Geothermal resource favorable area prediction method and system

Abstract

The invention relates to the technical field, in particular to a geothermal resource favorable area prediction method and system, and the method comprises the following steps: collecting electromagnetic and seismic wave data of a plurality of positions of a target area through a sensor based on regional position data, carrying out the correlation of the data and position information, and generating a geological exploration data set through the combination of data cleaning and standardization. According to the method, electromagnetic and seismic wave data of multiple positions are collected and associated with geographic position information, the practicability and reliability of the data are improved, the data quality is optimized in combination with data filtering, the accuracy of stratum structure prediction is improved, a three-dimensional geologic model is constructed and combined with actual drilling data, and the practicability and accuracy of the model are verified; the method improves the accuracy and credibility of model prediction, optimizes the precision and practicability of resource prediction by analyzing the correlation between a geologic structure and geothermal resources, provides scientific decision support for geothermal development, and supports the effective utilization of renewable energy sources and the realization of a sustainable development target.

Description

Technical Field

[0001] The present invention relates to the field of geological engineering technology, and in particular to a method and system for predicting favorable areas of geothermal resources. Background Art

[0002] The field of geological engineering technology focuses on the study of soil, rock and their interactions, including geological surveys, engineering geology, environmental geology and resource exploration. It uses geological surveys, geophysical exploration and geochemical testing to study the distribution and characteristics of underground resources, including seismic wave analysis, gravity field measurement, water chemical analysis, and mineral composition analysis. It conducts mineral resource exploration, infrastructure construction safety assessment, and geological disaster monitoring and prevention. Through the systematic analysis of underground thermal reservoirs, groundwater and mineral deposits, geological engineering provides the necessary technical support and theoretical basis for the development of renewable energy.

[0003] Among them, the geothermal resource favorable area prediction method uses geological, geophysical and geochemical data to identify geothermal resource areas with development potential through modeling and analysis, provide scientific guidance for geothermal energy development, and help engineers and scientific researchers accurately locate the optimal mining area of ​​geothermal resources. Through heat flow analysis, formation characteristic assessment, and underground temperature distribution model establishment, the geothermal resource potential of different regions is evaluated, which effectively reduces development costs, reduces environmental impacts, and improves the development efficiency of geothermal energy, promotes the effective use of renewable energy, and achieves sustainable development goals.

[0004] Traditional geothermal resource favorable area prediction technology has deficiencies in the accuracy of geothermal resource prediction and data processing efficiency. It is inefficient in processing large-scale data and it is difficult to achieve high-precision spatial data integration, which limits the accuracy and reliability of geological models. Although gravity field measurement and hydrochemical analysis methods can provide useful underground information, the data are collected in isolation and fail to be effectively integrated, resulting in difficulty in making comprehensive and accurate predictions of the distribution of geothermal resources in practical applications. The ability to dynamically monitor geothermal activity is also relatively limited, relying on intermittent measurements and subsequent data analysis, and lacking the ability to monitor and update data in real time, which limits and slows down the response speed to changes in geothermal resource potential, and cannot identify the correlation between geological structure and geothermal resource distribution location, resulting in geothermal energy development projects facing greater uncertainty in selecting the optimal location and planning development strategies, limiting the effective development of geothermal resources and maximizing the use of renewable energy. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for predicting favorable areas of geothermal resources.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a method for predicting favorable areas of geothermal resources, comprising the following steps: S1: Based on the regional location data, sensors are used to collect electromagnetic and seismic wave data at multiple locations in the target area, and the data and location information are associated, combined with data cleaning and standardization to generate a geological exploration data set; S2: Based on the geological exploration data set, using data filtering to eliminate environmental noise and irrelevant interference, combining data analysis to identify geological data features, identify data features of multiple stratigraphic structures, and generate stratigraphic structure identification results; S3: using the stratigraphic structure identification results, constructing a three-dimensional geological model according to the spatial distribution characteristics of the data, and combining the actual drilling data to verify the accuracy and reliability of the model, and generating a geological model construction result; S4: Based on the geological model construction results, using geothermal probes and surface temperature sensors, real-time acquisition of formation temperature data at multiple locations in the target area, calculation of geothermal gradients, and analysis of geothermal activity change trends to generate activity change analysis results; S5: Based on the activity change analysis results, combined with the geological model, by analyzing the impact of various geological structure characteristics on geothermal resources, identifying regional characteristics rich in geothermal resources, and generating a geothermal resource distribution heat map; S6: Utilize the geothermal resource distribution heat map to predict geothermal resource hotspots, conduct a quantitative assessment of geothermal potential in combination with the geological characteristics of the target location, analyze the economic feasibility of the prediction results, and generate favorable area prediction results.

[0007] As a further solution of the present invention, the geological exploration data set includes electromagnetic wave intensity data, seismic wave velocity data, and geographic coordinate information; the stratigraphic structure identification results include rock hardness characteristic information, soil moisture content information, and void distribution density data; the geological model construction results include a three-dimensional stratigraphic continuity model, geological fault locations, and groundwater flow direction maps; the activity change analysis results include stratigraphic temperature time series information, geothermal gradient change maps, and abnormal temperature activity areas; the geothermal resource distribution thermal map includes high geothermal potential area identification information, temperature distribution uniformity ratings, and key geothermal activity indicators; the favorable area prediction results include geothermal resource development cost estimation data, expected geothermal output, and geothermal development environmental impact analysis results.

[0008] As a further solution of the present invention, based on the regional location data, sensors are used to collect electromagnetic and seismic wave data at multiple locations in the target area, and the data and location information are associated, combined with data cleaning and standardization, to generate a geological exploration data set in the following steps: S101: Based on the regional location data, using seismic wave and electromagnetic wave sensors, collect geological data of multiple locations in the target area in real time, record geographical location information, and generate geological data collection records; S102: Based on the geological data acquisition records, analyze and identify errors and outliers in the geological data, correct geographic coordinate deviations and erroneous readings, optimize data quality, and generate geological data processing results; S103: Based on the geological data processing result, the geological data is formatted and standardized, including unifying the timestamp format and data scale, to generate a geological exploration data set.

[0009] As a further solution of the present invention, based on the geological exploration data set, data filtering is used to eliminate environmental noise and irrelevant interference, and data analysis is combined to identify geological data features, identify data features of multiple stratigraphic structures, and generate stratigraphic structure identification results in the following steps: S201: Based on the geological exploration data set, frequency analysis is performed on the exploration data to identify and remove noise caused by environmental factors, including electromagnetic interference and microseismic activity noise, to generate denoised geological data; S202: Based on the de-noised geological data, feature analysis is performed on the geological data to identify data features of various rock densities, soil moisture, and void distribution, and generate stratigraphic feature data; S203: Classify the stratigraphic structure according to the stratigraphic characteristic data, identify the stratigraphic structures at multiple locations in the target area, including rocks, soil, and voids, and generate stratigraphic structure identification results.

[0010] As a further solution of the present invention, the three-dimensional geological model is constructed by using the stratigraphic structure identification result according to the spatial distribution characteristics of the data, and the accuracy and reliability of the model are verified in combination with the actual drilling data. The steps of generating the geological model construction result are specifically as follows: S301: Based on the stratigraphic structure identification result and according to the geographical location information, the geological data is spatially aligned, and the stratigraphic data is dimensionally adjusted to match the actual physical scale to generate a spatially aligned data set; S302: Based on the spatially aligned data set, the connectivity and continuity between the strata are evaluated by analyzing the physical and chemical correlations between the multiple strata data, and an inter-strata characteristic analysis result is generated; S303: Using the interlayer characteristic analysis results, construct a three-dimensional geological model, combine the field drilling data, evaluate the accuracy and reliability of the model, and generate a geological model construction result.

[0011] As a further solution of the present invention, based on the geological model construction result, using geothermal probes and surface temperature sensors, the formation temperature data of multiple locations in the target area are collected in real time, the geothermal gradient is calculated, and the change trend of geothermal activity is analyzed. The steps of generating activity change analysis results are specifically as follows: S401: Based on the geological model construction result, using a geothermal probe and a surface temperature sensor, real-time acquisition of formation temperature data at multiple locations in the target area is performed to generate a real-time temperature data set; S402: Based on the real-time temperature data set, fill in the data gaps at multiple locations using data interpolation to optimize the continuity of the data, calculate the variation range of the formation temperature and the geothermal gradient, and generate a geothermal gradient calculation result; S403: Based on the geothermal gradient calculation result, analyze the time variation characteristics of the temperature data, identify the pattern and cycle of geothermal activity, and generate activity variation analysis results.

[0012] As a further solution of the present invention, based on the activity change analysis results, combined with the geological model, by analyzing the impact of various geological structure characteristics on geothermal resources, identifying regional characteristics rich in geothermal resources, and generating a geothermal resource distribution heat map, the specific steps are: S501: Based on the activity change analysis results, spatial distribution analysis is performed on the formation temperature data to identify the geothermal activity concentration area and generate a geothermal activity area data set; S502: using the geothermal activity area dataset and combining it with the geological model, analyzing geological structural characteristics, including thermal conductivity and fracture distribution of rock formations, calculating correlations between various geological characteristics and geothermal activity, and generating a key geological characteristic dataset; S503: Analyze and predict the spatial distribution of geothermal data using the key geological feature data set, identify areas rich in geothermal resources, and generate a geothermal resource distribution heat map.

[0013] As a further solution of the present invention, the specific formula for calculating the correlation between various geological features and geothermal activity is:

[0014] in, represents the correlation coefficient between geological characteristics and geothermal activity, Representative The geological characteristic observation value of each data point, represents the average value of all geological feature observations, Representative Observed value of geothermal activity intensity at data points, represents the average value of all geothermal activity intensity observations, is the index of each data point in the dataset.

[0015] As a further solution of the present invention, the geothermal resource distribution heat map is used to predict the geothermal resource hotspot area, and the geothermal potential is quantitatively evaluated in combination with the geological characteristics of the target location, and the economic feasibility of the prediction results is analyzed. The steps of generating the prediction results of the favorable area are specifically as follows: S601: Based on the geothermal resource distribution heat map, according to the identified geothermal resource hotspot areas, the geographical locations and geological feature information of multiple hotspots are recorded to generate hotspot positioning data; S602: Based on the hotspot location data, considering geological stability and infrastructure status, evaluating the geothermal development potential of multiple hotspot locations, and generating development potential analysis data; S603: Utilizing the development potential analysis data, analyzing and predicting the development costs, outputs and environmental impacts of multiple hotspot locations, evaluating economic feasibility, and generating favorable area prediction results.

[0016] A geothermal resource favorable area prediction system, the geothermal resource favorable area prediction system is used to execute the above-mentioned geothermal resource favorable area prediction method, the system comprises: The data acquisition and cleaning module uses sensors to collect electromagnetic and seismic wave data of the target area in real time based on regional location data, records the location information of multiple data points, and performs data cleaning and standardization. It combines data filtering to eliminate environmental noise and irrelevant interference, and identifies data features of various stratigraphic structures to generate feature data recognition results. The stratigraphic analysis and modeling module analyzes the correlation between data layers based on the feature data identification results through spatial alignment and scale conversion of the data, constructs a three-dimensional geological model, verifies the accuracy of the model in combination with field drilling data, and generates model verification results; The geothermal gradient analysis module uses geothermal probes and surface temperature sensors based on the model verification results to collect formation temperature data in real time and calculate geothermal gradients, analyze the changing trend of geothermal activity, and generate geothermal data collection records; The structural impact analysis module analyzes the impact of geological structural characteristics on geothermal resources based on the geothermal data collection records, identifies regional characteristics rich in geothermal resources, and generates a geothermal resource characteristic heat map; The resource assessment and prediction module uses the geothermal resource characteristic heat map to predict geothermal resource hot spots, conducts quantitative assessment of geothermal potential in combination with geological characteristics, considers output, development costs and environmental impacts, analyzes the economic feasibility of the prediction results, and generates favorable area prediction results.

[0017] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, electromagnetic and seismic wave data from multiple locations are collected and associated with geographic location information to improve the practicability and reliability of the data, and data filtering is combined to optimize data quality, thereby improving the accuracy of stratigraphic structure prediction, and constructing a three-dimensional geological model and combining it with actual drilling data to verify the practicability and accuracy of the model, thereby improving the accuracy and credibility of the model prediction, and by analyzing the correlation between geological structure and geothermal resources, optimizing the accuracy and practicability of resource prediction, providing scientific decision-making support for geothermal development, and supporting the effective use of renewable energy and the realization of sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flow chart of S1 of the present invention; Figure 3 This is a detailed flow chart of S2 of the present invention; Figure 4 This is a detailed flow chart of S3 of the present invention; Figure 5 This is a detailed flow chart of S4 of the present invention; Figure 6 This is a detailed flow chart of S5 of the present invention; Figure 7 This is a detailed flow chart of S6 of the present invention; Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0021] See also Figure 1 The present invention provides a technical solution, a method for predicting favorable areas of geothermal resources, comprising the following steps: S1: Based on the regional location data, sensors are used to collect electromagnetic and seismic wave data at multiple locations in the target area, and the data and location information are associated, combined with data cleaning and standardization to generate a geological exploration data set; S2: Based on the geological exploration data set, data filtering is used to eliminate environmental noise and irrelevant interference, and data analysis is combined to identify geological data features, identify data features of various stratigraphic structures, and generate stratigraphic structure identification results; S3: Using the stratigraphic structure identification results, according to the spatial distribution characteristics of the data, a three-dimensional geological model is constructed, and combined with actual drilling data, the accuracy and reliability of the model are verified to generate the geological model construction results; S4: Based on the results of geological model construction, using geothermal probes and surface temperature sensors, collect formation temperature data at multiple locations in the target area in real time, calculate geothermal gradients, analyze the changing trends of geothermal activities, and generate activity change analysis results; S5: Based on the results of activity change analysis and combined with geological models, the influence of various geological structural characteristics on geothermal resources is analyzed to identify the regional characteristics rich in geothermal resources and generate a geothermal resource distribution heat map; S6: Use geothermal resource distribution heat maps to predict geothermal resource hotspots, combine the geological characteristics of the target location to conduct a quantitative assessment of geothermal potential, analyze the economic feasibility of the prediction results, and generate favorable area prediction results.

[0022] The geological exploration data set includes electromagnetic wave intensity data, seismic wave velocity data, and geographic coordinate information. The stratigraphic structure identification results include rock hardness characteristic information, soil moisture content information, and void distribution density data. The geological model construction results include three-dimensional stratigraphic continuity model, geological fault location, and groundwater flow direction map. The activity change analysis results include stratigraphic temperature time series information, geothermal gradient change map, and abnormal temperature activity areas. The geothermal resource distribution thermal map includes high geothermal potential area identification information, temperature distribution uniformity rating, and key geothermal activity indicators. The favorable area prediction results include geothermal resource development cost estimation data, expected geothermal output, and geothermal development environmental impact analysis results.

[0023] See also Figure 2 Based on the regional location data, sensors are used to collect electromagnetic and seismic wave data at multiple locations in the target area, and the data and location information are associated. Combined with data cleaning and standardization, the steps to generate a geological exploration data set are as follows: S101: Based on the regional location data, using seismic wave and electromagnetic wave sensors, collect geological data of multiple locations in the target area in real time, record geographical location information, and generate geological data collection records; In sub-step S101, based on regional location data, through seismic wave and electromagnetic wave sensors, the geographic information system combined with the global positioning system is used to track multiple locations in the target area, and geological data, including soil composition, rock structure and various underground characteristics, are collected in real time. By deploying sensors in key geological faults and active areas, the comprehensiveness and representativeness of the data are ensured. The data collection system uses a clock synchronization mechanism to ensure the consistency of timestamps and realize synchronous data collection. The location data of the sensor is recorded through the GPS system and stored together with the collected geological data to form a geological data collection record. Combined with data cleaning, obvious noise introduced by sensor failure or external interference is eliminated. The geological data is stored in the central database for subsequent analysis.

[0024] S102: Based on the geological data acquisition records, analyze and identify errors and outliers in the geological data, correct geographic coordinate deviations and erroneous readings, optimize data quality, and generate geological data processing results; In sub-step S102, based on the geological data acquisition records collected by seismic wave and electromagnetic wave sensors, errors and outliers in the geological data are analyzed and identified, using the formula , calculate the data points Whether it is an outlier; In the formula, represents the standard score, represents the observed value, represents the average value, represents standard deviation; Detailed explanation of the formula and the process of formula calculation and derivation: Assume that a geological feature is measured unit, unit, Unit, calculation :

[0025] The results show that the observed value of 200 units The score is 2.5, Scores exceeding ±2 are considered outliers. The calculation process is used to identify potential errors or anomalies in the data, ensure that geographic coordinate deviations and erroneous readings are effectively corrected, optimize data quality, improve the overall reliability and accuracy of the data set, and provide a clean and reliable data foundation for data processing and analysis.

[0026] S103: Based on the geological data processing results, format and standardize the geological data, including unifying the timestamp format and data scale, to generate a geological exploration data set; In sub-step S103, based on the optimized geological data processing results, data formatting and standardization measures are taken to ensure the consistency and interoperability of the data set. The formatting process includes unifying the timestamp format of the data, using the international standard time ISO8601 format to facilitate the time series analysis of the data, standardizing the data scale, using the normalization method to convert the data value into a value between 0 and 1, eliminating the scale difference caused by different measuring equipment, applying the statistical analysis method principal component analysis, reducing the data dimension, extracting key features, and enhancing the efficiency of data processing. The generated geological exploration data set is encoded and compressed, and the data set will be used for geological structure prediction and resource assessment.

[0027] See also Figure 3 Based on the geological exploration data set, data filtering is used to eliminate environmental noise and irrelevant interference, and data analysis is combined to identify geological data features, identify data features of various stratigraphic structures, and generate stratigraphic structure identification results in the following steps: S201: Based on the geological exploration data set, frequency analysis is performed on the exploration data to identify and remove noise caused by environmental factors, including electromagnetic interference and microseismic activity noise, and generate denoised geological data; In sub-step S201, based on the geological exploration data set, fast Fourier transform is used for frequency analysis to identify and separate the noise caused by electromagnetic interference and microseismic activity in the data, and the data is converted to the frequency domain, where the interference usually manifests as abnormal peaks in the frequency. The peaks are identified by setting thresholds, and bandpass filters are applied to remove the noise in the target frequency range and clean the data. The denoising data has significant improvements in clarity and quality, providing an accurate data basis for geological analysis, generating denoised geological data for subsequent feature analysis and identification of stratigraphic structures.

[0028] S202: Based on the de-noised geological data, feature analysis is performed on the geological data to identify data features of various rock densities, soil moisture, and void distribution, and generate stratigraphic feature data; In sub-step S202, based on the denoised geological data, statistical analysis methods and machine learning techniques such as support vector machines and random forests are used to perform feature analysis of rock density, soil moisture and void distribution. The data is normalized during the analysis process to ensure fairness and effectiveness of comparisons of different parameters. SVM is used to classify and identify data features to distinguish the physical properties of different strata. The random forest algorithm is used to evaluate and verify the importance of features to ensure the accuracy and reliability of the analysis results. Stratigraphic characteristic data are generated, which record the specific attributes of multiple types of geological structures and provide a scientific basis for subsequent stratigraphic classification and resource assessment.

[0029] S203: Classifying the stratigraphic structure according to the stratigraphic characteristic data, identifying stratigraphic structures at multiple locations in the target area, including rocks, soil, and voids, and generating stratigraphic structure identification results; In sub-step S203, based on the stratigraphic characteristic data, the cluster analysis method K-means algorithm is used to classify the stratigraphic structure, and the stratigraphic structure of multiple locations in the target area is identified, including rocks, soil and voids. Cluster analysis calculates the distance between each data point, automatically classifies the data according to the physical and chemical properties of the strata, identifies stratigraphic types with similar characteristics, and generates stratigraphic structure identification results, providing data support for geological exploration and resource development, enhancing the predictive ability of geological models, and optimizing resource exploration strategies.

[0030] See also Figure 4 , using the stratigraphic structure identification results, according to the spatial distribution characteristics of the data, a three-dimensional geological model is constructed, and combined with actual drilling data, the accuracy and reliability of the model are verified. The specific steps for generating the geological model construction results are as follows: S301: Based on the stratigraphic structure recognition result and the geographical location information, the geological data is spatially aligned, and the stratigraphic data is dimensionally adjusted to match the actual physical scale to generate a spatially aligned data set; In sub-step S301, based on the stratigraphic structure identification results, the geological data are spatially aligned using geographic information system tools and coordinate transformation algorithms. The coordinate reference system in the GIS software is used to convert geological data from different sources into a unified spatial reference frame to ensure the consistency of the data in geographic space. Linear transformation and scale adjustment techniques are used to adjust the dimensions of the stratigraphic data to match the actual physical scale of the geological data, ensuring the precise spatial alignment of the data so that subsequent analysis can be performed based on the accurate geographic location. The generated spatially aligned data set is used to analyze the physical connection between strata and resource distribution, providing reliable decision support for resource development and environmental protection.

[0031] S302: Based on the spatially aligned data set, the connectivity and continuity between the strata are evaluated by analyzing the physical and chemical correlations between the multiple strata data, and an inter-strata characteristic analysis result is generated; In sub-step S302, based on the spatially aligned data set, correlation analysis and statistical modeling methods, including Pearson correlation coefficient and principal component analysis, are used to evaluate the correlation between physical and chemical properties between different strata, reveal the connectivity and continuity between strata, identify continuous layers and fault layers of geological structures, including calculating the correlation coefficients between the content of each chemical element and physical properties in the stratigraphic data, and identifying strata that influence each other. Principal component analysis helps identify key features in multidimensional data and analyze the intrinsic connections between stratigraphic data. The generated inter-stratum characteristic analysis results provide a scientific basis for the construction of geological models and support the precise implementation of resource assessment and environmental monitoring.

[0032] S303: construct a three-dimensional geological model using the interlayer characteristic analysis results, and evaluate the accuracy and reliability of the model in combination with field drilling data to generate a geological model construction result; In sub-step S303, the three-dimensional geological model is constructed using the interlayer characteristic analysis results, and the accuracy and reliability of the model are evaluated in combination with field drilling data. The error estimation formula is applied ,in represents the model error; In the formula, is the model error, is the model prediction value, is the actual drilling measurement, is the total number of data points; Detailed explanation of the formula and the process of formula calculation and derivation: Assume that the measured values ​​are [145,150,150,150,150,150,150,150,150,150] meters, and the corresponding predicted values ​​are [150,150,150,150,150,150,150,150,150,150] meters. Calculate :

[0033] The accuracy of the three-dimensional geological model is evaluated by calculating the root mean square error between the predicted value and the measured value, the matching degree between the geological model and the actual stratigraphic data is quantitatively analyzed, the reliability and practicality of the model are verified, and it is ensured that the model can reflect the actual geological conditions and provide scientific decision-making support.

[0034] See also Figure 5 Based on the results of geological model construction, geothermal probes and surface temperature sensors are used to collect stratum temperature data at multiple locations in the target area in real time, calculate geothermal gradients, and analyze the changing trends of geothermal activity. The specific steps for generating activity change analysis results are as follows: S401: Based on the geological model construction result, using the geothermal probe and the surface temperature sensor, the formation temperature data of multiple locations in the target area are collected in real time to generate a real-time temperature data set; In sub-step S401, based on the established geological model, geothermal probes and surface temperature sensors are used to collect formation temperature data in real time at multiple locations in the target area. The sensors are distributed in different geographical locations to provide temperature information with a wide coverage, ensuring the comprehensiveness and representativeness of the data. The data collected by the sensors are transmitted to the data processing center in real time via a wireless network to monitor the temperature changes of the formation in real time. The collected temperature data is immediately marked with a timestamp and stored in a database to form a real-time temperature data set. The data set records the real-time data of each measuring point and is used to observe the short-term and long-term change trends of the ground temperature, providing accurate real-time data for the development and management of geothermal energy.

[0035] S402: Based on the real-time temperature data set, fill the data gaps at multiple locations using data interpolation, optimize the continuity of the data, calculate the variation range of the formation temperature and the geothermal gradient, and generate a geothermal gradient calculation result; In sub-step S402, based on the real-time temperature data set, data interpolation technology is used to fill in data gaps and calculate the change amplitude of formation temperature and geothermal gradient. During the execution process, the interpolation method is used to fill in data gaps, and the formula is used. Calculate geothermal gradients; in, represents the geothermal gradient, is the temperature difference, is the depth difference, Detailed explanation of the formula and the process of formula calculation and derivation: Assume that at two different depths, and , the temperatures recorded by the geothermal probes are and , the temperature difference and depth difference are , , calculate the geothermal gradient:

[0036] The results show that the temperature rises by an average of 0.1°C for every meter of drilling down in the region. The calculation is used to assess the geothermal resource potential of the region and provide key data for geothermal energy development.

[0037] S403: Based on the geothermal gradient calculation results, analyze the time variation characteristics of the temperature data, identify the pattern and cycle of geothermal activity, and generate activity variation analysis results; In sub-step S403, based on the geothermal gradient calculation results, time series analysis methods are used, including autoregressive moving average model and seasonal decomposition time series trend, to analyze the temporal variation characteristics of temperature data, reveal the periodicity and trend of temperature variation, identify the pattern and cycle of geothermal activity, predict future temperature trends and geothermal activity through the model, identify potential geothermal anomalies, and the generated activity change analysis results provide a perspective for in-depth understanding of geothermal phenomena, provide a scientific basis for the monitoring and management of geothermal energy, and support the sustainable use of geothermal energy.

[0038] See also Figure 6 Based on the results of activity change analysis and combined with geological models, the steps of identifying regional characteristics rich in geothermal resources and generating geothermal resource distribution heat maps are as follows: S501: Based on the activity change analysis results, perform spatial distribution analysis on the formation temperature data, identify the geothermal activity concentration area, and generate a geothermal activity area data set; In sub-step S501, the spatial distribution analysis of the formation temperature data is carried out using the activity change analysis results, and the geographic information system and spatial interpolation technology are used. The Kriging interpolation method is used to process the formation temperature data to realize spatial continuity modeling and identify the concentrated areas of geothermal activity, including data cleaning of the formation temperature data, elimination of outliers, importing the processed temperature data through GIS software, setting spatial interpolation parameters, such as selecting a suitable semivariable function to optimize the interpolation accuracy, generating a temperature contour map, and intuitively displaying the temperature distribution. The execution result is a geothermal activity area dataset, which includes the geothermal activity intensity and distribution location of each area. The dataset provides an important basis for geothermal resource assessment and development.

[0039] S502: Using the geothermal activity regional data set and combining it with the geological model, analyze the geological structural characteristics, including the thermal conductivity and fracture distribution of the rock formation, calculate the correlation between various geological characteristics and geothermal activity, and generate a key geological characteristic data set; The specific formula for calculating the correlation between various geological features and geothermal activity is:

[0040] in, represents the correlation coefficient between geological characteristics and geothermal activity, Representative The geological characteristic observation value of each data point, represents the average value of all geological feature observations, Representative Observed value of geothermal activity intensity at data points, represents the average value of all geothermal activity intensity observations, is the index of each data point in the dataset.

[0041] formula:

[0042] Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the correlation coefficient between various geological characteristics and the intensity of geothermal activity, including rock thermal conductivity, fracture distribution, and rock mineral composition. The results reflect the strength of the linear relationship between geological characteristics and geothermal activity, and determine the impact of various geological characteristics on geothermal activity, providing a scientific basis for geothermal energy exploration and development; Parameter meaning and setting value: For the The geological characteristic observation values ​​of the data points are assumed to be the thermal conductivity of the rock formation. The measured values ​​of the target measurement points are 3.5W / mK, 5.0W / mK, and 4.0W / mK. is the average value of all geological characteristic observations, W / mK; For the The observed values ​​of geothermal activity intensity at data points are assumed to be 100°C, 105°C, and 98°C. is the average value of the observed geothermal activity intensity, °C; Substitute the parameters into the formula for calculation:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] result It shows that there is a positive correlation between geological characteristics and geothermal activity, reflecting that when the selected geological characteristics are enhanced, geothermal activity also shows an enhancing trend. The calculation results provide a scientific basis for the development and exploration of geothermal resource areas.

[0051] S503: Analyze and predict the spatial distribution of geothermal data using key geological feature data sets, identify areas rich in geothermal resources, and generate a geothermal resource distribution heat map; In sub-step S503, based on the key geological feature data set, spatial analysis and modeling techniques are used to predict the spatial distribution of geothermal data, and spatial autocorrelation analysis and hotspot analysis methods are used to identify areas rich in geothermal resources, including inputting key geological feature data into the geographic information system, setting spatial analysis parameters, using hotspot analysis algorithms, identifying high-density areas of geothermal resources, generating heat maps by calculating local statistics of geothermal data, and outputting geothermal resource distribution heat maps to intuitively display resource distribution patterns. Heat maps provide key geographic positioning information for the exploration and development of geothermal energy, and support decision makers in effective resource management and development.

[0052] See also Figure 7 , using the geothermal resource distribution heat map, predicting the geothermal resource hotspot areas, combining the geological characteristics of the target location, quantitatively evaluating the geothermal potential, analyzing the economic feasibility of the prediction results, and generating the prediction results of favorable areas are as follows: S601: Based on the geothermal resource distribution heat map, according to the identified geothermal resource hotspot areas, the geographical locations and geological feature information of multiple hotspots are recorded to generate hotspot location data; In sub-step S601, based on the geothermal resource distribution heat map, the geographical location and geological characteristic information of multiple identified geothermal resource hotspots are recorded, the heat map is analyzed using a geographic information system, the coordinate information of each hotspot is extracted, and the geological characteristics related to the hotspot are extracted from the geological database, including rock type, thermal conductivity and fracture density. The process is implemented through database query and spatial connection analysis, integrating the extracted hotspot location information and geological characteristics, and constructing a data table including the latitude and longitude, geological characteristics and attributes of each hotspot. The execution result is hotspot positioning data, including detailed information of each hotspot, which lays the foundation for subsequent development potential assessment.

[0053] S602: Based on the hotspot location data, considering geological stability and infrastructure status, evaluate the geothermal development potential of multiple hotspot locations and generate development potential analysis data; In sub-step S602, based on the hotspot positioning data, the geothermal development potential of multiple hotspot locations is evaluated by considering geological stability and infrastructure status, and development potential analysis data is generated. A geological stability analysis is performed on each hotspot area, and the stability of the stratigraphic structure is evaluated using the finite element analysis method. The infrastructure status is evaluated, and the engineering geological survey data is used to determine whether the development conditions are met. Through comprehensive analysis, a potential assessment model is established. The model uses geological stability, infrastructure and thermal resource richness as input variables to generate development potential analysis data, indicating the development potential level of each hotspot area. The data provides an important basis for subsequent decision-making and helps determine priority development areas.

[0054] S603: Analyze and predict the development costs, outputs and environmental impacts of multiple hotspot locations using development potential analysis data, evaluate economic feasibility, and generate favorable area prediction results; In sub-step S603, the development potential analysis data is used to analyze and predict the development costs, outputs and environmental impacts of multiple hotspot locations, evaluate economic feasibility, generate favorable area prediction results, apply the cost-benefit analysis model, quantify the development costs of each hotspot, including equipment investment, operating expenses and maintenance costs, use the output prediction model, combine resource assessment data, predict potential energy output, calculate the rate of return on investment, and evaluate the impact of development on the ecological environment through the environmental impact assessment model. Considering multiple factors such as water resource utilization, land use changes and biodiversity, generate favorable area prediction results, clarify economic feasibility and environmental impact, and provide a comprehensive reference for development decisions.

[0055] See also Figure 8 , a geothermal resource favorable area prediction system, the geothermal resource favorable area prediction system is used to execute the above geothermal resource favorable area prediction method, the system includes: The data acquisition and cleaning module uses sensors to collect electromagnetic and seismic wave data of the target area in real time based on regional location data, records the location information of multiple data points, and performs data cleaning and standardization. It combines data filtering to eliminate environmental noise and irrelevant interference, and identifies data features of various stratigraphic structures to generate feature data recognition results. The stratigraphic analysis and modeling module is based on the feature data recognition results. Through spatial alignment and scale conversion of data, it analyzes the correlation between data layers, constructs a three-dimensional geological model, verifies the accuracy of the model with field drilling data, and generates model verification results. Based on the model verification results, the geothermal gradient analysis module uses geothermal probes and surface temperature sensors to collect formation temperature data in real time and calculate geothermal gradients, analyze the changing trend of geothermal activity, and generate geothermal data collection records; The structural impact analysis module analyzes the impact of geological structural characteristics on geothermal resources based on geothermal data collection records, identifies regional characteristics rich in geothermal resources, and generates geothermal resource characteristic heat maps; The resource assessment and prediction module uses geothermal resource characteristic heat maps to predict geothermal resource hot spots, conducts quantitative assessment of geothermal potential in combination with geological characteristics, considers output, development costs and environmental impacts, analyzes the economic feasibility of prediction results, and generates favorable area prediction results.

[0056] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for predicting favorable areas of geothermal resources, characterized in that: The following steps are involved: Based on the regional location data, sensors are used to collect electromagnetic and seismic wave data at multiple locations in the target area, and the data and location information are associated, combined with data cleaning and standardization to generate a geological exploration data set; Based on the geological exploration data set, using data filtering to eliminate environmental noise and irrelevant interference, combining data analysis to identify geological data features, identify data features of multiple stratigraphic structures, and generate stratigraphic structure identification results; Using the stratigraphic structure identification results, according to the spatial distribution characteristics of the data, a three-dimensional geological model is constructed, and combined with actual drilling data, the accuracy and reliability of the model are verified to generate a geological model construction result; Based on the geological model construction results, using geothermal probes and surface temperature sensors, real-time acquisition of formation temperature data at multiple locations in the target area, calculation of geothermal gradients, and analysis of geothermal activity trends to generate activity change analysis results; Based on the activity change analysis results, combined with the geological model, by analyzing the impact of various geological structural characteristics on geothermal resources, identifying regional characteristics rich in geothermal resources, and generating a geothermal resource distribution heat map; The geothermal resource distribution heat map is used to predict geothermal resource hot spots, and the geothermal potential is quantitatively evaluated in combination with the geological characteristics of the target location, the economic feasibility of the prediction results is analyzed, and the prediction results of favorable areas are generated.

2. The method for predicting favorable geothermal resource areas according to claim 1, characterized in that: The geological exploration data set includes electromagnetic wave intensity data, seismic wave velocity data, and geographic coordinate information; the stratigraphic structure identification results include rock hardness characteristic information, soil moisture content information, and void distribution density data; the geological model construction results include a three-dimensional stratigraphic continuity model, geological fault locations, and groundwater flow direction maps; the activity change analysis results include stratigraphic temperature time series information, geothermal gradient change maps, and abnormal temperature activity areas; the geothermal resource distribution heat map includes high geothermal potential area identification information, temperature distribution uniformity ratings, and key geothermal activity indicators; the favorable area prediction results include geothermal resource development cost estimation data, expected geothermal output, and geothermal development environmental impact analysis results.

3. The method for predicting favorable geothermal resource areas according to claim 1, characterized in that: Based on the regional location data, sensors are used to collect electromagnetic and seismic wave data at multiple locations in the target area, and the data and location information are associated. Combined with data cleaning and standardization, the steps to generate a geological exploration data set are as follows: Based on regional location data, using seismic wave and electromagnetic wave sensors, collect geological data from multiple locations in the target area in real time, record geographic location information, and generate geological data collection records; Based on the geological data acquisition records, analyze and identify errors and outliers in the geological data, correct geographic coordinate deviations and erroneous readings, optimize data quality, and generate geological data processing results; Based on the geological data processing results, the geological data is formatted and standardized, including unifying the timestamp format and data scale, to generate a geological exploration data set.

4. The method for predicting favorable geothermal resource areas according to claim 1, characterized in that: Based on the geological exploration data set, the steps of using data filtering to eliminate environmental noise and irrelevant interference, combining data analysis to identify geological data features, identifying data features of multiple stratigraphic structures, and generating stratigraphic structure identification results are as follows: Based on the geological exploration data set, frequency analysis is performed on the exploration data to identify and remove noise caused by environmental factors, including electromagnetic interference and microseismic activity noise, to generate denoised geological data; Based on the de-noised geological data, feature analysis is performed on the geological data to identify data features of various rock densities, soil moisture, and void distribution, and generate stratigraphic feature data; According to the stratigraphic characteristic data, stratigraphic structure classification is performed to identify stratigraphic structures at multiple locations in the target area, including rocks, soil, and voids, and generate stratigraphic structure identification results.

5. The method for predicting favorable geothermal resource areas according to claim 1, characterized in that: Using the stratigraphic structure identification results, according to the spatial distribution characteristics of the data, a three-dimensional geological model is constructed, and combined with actual drilling data, the accuracy and reliability of the model are verified. The specific steps for generating the geological model construction results are: Based on the stratigraphic structure identification result, the geological data is spatially aligned according to the geographic location information, and the stratigraphic data is dimensionally adjusted to match the actual physical scale to generate a spatially aligned data set; Based on the spatially aligned data set, the connectivity and continuity between the strata are evaluated by analyzing the physical and chemical correlations between the multiple strata data, and an inter-strata characteristic analysis result is generated; The three-dimensional geological model is constructed by using the interlayer characteristic analysis results, and the accuracy and reliability of the model are evaluated in combination with the field drilling data to generate the geological model construction results.

6. The method for predicting favorable geothermal resource areas according to claim 1, characterized in that: Based on the geological model construction results, using geothermal probes and surface temperature sensors, the formation temperature data of multiple locations in the target area are collected in real time, the geothermal gradient is calculated, and the change trend of geothermal activity is analyzed. The specific steps for generating activity change analysis results are as follows: Based on the geological model construction results, using geothermal probes and surface temperature sensors, real-time acquisition of formation temperature data at multiple locations in the target area to generate a real-time temperature data set; Based on the real-time temperature data set, data interpolation is used to fill in data gaps at multiple locations, the continuity of the data is optimized, and the variation range of the formation temperature and the geothermal gradient are calculated to generate a geothermal gradient calculation result; Based on the geothermal gradient calculation results, the time variation characteristics of the temperature data are analyzed, the patterns and cycles of geothermal activity are identified, and activity variation analysis results are generated.

7. The method for predicting favorable geothermal resource areas according to claim 1, characterized in that: Based on the activity change analysis results, combined with the geological model, by analyzing the impact of various geological structure characteristics on geothermal resources, identifying the regional characteristics rich in geothermal resources, and generating the geothermal resource distribution heat map, the specific steps are: Based on the activity change analysis results, spatial distribution analysis of formation temperature data is performed to identify geothermal activity concentration areas and generate geothermal activity area data sets; Using the geothermal activity regional data set, combined with the geological model, the geological structural characteristics are analyzed, including the thermal conductivity and fracture distribution of the rock formation, the correlation between various geological characteristics and geothermal activity is calculated, and a key geological characteristic data set is generated; The key geological feature data set is used to analyze and predict the spatial distribution of geothermal data, identify areas rich in geothermal resources, and generate a geothermal resource distribution heat map.

8. The method for predicting favorable geothermal resource areas according to claim 7, characterized in that: The specific formula for calculating the correlation between various geological features and geothermal activity is: in, represents the correlation coefficient between geological characteristics and geothermal activity, Representative The geological characteristic observation value of each data point, represents the average value of all geological characteristic observations, Representative Observed value of geothermal activity intensity at data points, represents the average value of all geothermal activity intensity observations, is the index of each data point in the dataset.

9. The method for predicting favorable geothermal resource areas according to claim 1, characterized in that: The geothermal resource distribution heat map is used to predict the geothermal resource hotspot area, and the geothermal potential is quantitatively evaluated in combination with the geological characteristics of the target location. The economic feasibility of the prediction results is analyzed, and the steps for generating the prediction results of the favorable area are specifically as follows: Based on the geothermal resource distribution heat map, according to the identified geothermal resource hotspot areas, the geographical locations and geological feature information of multiple hotspots are recorded to generate hotspot location data; Based on the hotspot location data, considering geological stability and infrastructure status, evaluating the geothermal development potential of multiple hotspot locations, and generating development potential analysis data; The development potential analysis data is used to analyze and predict the development costs, outputs and environmental impacts of multiple hotspot locations, evaluate economic feasibility, and generate favorable area prediction results.

10. A geothermal resource favorable area prediction system, characterized in that: According to the method for predicting favorable areas of geothermal resources according to any one of claims 1 to 9, the system comprises: The data acquisition and cleaning module uses sensors to collect electromagnetic and seismic wave data of the target area in real time based on regional location data, records the location information of multiple data points, and performs data cleaning and standardization. It combines data filtering to eliminate environmental noise and irrelevant interference, and identifies data features of various stratigraphic structures to generate feature data recognition results. The stratigraphic analysis and modeling module analyzes the correlation between data layers based on the feature data identification results through spatial alignment and scale conversion of the data, constructs a three-dimensional geological model, verifies the accuracy of the model in combination with field drilling data, and generates model verification results; The geothermal gradient analysis module uses geothermal probes and surface temperature sensors based on the model verification results to collect formation temperature data in real time and calculate geothermal gradients, analyze the changing trend of geothermal activity, and generate geothermal data collection records; The structural impact analysis module analyzes the impact of geological structural characteristics on geothermal resources based on the geothermal data collection records, identifies regional characteristics rich in geothermal resources, and generates a geothermal resource characteristic heat map; The resource assessment and prediction module uses the geothermal resource characteristic heat map to predict geothermal resource hot spots, conducts quantitative assessment of geothermal potential in combination with geological characteristics, considers output, development costs and environmental impacts, analyzes the economic feasibility of the prediction results, and generates favorable area prediction results.

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