Method for optimally selecting target area for exploration of hydrothermal geothermal resources based on semi-quantitative analysis

Abstract

The invention discloses a semi-quantitative analysis-based target area optimization method for exploration of hydrothermal geothermal resources, which relates to the field of hydrothermal geothermal resources, and comprises the following steps: determining a calculation range, carrying out index division on sedimentary basin type geothermal resources, upheaval mountain convection type geothermal resources, hot spring geothermal resources and county geothermal resources, carrying out basic parameter selection on secondary indexes, and carrying out target area optimization. And geothermal resource potential calculation, development and utilization potential calculation and geothermal resource comprehensive potential calculation are carried out, and development and utilization comprehensive potential calculation is combined with ecological red line distribution to delimit four areas of development priority, development encouraging, development postponing and development limiting. The geothermal resource development and utilization potentials of different regions are evaluated based on geothermal geological conditions, geothermal resource conditions and development and utilization requirements, finally, the large and middle potential regions are superposed through a spatial comprehensive analysis method, and a geothermal resource target region for further exploration is preferably selected from the large and middle potential regions. The invention provides a set of multi-element semi-quantitative geothermal exploration target region optimization theory and method.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of hydrothermal geothermal resources, and specifically to a method for optimizing target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis. Background Art

[0002] Hydrothermal geothermal resources refer to geothermal resources that exist in natural groundwater and its steam. They are usually at a high temperature and can be used for power generation, heating, hot springs and other purposes. According to the temperature, hydrothermal geothermal resources can be divided into medium-temperature hydrothermal type (temperature greater than 90°C) and high-temperature hydrothermal type (temperature greater than 150°C). The geothermal target area should have good geothermal geological conditions, including high heat flow value, high geothermal gradient, etc. These conditions are conducive to the formation and enrichment of geothermal resources.

[0003] At present, the delineation of hydrothermal geothermal resource targets at home and abroad is based on the theory of "finding heat by heat", which is simply to combine the surface thermal display and geophysical exploration results to delineate the target area and conduct drilling verification. Geothermal drilling has many uncertainties, high costs and high risks, which limits the large-scale exploration and development and utilization of hydrothermal geothermal resources. It does not comprehensively consider the relevant conditions of geothermal resources such as "source, access, cover, storage and water", quantitatively determine the potential of geothermal resources, and select target areas for further exploration. In summary, the existing technology has the problems of unreasonable target area selection and high risk of drilling verification, which need to be solved urgently. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a method for optimizing target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis, so as to solve the technical problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis comprises the following steps:

[0007] Determine the calculation scope: determine the sedimentary basin-type geothermal resources and uplifted mountain convection-type geothermal resources that need to be calculated for resource potential; determine the hot springs and county-level geothermal resources that need to be calculated for development and utilization potential;

[0008] Determine the calculation indicators: divide the indicators of sedimentary basin geothermal resources, uplift mountain convection geothermal resources, hot springs, and county geothermal resources, including primary indicators and secondary indicators. The secondary indicators are the refinement of the primary indicators.

[0009] Secondary indicator assignment: According to the existing geothermal reports and recent exploration results, basic parameters are selected for the secondary indicators of sedimentary basin-type geothermal resources, uplifted mountain convection-type geothermal resources, hot springs, and county geothermal resources. If there are measured data, the measured data will be selected. If there are no measured data, the values ​​will be obtained through analogy or experience.

[0010] Data standardization: Data standardization is performed on the basic parameters of the secondary indicators by unifying the basic parameters of different sources, formats and specifications;

[0011] Calculation of geothermal resource potential: The analytic hierarchy process and principal component analysis are used to calculate the scores of sedimentary basin-type geothermal resources and uplift mountain convection-type geothermal resources; and the quartile method is used to divide the intervals; the intervals are specifically divided into three intervals: large potential, medium potential, and small potential;

[0012] Calculation of development and utilization potential: Use the analytic hierarchy process to calculate the scores of hot springs and county geothermal resources; and use the quartile method to divide the intervals;

[0013] Calculation of comprehensive potential of geothermal resources: Based on the calculation scores of sedimentary basin-type geothermal resources and uplift mountain convection-type geothermal resources, the non-repeated areas are deleted and the geothermal resource prospecting areas and general areas are delineated.

[0014] Calculation of comprehensive development and utilization potential: Scoring is performed based on hot springs and county geothermal resources, and spatial analysis is used in combination with the distribution of ecological red lines to delineate four areas: priority development, encouraged development, deferred development, and restricted development;

[0015] Target area delineation: Based on the prospective areas obtained from the comprehensive potential calculation of geothermal resources, as well as the two areas of priority development and encouraged development determined from the comprehensive potential calculation of development and utilization, the preferred target areas are comprehensively determined.

[0016] Preferably, the first-level indicators of the sedimentary basin geothermal resources include five items: source, access, cover, reservoir, and water. The second-level indicators are as follows: "source" selects the geothermal flow, whether there is a low-velocity body at 20 km, and the main lithology of the basement as the second-level indicators; "access" selects the scale of the basin-controlling fault as the second-level indicator; "cover" selects the lithology of the cover as the second-level indicator; "reservoir" selects the reservoir thickness, reservoir temperature, reservoir lithology and recoverable modulus as the second-level indicators; "water" selects the water-richness grade and the number of geothermal fields as the second-level indicators;

[0017] The first-level indicators of the uplifted mountain convection-type geothermal resources include fault scale, fault-related hot springs, fault activity, and fault exposed strata. The second-level indicators are as follows: "Fault scale" can be subdivided into cutting depth, extension length, and fracture zone width as second-level indicators; "Fault-related hot springs" can be subdivided into average water temperature, number of hot springs, and heat storage temperature as second-level indicators; "Fault activity" can be subdivided into activity history and magnitude as second-level indicators; "Fault exposed strata" uses the proportion of igneous rocks as a second-level indicator;

[0018] The first-level indicators of hot springs include fluid quality, location conditions, development needs, and hot spring policies. The second-level indicators are as follows: "fluid quality" selects water temperature, water volume, and water quality as second-level indicators; "location conditions" selects transportation as second-level indicators; "development needs" selects population and GDP as second-level indicators; and "hot spring policies" select the number of regulations involved as second-level indicators;

[0019] The first-level indicators of geothermal resources in the county include resource conditions, mining potential, development needs, and geothermal policies. The second-level indicators are as follows: "Resource conditions" selects recoverable heat and water quality as the second-level indicators; "mining potential" selects exploration degree as the second-level indicator; "development needs" selects population, GDP, and heating time as the second-level indicators; and "geothermal policy" selects the number of regulations involved as the second-level indicator.

[0020] Preferably, the analytic hierarchy process further comprises the following sub-steps:

[0021] Sub-step 1: Establish the first-level index and the second-level index to construct the judgment matrix;

[0022] Sub-step 2: Determine the weights based on the judgment matrices constructed based on the primary and secondary indicators, and use a one-time test method to test the weights;

[0023] Sub-step three: Calculate the score based on the determined weights and standardized basic parameters.

[0024] Preferably, the specific formula of the one-time inspection method is as follows:

[0025] CR=CI / RI, CI=(λ_max-n) / (n-1);

[0026] Among them, CR: represents the consistency ratio of the consistency test method. If the CR value is less than 0.1, it is considered that the judgment matrix has satisfactory consistency; if the CR value is greater than or equal to 0.1, the judgment matrix needs to be adjusted;

[0027] RI: Random consistency index, a constant;

[0028] CI: represents the consistency index of the judgment matrix, λ_max is the maximum characteristic root of the judgment matrix, and n is the order of the judgment matrix, that is, the number of primary or secondary indicators.

[0029] Preferably, the principal component analysis method comprises the following sub-steps:

[0030] Sub-step 1: First, perform KMO and Bartlett's tests to determine whether principal component analysis can be performed;

[0031] Sub-step 2: Calculate the basic parameters of the secondary indicators by analyzing the variance explanation table, and select the number of characteristic root values ​​greater than 1, that is, the number of principal components;

[0032] Sub-step three: Calculate the score based on the determined principal components.

[0033] Preferably, the quartiles are values ​​at three split points when the calculated scores are arranged from small to large and divided into four equal parts; specifically, as follows:

[0034] Lower quartile Q1: calculate the value at the 25% position in the score;

[0035] Median Q2: Calculate the value at the 50% position in the score, which is the median in the traditional sense;

[0036] Upper quartile Q3: calculate the value at the 75% position in the score;

[0037] The calculation method of Q1, Q2, and Q3 is: Q1=(i+1)×0.25, Q2=(i+1)×0.5, Q3=(i+1)×0.75, where i is the total number of items for calculating the score; when the calculated position is an integer, the value of the corresponding position is directly taken; when the position is a decimal, the average of the values ​​at the two integer positions adjacent to the decimal is taken as the quartile.

[0038] Preferably, the ecological red line is specifically an ecological function red line or an ecological protection red line, which refers to an area within the ecological space that has special and important ecological functions and must be strictly protected.

[0039] In summary, the present invention mainly has the following beneficial effects:

[0040] The method of the present invention breaks through the traditional geothermal resource exploration method of "looking for heat where there is heat". Based on geothermal geological conditions, the evaluation indicators related to geothermal geological conditions are selected for basin-type and fault-type geothermal resources, and mathematical statistics methods are introduced to quantitatively evaluate the potential of different types of geothermal resources.

[0041] Based on the conditions of geothermal resources and the needs of development and utilization, indicators related to development and utilization are selected to evaluate the potential for development and utilization of geothermal resources in different regions. Finally, through the spatial comprehensive analysis method, the areas with large and medium potential are superimposed, and geothermal resource target areas for further exploration are selected from the areas with large and medium potential. A set of multi-factor semi-quantitative geothermal exploration target area optimization theory and method is proposed, which can solve the irrationality of previous target area optimization and further reduce the risk of drilling verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a diagram of the sedimentary basin geothermal resource potential calculation index system of the present invention;

[0043] Figure 2 Constructing a matrix table for the first-level indicators of geothermal resources of the present invention;

[0044] Figure 3 Constructing a matrix table for the source secondary index of the present invention;

[0045] Figure 4 Constructing a matrix table for the storage secondary index of the present invention;

[0046] Figure 5 A table showing the weights of various indicators in the sedimentary basin geothermal resource calculation system of the present invention;

[0047] Figure 6 The potential calculation result table of the analytic hierarchy process of the present invention;

[0048] Figure 7 The KMO test and Bartlett's test result table of the calculation system of the present invention;

[0049] Figure 8 is a table explaining the variance of the calculation system of the present invention;

[0050] Fig. 9 is the principal component matrix table of the present invention;

[0051] Fig.10 is the comprehensive score table of the principal component analysis method of the present invention;

[0052] Fig.11 This is a diagram of the uplift mountain convection type geothermal resource potential calculation index system of the present invention;

[0053] Fig.12 This is a list of weights of various indicators of the present invention;

[0054] Fig.13 The potential calculation result table of the analytic hierarchy process of the uplift mountain convection type geothermal resource calculation system of the present invention;

[0055] Fig.14It is a table of KMO test and Bartlett's test results of the uplift mountain convection type calculation system of the present invention;

[0056] Fig.15 The potential calculation result table of the principal component analysis method of the inter-fault zone calculation system of the present invention;

[0057] Fig.16 The potential analysis model of hot spring development and utilization of the present invention

[0058] Figure 17.1 and Figure 17.2 The calculation result of the hot spring development and utilization potential of the present invention;

[0059] Fig.18 The potential level analysis model of underground hot water development and utilization of the present invention;

[0060] Fig.19 The calculation result of the county development and utilization potential of the present invention;

[0061] Fig. 20 Determine a prospecting area list for the combined two methods of the present invention;

[0062] Fig.21 The potential calculation and calculation correspondence of the present invention;

[0063] Fig. 22 A list of exploration target area information submitted for the present invention;

[0064] Fig.23 The calculation diagram of the geothermal resource exploration and development and utilization in Qinghai Province of the present invention;

[0065] Fig.24 It is the overall step framework diagram of the present invention. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0067] like Figures 1 to 23 As shown, the method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis includes the following steps:

[0068] Determine the calculation scope: determine the sedimentary basin-type geothermal resources and uplifted mountain convection-type geothermal resources that need to be calculated for resource potential; determine the hot springs and county-level geothermal resources that need to be calculated for development and utilization potential;

[0069] Determine the calculation indicators: divide the indicators of sedimentary basin geothermal resources, uplift mountain convection geothermal resources, hot springs, and county geothermal resources, including primary indicators and secondary indicators. The secondary indicators are the refinement of the primary indicators.

[0070] Secondary indicator assignment: According to the existing geothermal reports and recent exploration results, basic parameters are selected for the secondary indicators of sedimentary basin-type geothermal resources, uplifted mountain convection-type geothermal resources, hot springs, and county geothermal resources. If there are measured data, the measured data will be selected. If there are no measured data, the values ​​will be obtained through analogy or experience.

[0071] Data standardization: Data standardization is performed on the assignment of secondary indicators by unifying the basic parameters of different sources, formats and specifications;

[0072] Calculation of geothermal resource potential: The analytic hierarchy process and principal component analysis were used to calculate the scores of sedimentary basin-type geothermal resources and uplift mountain convection-type geothermal resources; and the quartile method was used to divide the intervals; the intervals were specifically divided into three intervals: large potential, medium potential, and small potential;

[0073] Calculation of development and utilization potential: Use the analytic hierarchy process to calculate the scores of hot springs and county geothermal resources; and use the quartile method to divide the intervals;

[0074] Calculation of comprehensive potential of geothermal resources: Based on the calculation scores of sedimentary basin-type geothermal resources and uplift mountain convection-type geothermal resources, the non-repeated areas are deleted and the geothermal resource prospecting areas and general areas are delineated.

[0075] Calculation of comprehensive development and utilization potential: Scoring is performed based on hot springs and county geothermal resources, and spatial analysis is used in combination with the distribution of ecological red lines to delineate four areas: priority development, encouraged development, deferred development, and restricted development;

[0076] Target area delineation: Based on the prospective areas obtained from the comprehensive potential calculation of geothermal resources, as well as the two areas of priority development and encouraged development determined from the comprehensive potential calculation of development and utilization, the preferred target areas are comprehensively determined.

[0077] The first-level indicators of sedimentary basin geothermal resources include source, access, cover, reservoir and water. The second-level indicators are as follows: "source" selects the geothermal flow, whether there is a low-velocity body at 20km, and the main lithology of the basement as the second-level indicators; "access" selects the scale of basin-controlling faults as the second-level indicator; "cover" selects the lithology of the cover as the second-level indicator; "reservoir" selects reservoir thickness, reservoir temperature, reservoir lithology and recoverable modulus as the second-level indicators; "water" selects the water-richness level and the number of geothermal fields as the second-level indicators;

[0078] The primary indicators of uplift mountain convection-type geothermal resources include fault scale, fault-related hot springs, fault activity, and fault exposed strata. The secondary indicators are as follows: "Fault scale" can be subdivided into cutting depth, extension length, and fracture zone width as secondary indicators; "Fault-related hot springs" can be subdivided into average water temperature, number of hot springs, and heat storage temperature as secondary indicators; "Fault activity" can be subdivided into activity history and magnitude as secondary indicators; "Fault exposed strata" uses the proportion of igneous rocks as a secondary indicator;

[0079] The first-level indicators of hot springs include fluid quality, location conditions, development needs, and hot spring policies. The second-level indicators are as follows: "Fluid quality" selects water temperature, water volume, and water quality as the second-level indicators; "Location conditions" selects transportation as the second-level indicator; "Development needs" selects population and GDP as the second-level indicators; and "Hot spring policies" select the number of regulations involved as the second-level indicator;

[0080] The first-level indicators of geothermal resources in counties include resource conditions, mining potential, development needs, and geothermal policies. The second-level indicators are as follows: "Resource conditions" selects recoverable heat and water quality as the second-level indicators; "mining potential" selects exploration degree as the second-level indicator; "development needs" selects population, GDP, and heating time as the second-level indicators; and "geothermal policy" selects the number of regulations involved as the second-level indicator.

[0081] The AHP also includes the following sub-steps:

[0082] Sub-step 1: Establish the first-level index and the second-level index to construct the judgment matrix;

[0083] Sub-step 2: Determine the weights based on the judgment matrices constructed based on the primary and secondary indicators, and use a one-time test method to test the weights;

[0084] Sub-step three: Calculate the score based on the determined weights and standardized basic parameters.

[0085] The specific formula of the one-time inspection method is as follows:

[0086] CR=CI / RI, CI=(λ_max-n) / (n-1);

[0087] Among them, CR: represents the consistency ratio of the consistency test method. If the CR value is less than 0.1, it is considered that the judgment matrix has satisfactory consistency; if the CR value is greater than or equal to 0.1, the judgment matrix needs to be adjusted;

[0088] RI: Random consistency index, a constant;

[0089] CI: represents the consistency index of the judgment matrix, λ_max is the maximum characteristic root of the judgment matrix, and n is the order of the judgment matrix, that is, the number of primary or secondary indicators.

[0090] The principal component analysis method includes the following sub-steps:

[0091] Sub-step 1: First, perform KMO and Bartlett's tests to determine whether principal component analysis can be performed;

[0092] Sub-step 2: Calculate the basic parameters of the secondary indicators by analyzing the variance explanation table, and select the number of characteristic root values ​​greater than 1, that is, the number of principal components;

[0093] Sub-step three: Calculate the score based on the determined principal components.

[0094] The quartiles are the values ​​at the three dividing points when the calculated scores are arranged from small to large and divided into four equal parts; the details are as follows:

[0095] Lower quartile Q1: calculate the value at the 25% position in the score;

[0096] Median Q2: Calculate the value at the 50% position in the score, which is the median in the traditional sense;

[0097] Upper quartile Q3: calculate the value at the 75% position in the score;

[0098] The calculation method of Q1, Q2, and Q3 is: Q1=i+1×0.25, Q2=i+1×0.5, Q3=i+1×0.75, where i is the total number of items for calculating the score; when the calculated position is an integer, directly take the value of the corresponding position; when the position is a decimal, take the average of the values ​​at the two integer positions adjacent to the decimal as the quartile.

[0099] It should be noted that in this first embodiment, the geothermal resource potential of the sedimentary basin type is calculated.

[0100] 1. Determine the calculation scope of the potential of sedimentary basin-type geothermal resources. The calculation scope of the potential of conductive geothermal resources in sedimentary basins includes 13 sedimentary basins identified in the "Regional Geology of Qinghai Province" and the Tongren Basin, Menyuan Basin and Tanggula Mountain Hot Spring Basin that are considered to have potential through research. The calculation units selected in this way are more reasonable and basically cover the conductive geothermal resource endowment areas of sedimentary basins in Qinghai Province.

[0101] 2. Determine the calculation indicators. According to the basin-type geothermal resource endowment conditions of Qinghai Province and the different combinations of geothermal geological conditions, divide the inter-basin potential calculation system and select "source, connection, cover, storage, and water" as the first-level indicators. The second-level indicators will further refine the five items.

[0102] Among them, "source" selects geothermal flow, whether there is a low-velocity body at 20km, and the main lithology of the basement as secondary indicators; "connection" selects the scale of basin-controlling faults as secondary indicators; "cover" selects the lithology of the cover as secondary indicators; "reservoir" selects reservoir thickness, reservoir temperature, reservoir lithology and recoverable modulus as secondary indicators; "water" selects water-richness level and the number of geothermal fields as secondary indicators. The above 11 calculation indicators, such as Figure 1 As shown, it can basically reflect the characteristics of sedimentary basin-type geothermal resources in Qinghai Province. It is reasonable and feasible to use it as a rating indicator for resource potential calculation. The calculation results are divided into three levels: large, medium and small resource potential using the quartile method.

[0103] 3. Assignment of calculation indicators: According to previous geothermal reports and recent exploration results, basic parameters are selected for 11 indicators in different calculation areas. Basic parameter values ​​are taken from measured data if there are measured values; if there are no measured data, they are calculated by analogy or experience, and scoring standards are formulated according to the reasonable range of the basic parameters to assign basic parameters to the calculation indicators of geothermal resource potential in sedimentary basins.

[0104] 4. Data standardization: Data standardization is to eliminate the impact of different dimensions between basic parameters of different indicators during analysis and calculation, and to improve the comparability of data by uniformly processing basic parameters of different sources, formats and specifications, so as to facilitate data comparison, contrast and comprehensive analysis. This time, considering the actual indicator value range and characteristics, the scoring standard is determined in a targeted manner according to the basic data range, and the basic parameters are standardized according to the scoring standard.

[0105] 5. Calculation of geothermal resource potential, including two methods, namely, analytic hierarchy process and principal component analysis;

[0106] (I) Analytical Hierarchy Process (AHP): The principle is that the AHP decomposes the problem into different components according to the nature of the problem and the overall goal to be achieved, and clusters and combines the factors at different levels according to the mutual influence and affiliation between the factors to form a multi-level analysis structure model, which ultimately reduces the problem to the determination of the relative importance of the plans and measures for decision-making relative to the overall goal or the ranking of their relative advantages and disadvantages.

[0107] 1. Determine the weights. According to the principle of determining the weights of indicators in the hierarchical analysis method, the importance of each indicator of sedimentary basin-type geothermal resources is scored by experts, and the expert scoring results are statistically summarized to establish the first-level and second-level structural judgment matrices for the calculation indicators.

[0108] ①First-level calculation index construction matrix and weight calculation, such as Figure 2 As shown in the figure, by using the weight calculation of the sum-product method, the weight of the source is 38.892%; the weight of the access is 15.644%; the weight of the cover is 11.49%; the weight of the storage is 23.241%; and the weight of water is 10.734%. The calculation results of the hierarchical analysis method show that the maximum characteristic root λmax is 5.387. In order to judge the consistency and reliability of the judgment matrix, a consistency test is usually required;

[0109] RI is the random consistency index. According to the RI table, the corresponding RI value is 1.12. CI is the consistency index and CR is the consistency ratio. If the CR value is less than 0.1, the judgment matrix is ​​considered to have satisfactory consistency. If the CR value is greater than or equal to 0.1, the judgment matrix needs to be adjusted to improve its consistency. In summary, CR = CI / RI = 0.087 < 0.1, which passes the one-time test.

[0110] ② Secondary indicator calculation matrix and weight calculation. Since the secondary calculation indicators of the primary calculation indicators "connection" and "cover" are both 1, the secondary indicator weight is no longer calculated; the secondary indicator water-richness level and the number of geothermal fields of the primary calculation indicator "water" are considered to have the same importance level by experts, and are determined as 50% of the primary weight respectively. This time, only the secondary indicator weights of "source" and "storage" are calculated, such as Figure 3 shown.

[0111] ③ Source, calculated by the AHP method, specifically the weight calculation of the sum-product method, the weight of the terrestrial heat flow value is 57.143%; the weight of the existence of a low-velocity body is 28.571%; the weight of the basement lithology is 14.286%. The calculation results of the AHP method show that the maximum characteristic root is 3.0, and the corresponding RI value is 0.525 according to the RI table, so CR = CI / RI = 0.0 < 0.1, passing the one-time test.

[0112] ④ Storage, such as Figure 4 As shown in the figure, the weight calculation of the AHP method shows that the weight of reservoir temperature is 33.017%, the weight of reservoir thickness is 10.602%, the weight of reservoir lithology is 6.734%, and the weight of recoverable modulus is 49.647%. The calculation results of the AHP method show that the maximum characteristic root is 4.249, and the corresponding RI value is 0.882 according to the RI table. Therefore, CR = CI / RI = 0.094 < 0.1, passing the one-time test.

[0113] The weights of 11 secondary calculation indicators of conductive geothermal resources in sedimentary basins determined by the final analytic hierarchy process are as follows: Figure 5 As shown, the terrestrial heat flow value is 22.23%; whether there is a low-velocity body at 20km is 11.11%; the basement lithology is 5.56%; the scale of basin-controlling faults is 15.64%; the cap rock lithology is 11.49%; the reservoir temperature is 7.67%; the reservoir thickness is 2.46%; the reservoir lithology is 1.57%; the recoverable modulus is 11.54%; the water-richness grade is 5.37%; and the number of geothermal fields is 5.37%.

[0114] 2. Calculate the score. Multiply the determined indicator weight value by the standardized value of each indicator in the calculation area between basins and add them up to get the analytic hierarchy process score of resource potential in different calculation areas between basins, and use it as the basis for calculating the geothermal resource potential of each calculation area between sedimentary basins. According to the comprehensive score of the analytic hierarchy process, the quartile method, also known as the quartile method, is a commonly used data analysis method in statistics. Quartiles are a type of quantile in statistics, which refers to the values ​​at three dividing points when all values ​​are arranged from small to large and divided into four equal parts. Quartiles include the lower quartile, median and upper quartile. The first quartile of the quartile method is used as the dividing point between small potential and medium potential, and the third quartile is used as the dividing point between medium potential and large potential. The Gonghe Basin, Qaidam Basin, Hoh Xil-Tuotuo River Basin, and Xining Basin are designated as large geothermal resource potential areas; the Tongren Basin, Nangqian Basin, Tanggula Mountain Hot Spring Basin, Minhe Basin, Qumalai-Zhiduo Basin, Menyuan Basin, Haiyan Dongdatan Basin, and Hara Lake Basin are designated as medium geothermal resource potential areas; the Suri Basin, Xinghai-Zeku Basin, Xunhua and Hualong Basins, and Maduo Basin are designated as small geothermal resource potential areas. Figure 6 shown.

[0115] (ii) Principal component analysis: The goal is to find a set of principal components that are linear combinations of the original variables, and these new variables are independent of each other. These new variables are arranged in order from large to small variance, with the first principal component having the largest variance, the second principal component having the second largest variance, and so on. By selecting the first few principal components with the largest variance, the dimensionality reduction of the original data can be achieved.

[0116] 1. Consistency test: First, KMO and Bartlett tests were performed to determine whether principal component analysis can be performed. The results of the KMO test showed that the KMO value was 0.469. At the same time, the results of the Bartlett sphericity test showed that the significance P value was 0.000***1, which was significant at the level. The original hypothesis was rejected, and there was a correlation between the variables. The principal component analysis was valid.

[0117] 2. Determine the number of principal components. By analyzing the variance explanation table, determine the number of principal components. From the total variance explanation table, it can be seen that when the principal component is 5, the characteristic root of the total variance explanation is less than 1. Therefore, a total of 4 principal components are extracted, and the contribution rate of variable explanation is 89.691%, which meets the requirement that the contribution rate of variable explanation is higher than 85%. In general, the higher the variance explanation rate, the more important the principal component is, and the higher the weight ratio should be.

[0118] 3. Determine the principal component expression according to the principal component matrix table, such as Fig. 9 As shown, determine the principal component expression. The principal component matrix table is intended to explain the factor score coefficients (principal component loads) contained in each component, which is used to calculate the component scores and obtain the factor formula. The calculation formula is: linear combination coefficient * (variance explanation rate / cumulative variance explanation rate), and finally normalize it to get the factor weight score. The linear combination coefficient formula is: factor load coefficient divided by the corresponding characteristic root, that is, the coefficient of the component matrix. The final principal component expression is as follows:

[0119] F1 = 0.163 × recoverable modulus + 0.079 × geothermal heat flow + 0.109 × 20 km whether there is a low-velocity body + 0.026 × main lithology of the basement + 0.152 × scale of basin-controlling faults + 0.122 × cap rock lithology + 0.173 × reservoir temperature + 0.15 × reservoir thickness + 0.129 × reservoir lithology + 0.155 × water-richness grade + 0.079 × number of geothermal fields;

[0120] F2 = -0.182 × recoverable modulus + 0.401 × geothermal heat flow + 0.121 × whether there is a low-velocity body in 20 km + 0.214 × main lithology of the basement + 0.144 × scale of basin-controlling faults - 0.109 × cap rock lithology - 0.025 × reservoir temperature - 0.246 × reservoir thickness - 0.25 × reservoir lithology + 0.04 × water-richness grade + 0.477 × number of geothermal fields;

[0121] F3 = 0.05 × recoverable modulus - 0.139 × geothermal heat flow value - 0.259 × 20 km whether there is a low-velocity body + 0.762 × main lithology of the basement - 0.143 × scale of basin-controlling faults + 0.346 × cap rock lithology + 0.137 × reservoir temperature + 0.033 × reservoir thickness - 0.0 × reservoir lithology - 0.262 × water-richness grade + 0.027 × number of geothermal fields;

[0122] F4 = 0.221 × recoverable modulus + 0.39 × geothermal heat flow + 0.485 × whether there is a low-velocity body in 20 km + 0.022 × main lithology of the basement - 0.373 × scale of basin-controlling faults + 0.085 × lithology of the cap rock + 0.088 × reservoir temperature + 0.148 × reservoir thickness - 0.372 × reservoir lithology - 0.234 × water-richness grade - 0.34 × number of geothermal fields;

[0123] From the above we can get: F = (0.498 / 0.84)×F1+(0.142 / 0.84)×F2+(0.101 / 0.84)×F3+(0.099 / 0.84)×F4.

[0124] 4. Calculate the scores. According to the principal component expression, calculate the principal component scores of each calculation area of ​​sedimentary basin-type geothermal resources, and use this as the basis for calculating the geothermal resource potential of each calculation area between sedimentary basins, such as Fig.10 According to the results of the comprehensive score ranking of the principal component analysis method, the quartile method was used to divide the intervals, and it was determined that the Gonghe Basin, the Hoh Xil-Tuotuo River Basin, the Tongren Basin, and the Xining Basin were large geothermal resource potential areas; the Qaidam Basin, the Minhe Basin, the Tanggula Mountain Hot Spring Basin, the Nangqian Basin, the Qumalai-Zhiduo Basin, the Haiyan Dongdatan Basin, the Xinghai-Zeku Basin, and the Maduo Basin were medium geothermal resource potential areas; the Hala Lake Basin, the Suri Basin, the Menyuan Basin, and the Xunhua and Hualong Basins were small geothermal resource potential areas.

[0125] Example 2: Calculation of geothermal resource potential for uplifted mountainous areas;

[0126] First, determine the calculation scope. The calculation of the potential of uplift mountain convective geothermal resources is initially based on 2 first-level faults, 9 second-level faults, 20 third-level faults in the geological records and 24 active faults in the distribution characteristics of active faults in Qinghai Province as calculation units, and the repeated faults are merged, and those without geothermal anomalies along the faults are deleted. Finally, a total of 32 faults are determined for the calculation of uplift mountain geothermal resources potential. The selected calculation units are relatively reasonable, basically covering the uplift mountain convective geothermal resource storage area in Qinghai Province.

[0127] Second, determine the calculation indicators. According to the conditions of the convection-type geothermal resources in the uplifted mountains of Qinghai Province and the different combinations of geothermal geological conditions, the calculation system between fault zones is divided into "fault scale, hot springs related to faults, fault activity, and fault exposed strata" as the first-level calculation indicators; the second-level indicators are refined into five items, such as Fig.11As shown. Among them, "fault scale" can be subdivided into fault cutting depth, fault extension length, and broken zone width as secondary indicators; "hot springs related to faults" can be subdivided into average hot spring water temperature, number of hot springs on the fault zone, and hot spring heat storage temperature as secondary indicators; "fault activity" can be subdivided into fault activity history and magnitude of earthquakes on the fault zone as secondary indicators; "fracture exposed strata" uses the proportion of igneous rocks as a secondary indicator. The calculation results are ranked according to the comprehensive score and divided into three levels of resource potential: large, medium, and small.

[0128] 3. Based on previous geothermal reports, exploration results in recent years and this work, the basis for calculating the potential of uplift mountain convective geothermal resources is determined, basic parameters are assigned to the 9 indicators in different calculation areas, and scoring standards are formulated based on the basic parameter values. The basic parameter values ​​are taken as measured values ​​if there are measured data, and the values ​​are taken by analogy or experience if there are no measured data.

[0129] (I) Fracture scale:

[0130] 1. The fault cutting depth, fault extension length and broken zone width are assigned values ​​according to the "Geological Records of Qinghai Province", "Explanation of the Tectonic Map of Qinghai Province", "Research on the Distribution and Development Characteristics of Active Fault Zones in Qinghai Province", "Distribution Characteristics of Active Faults in Qinghai Province and Their Hazards to Highway Engineering" and recent geothermal resource exploration reports.

[0131] (II) Hot springs related to faults:

[0132] 1. The average water temperature of hot springs is determined based on the results of the latest round of hot spring surveys and previous data. The average water temperature of hot springs on the fault zone is assigned a value. If there are no hot springs on the fault zone, the analogy method is used to refer to the hot spring parameters of similar adjacent fault zones.

[0133] 2. The number of hot springs on the fault zone is assigned based on the results of the latest round of hot spring surveys and previous data. If there are no hot springs on the fault zone, according to the principle of quantifiable indicators, 1 hot spring is assigned.

[0134] 3. The hot spring heat reservoir temperature is assigned a value by taking the average value of the heat reservoir temperature of the hot springs on the fault zone based on the calculation results of the heat reservoir temperature of geothermal anomalies in Chapter 6 on resource calculation and evaluation. If there are no hot springs on the fault zone, the analogy method is used to refer to the hot spring heat reservoir temperature parameters of adjacent similar fault zones.

[0135] (III) Fault activity: 1. Fault activity history: Based on the data of "Geological Records of Qinghai Province", "Explanation of Tectonic Map of Qinghai Province", "Study on Distribution and Development Characteristics of Active Fault Zones in Qinghai Province" and "Distribution Characteristics of Active Faults in Qinghai Province and Their Hazards to Highway Engineering", five categories are assigned: Pre-Triassic, Late Triassic, Himalayan, Late Pleistocene and Holocene.

[0136] 2. The magnitude of earthquakes occurring on the fault zone is assigned based on the relationship diagram of fault structure, new structure and earthquake distribution in Qinghai Province and the magnitude of earthquakes occurring on the fault zone according to the China Earthquake Networks Center.

[0137] (IV) Strata exposed on faults: 1. The proportion of igneous rocks is assigned based on the regional geology and regional geological structure by the ratio of the igneous rock distribution range of the hot springs on the fault zone to the igneous rock distribution range of the fault zone. If there are no hot springs on the fault zone, the parameter values ​​are obtained by analogy with similar adjacent fault zones.

[0138] 4. Data standardization: Data standardization is to eliminate the impact of different dimensions between basic parameters of different indicators during analysis and calculation. By uniformly processing basic parameters of different sources, formats and specifications, the comparability of data is improved, which is convenient for data comparison, contrast and comprehensive analysis. This time, considering the actual indicator value range and characteristics, the scoring standard is determined in a targeted manner according to the basic data range, and each basic parameter is standardized according to the scoring standard. Scoring standard for uplift mountain convective geothermal resource indicators, such as Figure 17.1 and 17.2 shown.

[0139] 5. Calculation of geothermal resource potential:

[0140] (I) Hierarchy Analysis Method, 1. Determine the weights. According to the principle of determining the weights of indicators in the hierarchy analysis method, experts score each indicator of the uplift mountain convective geothermal resources, and statistically summarize the expert scoring results to establish the first-level and second-level structural judgment matrices of the calculation indicators. Among them, the second-level indicators of the first-level calculation indicator "fault activity" are activity history and magnitude. According to the expert scoring, the activity history accounts for 25% of the first-level weight, and the magnitude accounts for 75% of the first-level weight. There is only one second-level calculation indicator of the first-level calculation indicator "fracture exposed strata", and the first-level indicator is directly used instead of the second-level indicator. The indicator weight determination method is consistent with that of the conductive geothermal resources in the sedimentary basin. This section will not go into details, and directly lists the final weights of each indicator, such as Fig.12 shown.

[0141] 2. Calculate the score. Multiply the weight value obtained by the analytic hierarchy process by the standardized value of each indicator in different calculation areas of uplift mountain convection-type geothermal resources and add them up to get the analytic hierarchy process score of geothermal resource potential in different calculation areas, which will be used as the final score of geothermal resource potential in each calculation area. According to the comprehensive ranking results of the analytic hierarchy process, the quartile method is used to divide the intervals, and the rankings 1-8 are defined as large potential areas; rankings 9-24 are medium potential areas; rankings 25-32 are small potential areas, such as Fig.13 shown.

[0142] (II) Principal component analysis:

[0143] 1. Consistency test. Based on the resource potential calculation of conductive geothermal resources in sedimentary basins in the previous section, the resource potential calculation of convective geothermal resources in uplifted mountains by principal component analysis is based on the same model and algorithm. It should be noted that the principal component analysis of convective geothermal resources in uplifted mountains explains the information of all variables as much as possible through a few principal components to achieve the purpose of dimensionality reduction. The results of the KMO test show that the value of KMO is 0.562. At the same time, the results of the Bartlett sphericity test show that the significant P value is 0.000***1, which is significant at the level, rejecting the original hypothesis, and there is a correlation between the variables. The principal component analysis is effective, such as Fig.14 shown.

[0144] 2. Calculate the ranking and output the comprehensive scores of the principal component analysis method for uplift mountain convection-type geothermal resources. See the table. According to the comprehensive ranking results of the principal component analysis method, the quartile method is used to divide the intervals, and the rankings 1-8 are defined as large potential areas; the rankings 9-24 are medium potential areas; the rankings 24-32 are small potential areas, such as Fig.15 shown.

[0145] Example 3: Calculation of the development and utilization potential of underground hot water resources:

[0146] The calculation system for the development and utilization potential of geothermal resources is based on 91 hot springs and 42 counties in the province as calculation units. Among them, the first and second level calculation indicators are set for hot springs with physiotherapy as the main calculation direction; the first and second level calculation indicators are set for heating as the main calculation direction in the counties. The calculation results are divided into three levels of development and utilization potential, large, medium and small, according to the comprehensive score, and finally the prospective areas for the development and utilization of geothermal resources in our province are determined.

[0147] 1. Calculation of hot spring development and utilization potential:

[0148] (I) Determine the calculation units and indicators. The calculation objects are 91 hot springs in the province. With hot spring tourism as the utilization direction, fluid quality, location conditions, development needs, and policies are selected as four primary indicators, and 7 secondary indicators are determined based on the primary indicators. Among them, water temperature, water volume, and water quality are selected as secondary indicators for fluid quality, transportation is selected as secondary indicator for location conditions, population and GDP are selected as secondary indicators for development needs, and the number of regulations involved is selected as secondary indicator for policy settings. According to the distribution of calculation scores, the calculation objects are calculated according to quartiles to determine the potential of large, medium, and small. The hierarchical analysis model of hot spring development and utilization potential is as follows: Fig.16 shown.

[0149] (II) Determine the weights. The potential for hot spring development and utilization is calculated using the analytic hierarchy process. The method for determining the weights of indicators is consistent with that for conductive geothermal resources in sedimentary basins and is not repeated here. The specific results are as follows: The weights of the first-level indicators: fluid quality, location conditions, development needs, and policies are 47.8%, 10.8%, 19.7%, and 21.7%, respectively; the weights of the second-level indicators: water temperature, water volume, water quality, transportation, population, GDP, and number of regulations are 64.8%, 23.0%, 12.2%, 10.8%, 66.7%, 33.3%, and 21.7%, respectively; the total weights are 31.0%, 11.0%, 5.8%, 10.8%, 13.1%, 6.60%, and 21.7%, respectively.

[0150] (III) Calculate the score,

[0151] The working principle of the present invention is to divide the intervals according to the calculation results by using the quartile method, that is, the hot springs ranked 1-23 are calculated as having great potential; the hot springs ranked 24-68 are calculated as having medium potential; the hot springs ranked 69-91 are calculated as having small potential. Figure 17.1 and Figure 17.2 shown.

[0152] According to the comprehensive score ranking results of the principal component analysis method, the quartile method was used to divide the intervals, and it was determined that the Gonghe Basin, the Hoh Xil-Tuotuo River Basin, the Tongren Basin, and the Xining Basin were large areas with geothermal resource potential; the Qaidam Basin, the Minhe Basin, the Tanggula Mountain Hot Spring Basin, the Nangqian Basin, the Qumalai-Zhiduo Basin, the Haiyan Dongdatan Basin, the Xinghai-Zeku Basin, and the Maduo Basin were medium areas with geothermal resource potential; the Hala Lake Basin, the Suri Basin, the Menyuan Basin, and the Xunhua and Hualong Basins were small areas with geothermal resource potential.

[0153] Example 4: Calculation of the potential for development and utilization of geothermal resources in counties:

[0154] (I) Determine the calculation scope and indicators. The calculation objects are 42 districts and counties in the province. Taking geothermal heating as the utilization direction, resource conditions, mining potential, development needs, and policies are selected as four primary indicators, and 7 secondary indicators are determined based on the primary indicators. Among them, resource conditions select recoverable heat and water quality as secondary indicators, mining potential selects exploration degree as secondary indicator, development needs select population, GDP, and heating time as secondary indicators, and policies select the number of regulations involved as secondary indicators. According to the distribution of calculation scores, the calculation objects are divided into quartiles to determine the potential of large, medium, and small. The hierarchical analysis model of the potential for the development and utilization of geothermal resources in counties is as follows: Fig.18 shown.

[0155] (II) Determine the weights and calculate the potential for the development and utilization of underground hot water using the analytic hierarchy process. The method for determining the weights of indicators is consistent with that for conductive geothermal resources in sedimentary basins and will not be repeated here.

[0156] (III) Calculate the scores. Based on the calculation results, the quartile method is used to divide the intervals, that is, the counties ranked 1-10 are calculated as having great potential; the counties ranked 11-32 are calculated as having medium potential; and the counties ranked 33-42 are calculated as having small potential. The calculation results are as follows: Fig.19 shown.

[0157] Potential calculation and target area delineation are performed according to the above-mentioned embodiments 1 to 4:

[0158] 1. Calculation of geothermal resource potential:

[0159] Based on the potential calculation results of geothermal resources of conductive type in sedimentary basins and convective type in uplifted mountains calculated by two methods, and considering the effectiveness of the two methods and the comparison between the results and the actual situation, we finally decided to adopt the idea of ​​"intersection", delete the non-repeated areas, and delineate the prospective areas and general areas of geothermal resources. Prospective areas are defined as areas with large and medium potential; the remaining potential areas are delineated as general areas. Finally, the analytic hierarchy process determined 36 prospective areas, namely 12 basins and 24 fault zones; the principal component analysis method determined 36 prospective areas, namely 12 basins and 24 fault zones. The intersection of the two methods was taken for the prospective areas, and finally 32 prospective areas for geothermal resources in the province were determined, namely 10 basins and 22 fault zones.

[0160] 2. Calculation of development and utilization potential:

[0161] Based on the calculation results of the development and utilization potential of hot springs and county geothermal resources, the spatial analysis method is used, combined with the distribution of ecological red lines, to delineate four development areas: priority, encouragement, suspension and restriction. Fig.21As shown. The priority development areas are mainly concentrated in Gonghe-Guide-Guinan in Gonghe Basin, Tongren in Huangnan Prefecture, and Dachaidan, Delingha, and Tanggula Pass in Haixi Prefecture; the encouraged development areas are mainly concentrated in Xining-Haidong in Xining Basin, Haiyan-Gangcha-Tianjun in Haibei Prefecture, Xinghai-Tongde in Hainan Prefecture, and Zhiduo and Nangqian in Yushu Prefecture; the ecological red line areas are restricted for development; the rest of the areas are temporarily suspended for development. Finally, the priority and encouraged areas will be determined as the prospective areas for the development and utilization of geothermal resources, and the other areas will be general areas.

[0162] 3. Delineate the target area

[0163] (I) Basis for target area delineation:

[0164] The indicators selected for the calculation of geothermal resource potential can fully reflect the actual geothermal geological conditions of our province. The indicators selected for the calculation of development and utilization potential comprehensively consider indicators such as local needs, resource status and economic conditions. The calculation results can effectively guide the next step of geothermal resource exploration and development and utilization in our province. Therefore, this study believes that the potential calculation results are the most important basis for the delineation of geothermal target areas. According to the calculation results of geothermal resource potential, the calculation areas with large and medium potential are delineated as resource prospecting areas. According to the calculation results of geothermal resource development and utilization potential, the priority areas and encouraged areas are delineated as development and utilization prospecting areas. The geothermal resource prospecting areas are superimposed on the development and utilization prospecting areas, and the needs of major towns and localities in the concentrated population areas of our province are comprehensively considered, and finally the geothermal target areas in our province that can be further explored are delineated.

[0165] (II) Target area optimization:

[0166] According to the target area delineation basis, 10 underground hot water resource target areas in our province are initially submitted for further exploration: Lushar Town, Huangzhong District, Xining, Bazhou Town, Minhe County, Haidong, Ping'an-Ledu area, Haidong, Xihai Town, Haiyan County, Haibei, Shagou Township, Guinan County, Hainan, Ziketan Town, Xinghai County, Hainan, Dachaidan Town, Haixi, Chaka Town, Wulan County, Haixi, Xiangda Town, Nangqian County, Yushu, and Jiajiboluo Town, Zhiduo County, Yushu. Fig. 22 and Fig.23 shown.

[0167] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis, characterized in that , including the following steps: S1. Determine the calculation scope: determine the sedimentary basin-type geothermal resources and uplifted mountain convection-type geothermal resources that need to be calculated for resource potential; Identify the hot springs and county geothermal resources that need to be calculated for development and utilization potential; S2. Determine the calculation indicators: divide the indicators of sedimentary basin-type geothermal resources, uplift mountain convection-type geothermal resources, hot springs, and county geothermal resources, including primary indicators and secondary indicators. The secondary indicators are the refinement of the primary indicators. S3. Secondary index assignment: According to the existing geothermal reports and recent exploration results, basic parameters are selected for the secondary indicators of sedimentary basin geothermal resources, uplift mountain convection geothermal resources, hot springs, and county geothermal resources. If there are measured data, the measured data will be selected. If there are no measured data, the values ​​will be obtained through analogy or experience. S4. Data standardization: Data standardization is performed on the basic parameters of the secondary indicators by unifying the basic parameters of different sources, formats and specifications; S5. Calculation of geothermal resource potential: The analytic hierarchy process and principal component analysis are used to calculate the scores of sedimentary basin-type geothermal resources and uplift mountain convection-type geothermal resources; and the quartile method is used to divide the intervals; the intervals are specifically divided into three intervals: large potential, medium potential, and small potential; S6. Calculation of development and utilization potential: Use the analytic hierarchy process to calculate the scores of hot springs and county geothermal resources; and use the quartile method to divide the intervals; S7. Calculation of comprehensive potential of geothermal resources: Based on the calculation scores of sedimentary basin-type geothermal resources and uplift mountain convection-type geothermal resources, the non-repeated areas are deleted and the geothermal resource prospective areas and general areas are delineated. S8. Calculation of comprehensive development and utilization potential: Calculate scores based on hot springs and county geothermal resources, and use spatial analysis methods, combined with the distribution of ecological red lines, to delineate four areas: priority development, encouraged development, deferred development, and restricted development; S9. Target area delineation: Based on the prospective areas obtained from the comprehensive potential calculation of geothermal resources, as well as the two areas of priority development and encouraged development delineated from the comprehensive potential calculation of development and utilization, the preferred target area is comprehensively determined.

2. The method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis according to claim 1, characterized in that: The first-level indicators of the sedimentary basin geothermal resources include source, access, cover, reservoir and water. The second-level indicators are as follows: "source" selects the geothermal flow, whether there is a low-velocity body at 20km, and the main lithology of the basement as the second-level indicators; "access" selects the scale of basin-controlling faults as the second-level indicator; "cover" selects the lithology of the cover as the second-level indicator; "reservoir" selects the reservoir thickness, reservoir temperature, reservoir lithology and recoverable modulus as the second-level indicators; "water" selects the water-richness level and the number of geothermal fields as the second-level indicators; The primary indicators of the uplifted mountain convection-type geothermal resources include fault scale, fault-related hot springs, fault activity, and fault exposed strata. The secondary indicators are as follows: "fault scale" can be subdivided into cutting depth, extension length, and fracture zone width as secondary indicators; "fault-related hot springs" can be subdivided into average water temperature, number of hot springs, and heat storage temperature as secondary indicators; "fault activity" can be subdivided into activity history and magnitude as secondary indicators; "fault exposed strata" uses the proportion of igneous rocks as a secondary indicator; The first-level indicators of hot springs include fluid quality, location conditions, development needs, and hot spring policies. The second-level indicators are as follows: "fluid quality" selects water temperature, water volume, and water quality as second-level indicators; "location conditions" selects transportation as second-level indicators; "development needs" selects population and GDP as second-level indicators; "hot spring policies" selects the number of regulations involved as second-level indicators; The first-level indicators of geothermal resources in the county include resource conditions, exploitation potential, development needs, and geothermal policies. The second-level indicators are as follows: "resource conditions" selects recoverable heat and water quality as the second-level indicators, "exploitation potential" selects exploration degree as the second-level indicators, "development needs" selects population, GDP, and heating time as the second-level indicators, and "geothermal policy" selects the number of regulations involved as the second-level indicator.

3. The method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis according to claim 1, characterized in that: The analytic hierarchy process also includes the following sub-steps: Sub-step 1: Establish judgment matrices constructed by primary and secondary indicators respectively; Sub-step 2: Determine the weights based on the judgment matrices constructed based on the primary and secondary indicators, and use a one-time test method to test the weights; Sub-step three: Calculate the score based on the determined weights and standardized basic parameters.

4. The method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis according to claim 3 is characterized in that: The specific formula of the one-time inspection method is as follows: CR=CI / RI, CI=(λ_max-n) / (n-1); Among them, CR: represents the consistency ratio of the consistency test method. If the CR value is less than 0.1, it is considered that the judgment matrix has satisfactory consistency; if the CR value is greater than or equal to 0.1, the judgment matrix needs to be adjusted; RI: Random consistency index, a constant; CI: represents the consistency index of the judgment matrix, λ_max is the maximum characteristic root of the judgment matrix, and n is the order of the judgment matrix, that is, the number of primary or secondary indicators.

5. The method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis according to claim 1, characterized in that: The principal component analysis method comprises the following sub-steps: Sub-step 1: First, perform KMO and Bartlett's tests to determine whether principal component analysis can be performed; Sub-step 2: Calculate the basic parameters of the secondary indicators by analyzing the variance explanation table, and select the number of characteristic root values ​​greater than 1, that is, the number of principal components; Sub-step three: Calculate the score based on the determined principal components.

6. The method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis according to claim 1, characterized in that: The quartiles are the values ​​at the three split points when the calculated scores are arranged from small to large and divided into four equal parts; specifically, they are as follows: Lower quartile (Q1): The value at the 25% position in the calculated score; Median (Q2): Calculate the value at the 50% position in the score, which is the median in the traditional sense; Upper quartile (Q3): Calculate the value at the 75% position in the score; The calculation method of Q1, Q2, and Q3 is: Q1=(i+1)×0.25, Q2=(i+1)×0.5, Q3=(i+1)×0.75, where i is the total number of items for calculating the score; when the calculated position is an integer, the value of the corresponding position is directly taken; when the position is a decimal, the average of the values ​​at the two integer positions adjacent to the decimal is taken as the quartile.

7. The method for selecting target areas for hydrothermal geothermal resource exploration based on semi-quantitative analysis according to claim 1, characterized in that: The ecological red line is specifically the ecological function red line or the ecological protection red line, which refers to an area within the ecological space that has special and important ecological functions and must be strictly protected.

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