoretical heat transfer capacities of multiple geothermal evaluation units based on the multiple formation thermal conductivity parameters and the borehole depth and well spacing in the basic data; the heat transfer analysis module 15 is used to perform groundwater flow velocity calculation and reinjection capacity calculation for the multiple geothermal evaluation units based on the groundwater dynamic data, construct multiple groundwater heat transfer capacity feature vectors, perform engineering heat transfer analysis, and obtain multiple engineering heat transfer capacities; the resource assessment module 16 is used to perform thermal energy resource assessment by combining the multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities to obtain regional assessment results.

[0077] Furthermore, the system is also used to implement the following functions:

[0078] Temperature acquisition devices were deployed in multiple thermal response test wells, and circulating fluid with constant thermal power was injected into the corresponding buried pipe heat exchange circuit. During the test, fluid temperature data in the wells were collected at preset time intervals to obtain multiple formation temperature response sequences.

[0079] Furthermore, the system is also used to implement the following functions:

[0080] Based on the groundwater dynamic data, the groundwater flow velocity and recharge capacity of the multiple geothermal evaluation units are analyzed at multiple time nodes to obtain multiple groundwater flow velocity-recharge capacity sequences; multi-scale trend overlay analysis is performed on the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple trend overlay features; using the multiple trend overlay features, trend tolerance intervals are identified for multiple last-position groundwater flow velocity-recharge capacity sequences to construct the multiple groundwater heat exchange capacity feature vectors.

[0081] Furthermore, the system is also used to implement the following functions:

[0082] Extreme value analysis is performed on the multiple groundwater flow velocity-recharge capacity sequences to determine multiple sets of analysis scales; multi-scale trend analysis is performed on the multiple sets of analysis scales to obtain multiple sets of trend features; trend superposition analysis is performed on the multiple sets of trend features to obtain multiple superposition features.

[0083] Furthermore, the system is also used to implement the following functions:

[0084] Local extreme values ​​are extracted from the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple local extreme value sequences; the interval duration is extracted by traversing the multiple local extreme value sequences to obtain multiple interval duration sets; the maximum, mean and minimum values ​​are extracted from the multiple interval duration sets to obtain multiple analysis scale sets.

[0085] Furthermore, the system is also used to implement the following functions:

[0086] The multiple trend feature sets are enumerated pairwise to obtain multiple enumeration combination sets; adjacency correlation matrix analysis is performed on each enumeration combination, and graph convolution operation is performed on the obtained adjacency correlation matrix and the two trend features in the enumeration combination respectively, and the operation result is added to the corresponding multiple combined trend superimposed feature sets; the multiple combined trend superimposed feature sets are subjected to set mean processing to obtain multiple trend superimposed features.

[0087] Furthermore, the system is also used to implement the following functions:

[0088] Calculate the set of element similarities between two trend features in each enumerated combination, and perform normalization and matrix filling to obtain the adjacency correlation matrix.

[0089] Furthermore, the system is also used to implement the following functions:

[0090] The differences between multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities are calculated to obtain multiple heat exchange capacity deviations; the deviation levels are divided according to preset quantile intervals; based on the deviation levels and multiple heat exchange capacity deviations, multiple geothermal evaluation units are divided into multiple correlation groups; using the multiple correlation groups as constraints, thermal energy resources are assessed in combination with the multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities to obtain the regional assessment results.

[0091] Furthermore, the system is also used to implement the following functions:

[0092] Based on the number of geothermal assessment units in each correlation group, multiple assessment weights are determined for multiple correlation groups; based on the multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities, multiple unit thermal energy resource assessment results for multiple geothermal assessment units are determined; based on the multiple assessment weights and multiple correlation groups, the multiple unit thermal energy resource assessment results are weighted to obtain the regional assessment results.

[0093] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for assessing shallow geothermal energy resources in urban areas, characterized in that, The method includes: Obtain borehole stratigraphic data, soil and rock thermal property data, groundwater dynamic data, and building heating and cooling load data for urban areas, construct a basic dataset, and perform spatial grid division on the urban area based on the basic dataset to construct multiple geothermal evaluation units; Multiple thermal response test wells are deployed in the multiple geothermal evaluation units. Cyclic heat injection tests are conducted on the multiple thermal response test wells to continuously collect temperature change data in the wells and construct multiple formation temperature response sequences. The time series slope and temperature increment are calculated by traversing the multiple formation temperature response sequences, multiple temperature response feature vectors are constructed, and formation thermal conductivity is inverted to obtain multiple formation thermal conductivity parameters. Based on the multiple formation thermal conductivity parameters, as well as the borehole depth and well spacing in the basic data, the theoretical heat transfer capacity of multiple geothermal evaluation units is calculated. Based on the groundwater dynamic data, groundwater flow velocity and reinjection capacity are calculated for the multiple geothermal evaluation units, multiple groundwater heat exchange capacity feature vectors are constructed, engineering heat exchange analysis is performed, and multiple engineering heat exchange capacities are obtained. By combining the aforementioned theoretical heat exchange capacities and multiple engineering heat exchange capacities, a regional assessment of thermal energy resources is obtained.

2. The method for assessing shallow geothermal energy resources in urban areas as described in claim 1, characterized in that, Multiple thermal response test wells are deployed in the multiple geothermal evaluation units. Cyclic heat injection tests are performed on these wells to continuously collect temperature change data within the wells, constructing multiple formation temperature response sequences, including: Temperature acquisition devices were installed in multiple thermal response test wells, and circulating fluid with constant thermal power was injected into the corresponding buried pipe heat exchange circuit. During the test, fluid temperature data in the well were collected at preset time intervals to obtain multiple formation temperature response sequences.

3. The method for assessing shallow geothermal energy resources in urban areas as described in claim 1, characterized in that, Based on the aforementioned groundwater dynamic data, groundwater flow velocity and reinjection capacity calculations are performed on the multiple geothermal evaluation units to construct multiple groundwater heat exchange capacity feature vectors, including: Based on the groundwater dynamic data, the groundwater flow velocity and recharge capacity of the multiple geothermal evaluation units are analyzed at multiple time nodes to obtain multiple groundwater flow velocity-recharge capacity sequences. Multi-scale trend overlay analysis was performed on the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple trend overlay features; By utilizing the multiple trend superposition features, trend tolerance intervals are identified for multiple last-position groundwater flow velocity-recharge capacity in the multiple groundwater flow velocity-recharge capacity sequences, and feature vectors of the multiple groundwater heat exchange capacity are constructed.

4. The method for assessing shallow geothermal energy resources in urban areas as described in claim 3, characterized in that, Multi-scale trend overlay analysis was performed on the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple trend overlay features, including: Extreme value analysis was performed on the multiple groundwater flow velocity-recharge capacity sequences to determine multiple sets of analytical scales; Based on the multiple sets of analytical scales, multi-scale trend analysis is performed on the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple sets of trend features; A trend overlay analysis is performed on the multiple trend feature sets to obtain multiple trend overlay features.

5. The method for assessing shallow geothermal energy resources in urban areas as described in claim 4, characterized in that, Extreme value analysis was performed on the multiple groundwater flow velocity-recharge capacity sequences to determine multiple sets of analytical scales, including: Local extreme values ​​were extracted from the multiple groundwater flow velocity-recharge capacity sequences to obtain multiple local extreme value sequences. The interval duration is extracted by traversing the multiple local extremum sequences to obtain multiple interval duration sets; The maximum, mean, and minimum values ​​are extracted from the multiple interval duration sets to obtain multiple sets of analysis scales.

6. The method for assessing shallow geothermal energy resources in urban areas as described in claim 4, characterized in that, Perform trend overlay analysis on the multiple trend feature sets to obtain multiple trend overlay features, including: The multiple trend feature sets are enumerated pairwise to obtain multiple enumeration combination sets; For each enumeration combination, an adjacency correlation matrix analysis is performed, and the obtained adjacency correlation matrix is ​​convolved with the two trend features within the enumeration combination. The results are then added to the corresponding set of multiple combined trend superposition features. The multiple combined trend superposition feature sets are subjected to set mean processing to obtain multiple trend superposition features.

7. The method for assessing shallow geothermal energy resources in urban areas as described in claim 6, characterized in that, Calculate the set of element similarities between two trend features in each enumerated combination, and perform normalization and matrix filling to obtain the adjacency correlation matrix.

8. The method for assessing shallow geothermal energy resources in urban areas as described in claim 1, characterized in that, A combined assessment of multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities was conducted to obtain regional assessment results, including: The differences between multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities are calculated separately to obtain multiple heat transfer capacity deviations; Deviation levels are divided according to preset quantile intervals; Based on the deviation level and multiple heat exchange capacity deviations, multiple geothermal evaluation units are divided into multiple correlation groups; Using the aforementioned multiple correlation groups as constraints, and combining the aforementioned multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities, thermal energy resources are assessed to obtain the regional assessment results.

9. The method for assessing shallow geothermal energy resources in urban areas as described in claim 8, characterized in that, Using the aforementioned multiple correlation groupings as constraints, and combining the aforementioned multiple theoretical heat transfer capacities and multiple engineering heat transfer capacities, a thermal energy resource assessment is conducted to obtain the regional assessment results, including: Based on the number of geothermal assessment units in each correlation group, determine multiple assessment weights for multiple correlation groups; Based on the aforementioned theoretical heat exchange capacity and multiple engineering heat exchange capacity, the assessment results of multiple unit thermal energy resources for multiple geothermal evaluation units are determined. Based on the multiple assessment weights and multiple correlation groups, the assessment results of the multiple unit thermal energy resources are weighted to obtain the regional assessment results.

10. A system for assessing shallow geothermal energy resources in urban areas, characterized in that, The system is used to execute a method for assessing shallow geothermal energy resources in urban areas as described in any one of claims 1-9, the system comprising: The grid division module is used to acquire borehole strata data, soil and rock thermal property data, groundwater dynamic data, and building heating and cooling load data of urban areas, construct a basic dataset, and perform spatial grid division on the urban area based on the basic dataset to construct multiple geothermal evaluation units. The heat injection test module is used to deploy multiple thermal response test wells in the multiple geothermal evaluation units, perform cyclic heat injection tests on the multiple thermal response test wells, continuously collect temperature change data in the wells, and construct multiple formation temperature response sequences. The inversion module is used to traverse the multiple formation temperature response sequences to calculate the time series slope and temperature increment, construct multiple temperature response feature vectors, and perform formation thermal conductivity inversion to obtain multiple formation thermal conductivity parameters. The heat transfer capacity calculation module is used to calculate multiple theoretical heat transfer capacities of multiple geothermal evaluation units based on the multiple formation thermal conductivity parameters and the borehole depth and well spacing of the basic data. The heat exchange analysis module is used to perform groundwater flow velocity calculation and reinjection capacity calculation for the multiple geothermal evaluation units based on the groundwater dynamic data, construct multiple groundwater heat exchange capacity feature vectors, perform engineering heat exchange analysis, and obtain multiple engineering heat exchange capacities. The resource assessment module is used to combine multiple theoretical heat exchange capacities and multiple engineering heat exchange capacities to conduct thermal energy resource assessment and obtain regional assessment results.

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