Genesis Mechanism and Resource Evaluation of Low-Temperature Hydrothermal Geothermal Fields in Wenquan County, Xinjiang

Genesis Mechanism and Resource Evaluation of Low-Temperature Hydrothermal Geothermal Fields in Wenquan County, Xinjiang | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genesis Mechanism and Resource Evaluation of Low-Temperature Hydrothermal Geothermal Fields in Wenquan County, Xinjiang Chenhao Sun, Xinchang Wei, Kai Chen, Zhilong Qi, Yalu Wang, Ying Xi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7251739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Wenquan County area in Xinjiang has a large number of hot springs and rich geothermal resources, with high potential for geothermal resource development and utilization. At present, systematic research on the formation mechanism and resource potential of geothermal fields in this area is relatively weak, which has to some extent restricted the comprehensive development and utilization of geothermal resources in Wenquan County. This paper selects Wenquan County as the research area. Based on the collection and analysis of geophysical, geochemical, remote sensing interpretation, and well logging data, the scope of geothermal anomaly areas and geothermal fields in the research area is comprehensively determined, and the formation mechanism of geothermal fields is revealed. The geothermal resources in the research area are evaluated using the surface heat flow method and the thermal storage method. The results show that: (1) The Wenquan County geothermal field is located in the valley between Alatao Mountain and Biezhentao Mountain. It is roughly irregular in shape and mainly consists of low-temperature convection-type strip geothermal reservoirs with a longitudinal buried depth of about 3000 m; (2) The heat in geothermal fields mainly comes from the heat flow of lava in the upper mantle. Groundwater in the region is heated by deep circulation and forms geothermal fluids, which are transported upward through channels formed by fault structures, continuously transferring heat from the lower layers to the surface to form geothermal fields; (3) The geothermal field has a geothermal reservoir capacity of 2.37×109 km3 and geothermal resources of 6.52×1018 J, equivalent to 2.23×108 t of standard coal. This study basically clarified the geothermal formation model and resource rating of Wenquan County and provided an important basis for the development and utilization of geothermal resources in the study area. Physical sciences/Energy science and technology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Solid earth sciences geothermal field formation mechanism resource evaluation Wenquan County Xinjiang Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. INTRODUCTION As a low-carbon, clean, and renewable energy source, geothermal resources can not only effectively reduce dependence on fossil fuels, but also significantly reduce greenhouse gas emissions, and are of strategic importance in global energy transition and sustainable development.At the same time, accelerating the development and utilization of geothermal energy is of great significance for achieving carbon peak and carbon neutrality goals. The mechanism of geothermal formation is the theoretical basis for geothermal resource exploration and development. Foreign scholars Isabel Pérez Martínez et al. (2025) analyzed the fluid and gas geochemical characteristics of the Araró-Simirao geothermal system in Mexico and revealed the mechanism by which geothermal fluids rise through fracture zones in this geothermal field.Loïc Peiffer et al. (2024) analyzed the geochemical characteristics of hydrothermal fluids at the southern tip of the Baja California Peninsula and revealed the fluid sources, water-rock interactions, and tectonics of two geothermal systems in the coastal region.Currently, domestic research on the formation mechanism of geothermal energy mainly focuses on four aspects: heat source, reservoir, channel, and overburden (Wang Guiling et al., 2020).Mao Xiang et al. (2021) revealed the origin of the Gaoyang geothermal field through the analysis of geological structure, temperature field, and water chemistry characteristics, combined with fracture systems and groundwater circulation paths. Luo Shaoqiang (2020) analyzed the formation background, occurrence conditions, and distribution patterns of geothermal resources in the Chazi geothermal field by combining the regional geological background with the geological and geophysical field research results of the geothermal field.Liu Deming et al. (2025) constructed a four-dimensional model integrating regional tectonic evolution analysis, deep geophysical exploration, fracture system thermal conductivity analysis, and thermodynamic evaluation of the reservoir and overburden to reveal the formation mechanism of deep geothermal resources in the Fen-Wei Graben.Ning Yiwu (2024) revealed the geothermal origin of the Neihuang Uplift through geophysical exploration, hydrogeochemical, and isotope tracing, elucidating the “western precipitation recharge-deep heat conduction-tectonic fracture transport-sedimentary layer insulation” mechanism of geothermal formation.Du Jiang et al. (2024) revealed the formation mechanism of dry hot rocks in the Rehuo Geothermal Field in Hunan Province through geochemical temperature indicators, radioactive heat generation rate analysis, and multidisciplinary detection using gravity, magnetism, and seismic methods.Dong Dongzhu et al. (2022) revealed the formation mechanism of geothermal resources in the Linfen Basin through hydrogeological mapping, remote sensing interpretation, comprehensive geophysical exploration, drilling, and water quality analysis. Geothermal resource evaluation can quantify resource potential and is the basis for geothermal energy development and utilization. Commonly used evaluation methods mainly include the thermal storage method, numerical generation, and the Monte Carlo method (Wang Guiling et al., 2017; Ma Feng et al., 2020; Han Jun et al., 2022).Due to its advantages of simple calculation, wide applicability, and high cost-effectiveness compared with other methods, the thermal storage method is widely used in the evaluation of geothermal resources (Ren Shuai, 2020; Yang Yunzhan, 2020; Zhou Jianfei, 2023).Foreign scholar Brown Christopher S. (2022) utilized a combination of three-dimensional geological and thermal models with the volume method to complete a potential evaluation of dry hot rocks in northern England; Violine Gascuel et al. (2020) developed a 3D temperature model for the sedimentary basin of Anticosti Island, Canada, and its underlying Precambrian basement, and completed a geothermal resource evaluation for areas with sparse data.Anthony E. Ciriaco et al. (2020) established a proxy numerical model using an improved experimental design and response surface method (ED/RSM) in the Ohaaki and Rotorua geothermal fields. The calculation results were consistent with the traditional volume method, providing an efficient solution for geothermal resource probability assessment.María José Oviedo et al. (2025) conducted a geothermal resource potential assessment of the Charlevoix meteorite impact crater in Canada using numerical simulation methods. Daniel Fariña González et al. (2025) combined random Monte Carlo simulation methods with the well-known geothermal resource assessment volume method to complete a geothermal resource assessment for power generation on the island of La Palma in the Canary Islands.Mohamed Ayed Elbalawy et al. (2025) established a geothermal favorable zone analysis (GPFA) model, integrated 3D seismic interpretation, GIS technology, and 3DHIP calculations, and identified the optimal exploration and development areas in Békés County, Hungary, for the first time, assessing the geothermal potential.Sedara Samuel O. et al. (2022) used a combination of geophysical and numerical simulation methods to assess the geothermal resource potential of the Ikogosi hot spring area (IKGWS) in Nigeria. In recent years, Chinese scholars have improved the thermal storage method for calculating geothermal resources based on three-dimensional modeling, enabling more accurate calculations of geothermal resources in different areas studied (Zhao Jie et al., 2023; Zhu Zhenzhou, 2019).Chen Haiwen et al. (2023) used factor analysis to extract independent influencing factors and conducted an evaluation study of 11 dry hot rock areas in China. Hu Jian et al. (2025) conducted a study of the geothermal geological characteristics, resource evaluation, and favorable area prediction of the Zhumadian area based on the geological features of the Henan Zhumadian region and combined with two-dimensional seismic interpretation results.Therefore, the methods used to calculate and study geothermal resources at home and abroad are becoming increasingly diversified based on previous research methods. Currently, research and technology for the development and utilization of geothermal resources are generally mature in China, but geothermal research in Xinjiang is still in its infancy. Except for the Shengshan geothermal field, the level of exploration in other areas of Wenquan County is generally low, which has certain limitations on the development and utilization of geothermal resources in the study area.Therefore, based on a systematic analysis of the geothermal geological conditions in the study area, this paper mainly analyzes the formation mechanism of geothermal resources and calculates their reserves, thereby providing a scientific basis for the development and utilization of geothermal resources in Wenquan County. 2. GEOTHERMAL GEOLOGICAL BACKGROUND OF THE STUDY AREA 2.1. Natural geographical conditions The study area is located in the north of Wenquan County, Xinjiang, in the mountain valley between Alatao Mountain and Biezhentao Mountain. The main landforms include eroded hills, erosion valleys, impact valleys, alluvial plains, lacustrine plants, and diluvial plains. The study area is located in the heart of the Eurasian continent and has a typical continental temperate semi-arid climate.The study area is 29.25 km long, 13.65 km wide, and has an area of 399.26 km 2 , as shown in Fig.1. 2.2 Regional geological conditions The igneous rocks in the study area are mainly Late Mesoproterozoic granites, Mid-Variscan granites, and vein rocks, with an exposed area of about 120 km 2 . Metamorphic rocks are mainly distributed in Biezhentao Mountain south of Wenquan County and Alatao Mountain in the north, extending in an east-west direction consistent with the regional structural line, with an exposed area of approximately 190 km 2 . The study area is located in the western part of the North Tianshan Mountains and belongs to the Junggar-North Tianshan Fold Belt (II) and Tianshan Fold Belt (III) in terms of tectonic unit division. Specifically, it includes the North Tianshan Yudi Graben Fold Belt (II 3 ) and the Borokonu Graben Fold Belt (III 1 ) in terms of secondary tectonic units.A total of 20 fractures have developed in the study area. Based on the degree of influence of the geothermal system, they can be divided into three levels. Level I fractures are the main heat-controlling and water-conducting fractures, Level II fractures are the main fractures that control the distribution of the structural zone and have a certain influence on the migration of geothermal fluids, and Level III fractures are secondary fractures that restrict the distribution of the structural zone or local structural development, as shown in Table 1. Table 1. A list of fault structures in the study area Classification of fracture systems Numbering Strike Azimuth Fault characteristics Tendency Inclination angle Fracture level East-west compressional-extensional fault system F1 Approach EW 85°± Unknown nature South 80°± Level I F2 Approach EW 85°± Extensional concealed normal fault South 65°± F3 Approach EW 70°± Compressive concealed reverse fault South 75°± F3-1 Approach EW-NW 90°± Extensional concealed normal fault North 80°± F12 Approach EW-NW 85°± Unknown nature North 60°± Level II Northwest-trending normal fault or unknown nature F4 NW-SE 290°± Extensional concealed normal fault South 70°± Level I F4-1 Approach EW 85°± Extensional concealed normal fault South 45°± F5 NW-SE 290°± Extensional concealed normal fault Northeast 85°± F6-F11and F13 NW-SE 290°± Unknown nature Southwest 65°± Level III Northeast-southwest structural system F15、F16、F17 NW-SE 45°± Unknown nature South 50°± Level I F18 NW-SE 45°± Unknown nature North 75°± Level I 3. DELIMITATION OF THE GEOTHERMAL FIELD IN THE STUDY AREA AND ANALYSIS OF ITS GEOTHERMAL RESERVOIR CHARACTERISTICS 3.1 Delimitation of geothermal field (1) Remote sensing interpretation Thermal infrared remote sensing technology captures surface thermal radiation information through sensors and uses atmospheric correction methods, single-channel algorithms, and split-window algorithms to estimate surface temperature (Bian Yu et al., 2021).In this study, WI-DINSAR technology was employed to interpret the geological structure of the study area using eight PALSAR and two ALOS2 data sets from October 2017 to May 2018. Additionally, the temperature-emissivity separation algorithm was applied to 12 ASTER data sets from the summer (June–August) and winter (December–February) of 2007 to perform temperature inversion and thermal infrared interpretation. The specific remote sensing interpretation process is shown in Fig.3. Using WI-DINSAR technology, active faults and fractures within the study area were interpreted. The temperature-emissivity separation algorithm was used to obtain the distribution pattern of the surface temperature field. Comprehensive remote sensing information analysis was conducted by integrating thermal infrared spectral information and remote sensing structural interpretation information. The interpretation results are shown in Fig.4. As shown in Fig.4, five faults were interpreted in the study area. After excluding false anomaly areas, five geothermal anomaly areas with a total area of 5.928 km 2 were delineated. Furthermore, the remote sensing interpretation map shows that the anomaly areas are mainly distributed around two east-west structures, suggesting that the east-west structure in the central part is the main heat-conducting structure in the study area. At the same time, to verify the effectiveness of the temperature inversion results, the inverted temperatures were compared with the actual measured temperatures, and trend analysis and linear correlation analysis were performed, as shown in Fig.5 and Fig.6. As can be seen from Fig.5 and Fig.6, the temperatures derived from thermal infrared inversion in the study area generally follow the same trend as the measured temperatures, and there is a good linear correlation between the measured temperatures and the inverted temperatures. (2) Geochemical prospecting Studies have shown that elements such as mercury, arsenic, stibium, and bismuth are widely enriched in hot spring sediments, and their distribution patterns have geochemical significance for delineating the boundaries of geothermal fields(Zhu Bingqiu et al., 1983). Given the complex topography of the study area and significant differences in soil layer depths across different topographic units, 262 sampling points were established within the study area, and soil samples were collected at depths of 0 m, 1 m, and 2 m, the correlation coefficients for the four elements at the three depths were calculated and are presented in Table 2. Table 2. Statistical table of soil elements in the study area Quantity contained Elemental Elemental content in soil (μg/g) 0m 1m 2m Hg Maximum values 0.13 0.10 0.13 Minimum value 0.003 0.003 0.003 Average value 0.015 0.012 0.012 Abnormal lower value 0.035 0.028 0.030 As Maximum values 33..4 47.6 112 Minimum value 2.7 1.3 1.4 Average value 13.45 12.64 12.78 Abnormal lower value 23.42 24.70 34.88 Sb Maximum values 2.4 5.6 4.5 Minimum value 0.13 0.12 0.14 Average value 0.85 0.81 0.77 Abnormal lower value 1.48 1.75 1.71 Bi Maximum values 0.91 1.1 1.6 Minimum value 0.12 0.07 0.11 Average value 0.34 0.33 0.32 Abnormal lower value 0.59 0.62 0.63 As can be seen from Table 2, the elements show good correspondence at depths of 0 m, 1 m, and 2 m, with a relatively uniform planar distribution pattern. However, the correspondence between different elements in abnormal zones at the same depth is not obvious, with only a few areas showing a match between the two element abnormal zones. To study the overlap and adjacency of abnormal areas of different elements in a more intuitive and detailed manner, the abnormal areas of four elements (mercury, arsenic, stibium, and bismuth) at the same soil depth were mapped on a 1:10,000 scale map, as shown in Fig.7. Analysis of the distribution of abnormal regions in Fig.7 reveals that, in the element anomaly distribution maps at depths of 1 m and 2 m, the individual element anomaly zones are primarily concentrated around Shengquan Mountain, consistent with the existing hot spring distribution patterns, thereby confirming the accuracy of the test results;In the element anomaly distribution map at a depth of 0 m, there are two locations: one north of the main urban area of Hot Spring County, with an area of 39.7 m 2 , showing a mercury-bismuth element anomaly zone; and another west of Shengquan Mountain, with an area of 3807 m 2 , showing an arsenic-bismuth element anomaly zone. These two areas are designated as geothermal anomaly zones. (3) Geophysical exploration When exploring deep thermal reservoirs and tectonic structures, techniques such as magnetotelluric (MT) methods, audio-frequency magnetotelluric (AMT) methods, controlled-source audio-frequency magnetotelluric (CSAMT) methods, and seismic exploration are commonly employed (Li Pingping et al., 2019; Zhang Yangyang et al., 2020).This study utilized two geophysical methods, magnetotelluric tomography (MT) and controlled-source audio-frequency magnetotelluric tomography (CSAMT), to infer the morphology of hidden structures and the distribution of faults at depths of 10–15 km in the study area, as shown in Fig.8. A total of seven faults (F1, F2, F3, F3-1, F4, F4-1, and F5) were identified.By comparing the remote sensing interpretation results of the fault structures in Fig.8 and Fig.4, it can be observed that the fault locations are almost identical to the remote sensing interpretation fault locations.Based on the principle that the stronger the gradient of electrical resistivity differences, the greater the depth, and the larger the range of resistivity influence, the larger the scale of the fault, it can be concluded that F1, F2, F3, and F4 are relatively larger in scale, followed by F3-1, F4-1, and F5. These findings provide foundational data for delineating deep geothermal anomalies in the study area. (4) Geothermal field area By integrating remote sensing, geochemical, and geophysical data, the boundaries of the geothermal anomaly zone in the study area can be delineated as follows: to the north, extending to the F1 fault; to the south, extending to the F4 and F4-1 faults; to the west, based on thermal infrared anomalies and geophysical data, the boundary of the basement uplift is inferred; and to the east, extending to the eastern boundary of the Shengshan uplift. The total area is 58.05 km², as shown in Fig.9. Based on the geothermal anomaly zone, combined with drilling data, using 15°C (6–12°C higher than the background water temperature) as the benchmark, and comprehensively considering factors such as reservoir temperature, burial depth, tectonic structure, and aquifer boundaries, the final geothermal field was delineated as follows: to the north, extending to the upper plate of the F2 fault; to the south, extending to the upper plate of the F4 and F4-1 faults;westward to the east side of Haotuerha Village, and eastward to the upper part of the F5 fault, forming a long, narrow strip with an area of 35.43 km², as shown in Fig.9. 3.2 Geothermal reservoir characteristics and distribution Research shows that heat transfer in the geothermal reservoir in this area is mainly convective, extending in strips on a plane. It is a low temperature convective type strip geothermal reservoir composed of fracture zones with effective porosity and permeability. A conceptual model is shown in Fig.10. As can be seen from Fig.10: the distribution of geothermal reservoirs in the study area is closely related to fracture zones, and the water content of hot reservoirs is extremely uneven, consisting of rock formations from the Cambrian and Carboniferous periods.Based on the buried depth and thickness of the hot reservoir, the development of fractures, physical properties, thermophysical properties, reservoir temperature, and water content, it was comprehensively determined that the rock groups distributed along the Devonian-Carboniferous period structure are the main hot reservoirs in the study area. The specific characteristics are as follows. (1) Cambrian period hot reservoirs This geothermal reservoir is distributed near the F4 fracture zone on the south side of Shengshan Mountain, with a buried depth of 916.2 to 1355.9 m and a single layer thickness of 169.4 m. The rock type is mainly foliated sandstone and metamorphic siltstone.The fracture development zone accounts for 9.9%, with porosity ranging from 1.43% to 13.92% and permeability from 0.1×10 -3 to 24.45×10 -3 μm 2 , indicating poor water-bearing capacity.The reservoir temperature ranges from 48.9 to 57°C, the geothermal gradient ranges from 1.24 to 3.3°C/100 m, and the rock thermal conductivity ranges from 2.632 to 3.384 W/(m·K), reflecting good thermal conductivity but weak storage and infiltration capacity. (2) Ordovician period hot reservoirs The hot reservoir is distributed on both sides of the north-south uplift in the central part of Shengshan, with a buried depth of 20.22 to 546.84 m and a thickness of 77.4 to 388.2 m.The northern side is mainly quartz sandstone with intact rocks and poor water content. The southern side is affected by the F3-1 and F4 fractures, with 6.5% of the section developed with fractures, porosity of 1.58% to 11.33%, permeability of 0.11×10 -3 to 11.36×10 -3 μm 2 , and good water content.The reservoir temperature is 41.7 to 45.2°C, the geothermal gradient is 1.11 to 2.51°C/100 m, and the thermal conductivity is 1.707 to 3.598 W/(m·K), showing significant differences between the north and south. (3) Devonian period hot reservoirs This hot reservoir is one of the shallowest hot reservoirs in the study area, with a buried depth of 61.53 to 526.12 m on the north side and 1085.6 to 1295.5 m on the south side, and a thickness of 26.2 to 407.5 m.The rock types include silty mudstone, altered tuff, and granite. The northern side has a 34.3% fracture development rate, a permeability of 0.1×10 -3 to 23.33×10 -3 μm 2 , and a reservoir temperature of 24.2 to 55.4°C.The temperature on the south side is 50.8 to 54.1°C, with a geothermal gradient of 0.86 to 6.2°C/100 m. Overall, the water content is average. (4) Carboniferous period hot reservoirs This hot reservoir is mainly distributed in the F3 fracture zone on the north side of Shengshan, with a buried depth of 140 to 582.83 m and a thickness of 442.83 m. The rock type is mainly tectonic breccia and tuffaceous siltstone.The fracture development section accounts for 25.7%, with a porosity of 2.5% to 28.3%, permeability of 0.5×10 -3 to 30×10 -3 μm 2 , reservoir temperature of 41.3 to 69.9°C, and geothermal gradient of 2.53 to 5.15°C/100 m. It is the hot reservoir with the highest temperature and best permeability in the study area. 4. ANALYSIS OF GEOTHERMAL GENESIS MODELS IN THE STUDY AREA The geothermal field in the study area mainly forms convection through deep heat sources connected by fracture structures, and belongs to the low temperature convective type geothermal system.The mountains surrounding the basins, such as Alatao Mountain and Biezhentao Mountain, receive atmospheric precipitation and snowmelt, which are transported deep into the strata, absorbing heat and causing the temperature to rise continuously. When the temperature reaches a certain depth underground and the overburden conditions are favorable, low-temperature hot water is formed, which then rises along the fracture channel to the shallow surface and emerges as hot springs.The study area has a good geothermal system formation mechanism, as shown in Fig.11. Through the analysis of the geothermal genesis model of the study area in Fig.11, the characteristics of heat source, overburden, fluid, reservoir, and channel can be analyzed as follows. (1) Heat sources: The main heat sources in the study area include three types: geothermal heat, tectonic friction heat, and heat generated by radioactive material decay.Among them, mantle heat generation is the main heat source in the study area. According to the temperature measurement curve of KC1 borehole (as shown in Fig.12), the average geothermal gradient in the study area is 7.4°C/100 m, and the geothermal heat flow value is 210.46 mW/m 2 , which is significantly higher than the average value of the northwestern margin of the Junggar Basin in Xinjiang, indicating that there is stable mantle heat flow heat generation in the study area.Tectonic friction heat is a secondary heat source in the study area, mainly due to the intense uplift of the Biezhentao Mountain and Alatao Mountain blocks and the relative subsidence of the Bortala block, resulting in active neotectonic movement.Radioactive decay heat is also a minor heat source in the study area. The average heat generation rate of igneous rocks in the study area is 1.262 μw/m 3 ; that of metamorphic rocks is 1.774 μw/m 3 ; and that of sedimentary rocks is 2.934 μw/m 3 , which is higher than that of granite in general, as shown in Table 3. Table 3. Statistics on rock radiogenic heat generation in the study area Rock types Density（kg/m 3 ） Uranium（ug/g) Thorium（ug/g) Kalium（%） Heat production rateμw/m 3 Average heat generation rateμw/m 3 interval value average value interval value average value interval value average value interval value average value Igneous rock Fine grained diorite 2740-2860 2800 0.4-0.9 0.65 8.43 8.43 0.84-2.01 1.425 0.92 1.262 Felsitic rock 2700-2870 2785 4.3-12.2 8.25 9.83-6.31 8.07 2.07-2.97 2.52 3.01 Altered basalt 2880 2880 1.2 1.2 —— —— 1.56 1.56 0.49 Amphibole lamprophyre 2670 2670 1.57 1.57 1.14 1.14 1.63 1.63 0.63 Metamorphic rock Leptynite 2710-2780 2737 1.1-4.1 2.133 5.5-9.07 7.285 1.09-2.38 1.653 1.22 1.774 Quartz schist 2770-2780 2775 1.45-9.71 5.58 7.14-10.7 8.92 2.1-2.59 2.345 2.33 Metamorphic silty mudstone 2540-2770 2666 0.6-6.77 2.842 1.74-9.86 6.192 2.56-3.78 14.9 2.53 Flake quartzite 2510 2510 7.01 7.01 8.27 8.27 3.20 3.20 2.49 Felsic hornstone 2640 2640 0.7 0.7 —— —— 1.37 1.37 0.30 Sedimentary rock Lithic sandstone 2580-2770 2647 1.16-5.568 2.802 1.2-10.73 5.491 0.99-2.88 1.721 1.24 2.934 Lithic feldspar sandstone 2570-2670 2622 1.9-97.2 23.43 3.24-21.3 9.003 0.296-2.83 1.815 6.62 Crystalline vitric tuff 1850-2720 2585 2.73-74.5 13.115 2.33- 21.72 6.646 0.72-3.34 2.263 3.87 Metamorphic argillaceous siltstone (Sandy structure ) 2590-2670 2633 2.72-4.51 3.694 2.63-3.95 3.226 0.769-2.8 1.986 1.33 Silicarenite 2770 2770 3.66 3.66 7.1 7.1 1.45 1.45 1.61 (2) Overburden: The overburden of the geothermal field in the study area consists of Quaternary silty gravel, recent calcareous sandstone, and Devonian siltstone and tuffaceous sandstone with low thermal conductivity.It has low fracture rate, poor permeability, and poor water-bearing conditions. According to the geothermal well temperature measurement curve, the highest temperature of the stratum 0-358.9 m below the surface is 25.4 m, and the ground temperature increases slowly. Its thermal conductivity is 2.186-2.926 w/(m·℃), which is a good overburden for geothermal fields. (3) Fluids: The main types of groundwater in the study area are bedrock fissure water, karst-fractured groundwater, and loose rock pore diving.The main sources of replenishment are atmospheric precipitation and snowmelt from the northern, southern, and western mountainous areas, torrential rains and floods, and river seepage.It enters the underground aquifer through surface infiltration and underground runoff, and is heated deep in the earth's crust to form geothermal water, which continuously transfers heat from the lower layers to the surface or to a certain depth below the surface.The Piper diagram of geothermal fluids in the study area is shown in Fig.13. It can be seen that the chemical type of geothermal fluids in the study area is mainly SO 4 -Na type geothermal water. (4) Reservoir: The hot reservoirs in the study area are spatially manifested as two types: strip-shaped geothermal reservoirs and layered geothermal reservoirs. They are mainly located in the structural fracture zone and are composed of a set of Paleozoic coastal and shallow marine sedimentary rocks, metamorphic rocks, and igneous rocks. The average heat flow value is estimated to be about 58 mW/m 2 , which is basically consistent with the geothermal background value of the area. (5) Passageways: Fracture structures in the study area serve as key passageways for deep circulation of geothermal water. During the long process of tectonic evolution, especially during the Zhonghua-Lixi period, intense tectonic activity caused the rock layers to overturn, accompanied by the intrusion of large-scale medium-acidic igneous rocks, forming a nearly vertical fracture group (F17) running north-southeast-west.This fracture zone is well connected to the base's thermal fracture (F3), and its structural intersection and fragmented area provide ample space for the storage and migration of geothermal fluids, not only providing an effective channel for the upward flow of geothermal fluids, but also creating favorable conditions for groundwater circulation and storage. 5. EVALUATION OF GEOTHERMAL RESOURCES IN THE STUDY AREA 5.1 Surface heat flow method The surface heat flow method estimates geothermal resource reserves based on the heat emitted from the surface of geothermal fields. This method is mainly used for the evaluation of geothermal resources in areas with low exploration levels. The calculation formula is as follows: (Formula1) In the formula: is the heat emitted during a certain period of time, J; is the heat transferred through rock into the air per unit time, W; is the heat emitted by hot springs, geysers, and fumaroles per unit time, W; is the calculation time period, s; is the average geothermal heat flow value = 3.81°C/100 m × 2.844 W/m•°C = 108.36 mW/m 2 ; is the geothermal field area = 35.43 km 2 ; is the natural flow rate of hot springs or hot water wells (holes), L/s; is the specific heat of water, 1.0 kcal/kg•°C; is the density of water, ρ=1.0 kg/L; is the water temperature of hot spring water, ℃; is the local average temperature over many years, taken as the average temperature of 4.1℃ in the plain area. Table 4. Calculation results of heat release in the study area Parameters Spring No. Natural discharge qv(l/s) Specific heat of water c （kcal/kg•℃) Density of water ρ （kg/l） Hot spring water temperature tr(℃) Average temperature over many years ti(℃) Heat releaseQ（kcal/s） Q2 0.022 1 1 30.5 5 0.561 250.4 Q3 0.022 1 1 25 5 0.44 Q4 5.8 1 1 48 5 249.4 The calculation results in Table 4 show that the heat emitted into the air through rock conduction in the study area's geothermal field per unit time is: 108.36 mW/m 2 × 3.543 × 10 7 m 2 = 3.839 × 10 9 mW = 917.57 kcal/s.Therefore, the heat emitted by the geothermal field in the study area is 1167.97 kcal/s = 1.54 × 10 14 J/a, which is equivalent to 5260 t/a of standard coal. 5.2 Geothermal reservoir volume method The thermal storage method is based on treating the hot reservoir rock and the fluid in its pores as a whole, using the local annual average temperature as the reference temperature to calculate the geothermal resources in the entire hot reservoir. The calculation formula is as follows: (Formula 2) In the formula: is the heat stored in the geothermal reservoir, J; is the area of the calculation zone, m 2 ; is the thickness of the geothermal reservoir, m; is the density of the geothermal reservoir rock, kg/m 3 ; is the specific heat of the geothermal reservoir rock, J/kg·℃; is the porosity of the geothermal reservoir rock, dimensionless; is the reservoir temperature, ℃; is the local annual average temperature, °C; is the geothermal water density, kg/m 3 ; is the hydraulic conductivity, dimensionless; is the height above the calculation starting point, m; is the specific heat of water, J/kg·°C. Table 5. Statistics of thickness of thermal pore cap rock in various places Numbering J28 J31 J35 J36 AKT1 AKT2 AKT3 ZK1 ZK2 ZK3 KC2 KC1 Overburden thickness 100 400 80 200 140 440 200 440 38 38 60.1 346.9 Table 6. Thermal storage method calculation parameters table Parameter name Parameter symbol Parameter value Parameter unit Data source Geothermal field area A 35.43 km 2 Area of geothermal fields identified above Thermal storage thickness d 1793.1 m Take the lower limit of the calculated evaluation depth (2000 m) minus the average thickness of the overburden of each geothermal borehole (see Table 6) to obtain the thickness of the overburden of the geothermal field = 2000 - 206.9 = 1793.1 m Thermal storage temperature t r 60.54 ℃ Take the average reservoir temperature Base temperature t 0 9.83 ℃ Take the temperature of the constant temperature layer as 9.83°C Rock voidage φ 3.73 Dimensionless The weighted average method was used to calculate the average porosity of the geothermal reservoir rocks in wells ZK1 and ZK2 Geothermal water density ρ w 983.1 kg/m³ The density of water at 60°C Specific heat of geothermal water c w 4180 J/(kg·℃) Specific heat of water Specific heat of a thermally stored rock c r 733.58 J/(kg·℃) Take the average saturated specific heat capacity of sedimentary rocks, metamorphic rocks, and igneous rocks in the hot reservoir Thermal reserve rock density ρ r 2650 kg/m³ Average rock density in the Cambrian-Carboniferous period in the study area Calculation starting point Above Height H 193.92 m Overburden thickness (206.9 m) minus average geothermal water buried depth (12.98 m) Hydraulic conductivity S 0.000184 Dimensionless Take the average value of the water storage coefficient calculated using the Tese formula for geothermal wells ZK1, ZK2, and ZK3 in the study area Remarks:The lower limit depth for calculating and evaluating the thermal storage method should be determined based on comprehensive considerations such as local economic development, geothermal resource extraction technology conditions, and the economic benefits of geothermal utilization. In this study, the evaluation depth of geothermal resources in the geothermal field was set at 2,000 m as the evaluation benchmark.The reservoir temperature was taken as the maximum depth of the borehole (1404.4 m) as the boundary. The upper layer used the average temperature of the geothermal reservoir actually revealed by drilling, which was 52.66°C, and the lower layer used the geothermal gradient (3.81°C/100 m) to calculate the average temperature of 68.41°C. The average of the two layers was taken as 60.54°C.The weighted average porosity of the hot reservoir (the average porosity of the intact section of the study area is 1.89%, and the average porosity of the fractured section is 6.5%; the fractured layers of the ZK1 and ZK2 drill holes account for 18.5% and 60.6%, respectively. The average thickness of the fractured layer of the geothermal field accounts for 40%) is 3.73%. According to calculation formula 2 and Table 7, the water storage in the geothermal reservoir of Wenquan County is 2.37×10 9 km 3 , the heat stored in the water is 4.9×10 17 J, the heat stored in the rock is 6.029×10 18 J, and the geothermal resources of the geothermal field are 6.52×10 18 J, which is equivalent to 2.23×10 8 t of standard coal. 5.3 Comprehensive determination Since the spring water points selected for the Surface heat flow method calculation were concentrated in the southeastern part of the study area, the calculation results had excessive errors.At the same time, compared with the thermal storage method, which calculates heat energy reserves directly based on the reservoir temperature, volume, and physical properties measured from boreholes, the surface heat flow method estimates the heat loss rate of deep geothermal reservoirs through heat transfer theory. It is highly dependent on shallow geothermal gradients and rock thermal conductivity and is greatly affected by the surface environment, resulting in inaccurate estimates.Relatively speaking, the thermal storage method provides more accurate results, and the geothermal resources of the study area were finally determined to be 6.52×10 18 J/a. 6. CONCLUSIONS The geothermal anomaly area in Wenquan County is an irregular fan-shaped area covering 58.05 km 2 , with a geothermal field area of 35.43 km 2 .The geothermal reservoir is controlled by NE-trending fault structures and exhibits a banded distribution.The rock formations distributed along the structures during the Devonian and Carboniferous periods are the main geothermal reservoirs in the study area,the buried depth ranges from 61.53 to 1295.5 m.Drilling revealed that the reservoir temperature ranges from 33 to 70.1°C, which belongs to low- and medium-temperature geothermal resources. The maximum circulation depth of geothermal water in the geothermal field of Wenquan County is 3401.1 m. The heat source mainly comes from the heat of the upper mantle lava flow, and the heat is supplied to the geothermal reservoir through faults and magma intrusion contact zones.The geothermal reservoir is continuously distributed, buried at a shallow depth, and locally directly overflowing onto the surface. The thickness of the overburden is no more than 500 m, and the overall analysis of the well depth for geothermal resource development is around 900 to 1400 m. The well construction cost is relatively low, which has significant economic development advantages. The geothermal field in Wenquan County is a small low-temperature geothermal field. The geothermal resources were calculated using the surface heat flow method and the thermal storage method, with results of 1.54×10 14 J/a and 6.52×10 18 J/a, respectively. Compared with the surface heat flow method, the thermal storage method is more accurate, so the geothermal resources were taken as 6.52×10 18 J/a.If geothermal resources are developed and utilized reasonably, approximately 2.23×10 8 t of standard coal can be saved each year, which is of great significance for saving fossil energy and reducing carbon emissions. Declarations Funding This research is supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region of China(2022D01C360,2022B03017-4), Young Top Talent Project of Xinjiang Uygur Autonomous Region of China(2023TSYCCX0010), 2025 Ximjiang Uygur Autonomous Region Graduate Student Research Innovation Project(XJ2025G101), 2025 National Student Innovation Training Program Project of Xinjiang University, 2024 Professional Degree Graduate Student Teachimg Case Bank Construction Proiect of Xinjiang University. Author contributions Chenhao Sun: conceived this study and wrote the paper. Kai Chen and Mengmeng Zhao: propose research ideas and participate in paper revision and refinement. Xinchang Wei, Zhilong Qi, Yalu Wang, and Ying Xi: collated data. All authors read and approved the final manuscript. Availability of data and materials Thermal storage parameters used in this study are included in this published article and available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. REFERENCES Isabel Pérez Martínez, Ruth Esther Villanueva Estrada, Claudio Inguaggiato, Mario Alberto Hernández Hernández, Giovanni Sosa Ceballos. Origin of fluids in the Araró-Simirao geothermal system, Central Mexico[J]. Journal of Geochemical Exploration, 2025, 269 107637-107637. Loïc Peiffer, Claudio Inguaggiato, Jobst Wurl, John M. Fletcher, Maria Guadalupe Olguín Martínez, Daniel Carbajal Martínez, Denis Legrand, Pablo Hernández Morales, Carlos E. Reinoza, Boris Chako Tchamabé, Lilia Arana Salinas, Rodolfo Silva Casarín. Geochemistry of coastal geothermal systems from southern Baja California peninsula (Mexico): Fluid origins, water-rock interaction and tectonics[J]. Chemical Geology, 2024, 670 122316-122316. Wang G L, Lin W J. Formation mechanisms and genetic models of major hydrothermal geothermal systems in China[J]. Acta Geologica Sinica, 2020, 94(7): 1923-1937. Mao X, Wang X W, Guo S Y, Bao Z D. Formation mechanism of geothermal resources in Gaoyang geothermal field and its adjacent areas[J]. Carsologica Sinica, 2021, 40(2): 273-280. Luo S Q, Namdawa, Li H, Cheng F J, Gesang Pingcuo, Luosong Qunpei. Geological structural characteristics and formation mechanism of Chazi Geothermal Field in Shigatse City, Tibet[J]. Acta Geologica Sichuan, 2020, 40(1): 30-33. Liu D M, Zhang C S, Lu W L, Wei M H, Qi Y Y, Liu F, Zhao Y, Jiang H. Enrichment background and formation mechanism of medium-deep geothermal resources in Fenwei Graben[J]. Earth Science Frontiers, 2025, 32(1): 367-379. Ning Y W. Geothermal characteristics and genetic mechanism of Neihuang Uplift[D]. Zhengzhou University, 2022. Du J, Cai N B, Zhang B J, Wang K Y, He L H, Liao F C. Heat source mechanism and genetic model of hot dry rock formation in Reshuixu geothermal field, Hunan Province[J]. Coal Geology & Exploration, 2024, 52(1): 70-82. Dong D Z, Liu X L. Formation mechanism and development-utilization of geothermal resources in Linfen Basin[J]. Energy and Energy Conservation, 2022(7): 57-61. Wang G L, Zhang W, Liang J Y, Lin W J, Liu Z M, Wang W L. Evaluation of geothermal resource potential in China[J]. Acta Geoscientica Sinica, 2017, 38(4): 449-450+134+451-459. Ma F, Wang G L, Zhang W, Zhu X, Zhang H X, Yue G F. Thermal reservoir spatial structure and resource potential of Rongcheng geothermal field in Xiongan New Area[J]. Acta Geologica Sinica, 2020, 94(7): 1981-1990. Han J, Liu H, Zhen Z, Zhu P F. Application of Monte Carlo method in geothermal resource assessment[J]. Journal of Geology, 2022, 46(2): 158-164. Ren S S. Evaluation of geothermal resource potential of Paleogene in Baxian Sag, Jizhong Depression[D]. Northeast Petroleum University, 2020. Yang Y Z. Characteristics of Guantao Formation thermal reservoir and evaluation of geothermal resources in Cangzhou section, Huanghua Depression[D]. China University of Geosciences (Beijing), 2020. Zhou J F. Evaluation of geothermal resources and utilization direction in Bunanbao geothermal field based on thermal reservoir method[J]. Modern Mining, 2023, 39(10): 52-55+60. Brown Christopher S.. Regional geothermal resource assessment of hot dry rocks in Northern England using 3D geological and thermal models[J]. Geothermics, 2022, 105 Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Québec, QC, Canada, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Québec, QC, Canada, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Québec, QC, Canada, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Québec, QC, Canada, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Québec, QC, Canada. Geothermal resource assessment of remote sedimentary basins with sparse data: lessons learned from Anticosti Island, Canada[J]. Geothermal Energy: Science – Society – Technology, 2020, 8 (Supplement): 24-39. Anthony E. Ciriaco, Sadiq J. Zarrouk, Golbon Zakeri, Warren I. Mannington. Refined experimental design and response surface methodology workflow using proxy numerical models for probabilistic geothermal resource assessment[J]. Geothermics, 2020, 88 María José Oviedo, Jasmin Raymond, Daniela Blessent, Stéphanie Larmagnat. Numerical assessment of the geothermal resource potential of the Charlevoix meteorite impact crater, Canada[J]. Geothermics, 2025, 127 103243-103243. Daniel Fariña González, Óscar García Afonso, Agustín M. Delgado Torres. Assessment of geothermal resources for power generation on La Palma Island, Canary Islands[J]. Geothermics, 2025, 127 103263-103263. Mohamed Ayed Elbalawy, Mohamed Balash, Mohamed Hamdy Eid, Ernő Takács, Felicitász Velledits. Innovative method integrates play fairway analysis supported with GIS and seismic modeling for geothermal potential evaluation in a basement reservoir[J]. Scientific Reports, 2025, 15 (1): 1325-1325. Sedara Samuel O., Alabi Olusegun O.. Heat flow estimation and quantification of geothermal reservoir of a basement terrain using geophysical and numerical techniques[J]. Environmental Earth Sciences, 2022, 81 (3): Zhao J, Guo Q H. Geothermal resource potential assessment based on 3D geological modeling: A case study of Shidian geothermal area[J]. Earth Science, 2023, 48(3): 1107-1117. Zhu Z Z, Lei X D, Wu X, Xu N X, Jiang X Y. Evaluation of geothermal resources in Changping New Town, Beijing based on 3D geological modeling[J]. Geoscience, 2020, 34(1): 207-214. Chen H W, Song R C, Zhang C, Liang Y, Wang Y C, Zheng F, Wang Y L, Wang H H. Thermal reservoir evaluation of hot dry rock geothermal resources based on factor analysis method[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2023, 50(3): 333-350. Hu J, Wang D C, Xu L, Liu Q, Leng J G, Gao Y, Zhao Y J, Yin C. Integrated information exploration and potential evaluation of medium-deep geothermal resources in Zhumadian area, Henan Province[J]. Science Technology and Engineering, 2025, 25(1): 67-75. Bian Y, Yang Y P, Li M, He X, Tang H, Sun A, Ju X. Application of thermal infrared remote sensing technology in geothermal resource exploration[J]. China Mining Magazine, 2021, 30(S2): 153-157. Zhu B Q, She Y H, Zhu L X. A promising geochemical method for geothermal exploration[J]. Geophysical and Geochemical Exploration, 1983(5): 258-265. Li P P, Chen H L. Application of geophysical exploration in geothermal exploration[J]. Mineral Exploration, 2019, 10(4): 938-942. Zhang Y Y, Pan G, Zhu P, Yao W J, Cui Y Y, Xue W M. Application of CSAMT in geothermal exploration of Moon Bay, northern Jiangsu[J]. Mineral Exploration, 2020, 11(6): 1228-1233. Additional Declarations No competing interests reported. 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Map generated using ArcMap (https://desktop.arcgis.com/zh-cn/desktop/index.html).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/ea637b4cdaa04c7a4d54835a.png"},{"id":91419914,"identity":"ac2bd87d-e64b-40c5-ab38-d7aabcfed0bf","added_by":"auto","created_at":"2025-09-16 10:04:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":400208,"visible":true,"origin":"","legend":"\u003cp\u003eRegional structure map of the study area. Map generated using MapGIS (https://www.mapgis.com/).\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/4880362487c758d03631b016.jpeg"},{"id":91419503,"identity":"eae3ce94-c94a-4666-bc87-d5908f758d82","added_by":"auto","created_at":"2025-09-16 09:56:16","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRemote sensing interpretation flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/b7159b74c4fdefcac2e92df6.jpeg"},{"id":91421087,"identity":"e6e0aa07-42c4-453b-b269-bbee2c1c5c03","added_by":"auto","created_at":"2025-09-16 10:12:16","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165538,"visible":true,"origin":"","legend":"\u003cp\u003eRemote Sensing Interpretation Map of Geothermal Anomaly Range in the Study Area. Map generated using MapGIS (https://www.mapgis.com/).\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/48f4b2e5746d110e0747b520.jpeg"},{"id":91419913,"identity":"efd17115-c306-4489-aef4-4ad691cc3409","added_by":"auto","created_at":"2025-09-16 10:04:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92191,"visible":true,"origin":"","legend":"\u003cp\u003eInversion temperature and measured temperature trend analysis chart\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/a97af4233374a192df96c627.png"},{"id":91419502,"identity":"4febcfdf-d15e-49e2-899f-5bdf940cb7a8","added_by":"auto","created_at":"2025-09-16 09:56:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":99962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe linear relationship between the inversion temperature and the measured temperature\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/9d82a973bcafbc9eda666996.png"},{"id":91419511,"identity":"7fc5fed2-bbb9-41e8-ad39-c8e9f0e5af37","added_by":"auto","created_at":"2025-09-16 09:56:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1380564,"visible":true,"origin":"","legend":"\u003cp\u003eGeochemical exploration delineated geothermal anomaly range map. Map generated using MapGIS (https://www.mapgis.com/).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/762cda0188c2ffe1aa52ba98.png"},{"id":91419506,"identity":"4df97133-11bc-471f-bb9a-0c62c07772ea","added_by":"auto","created_at":"2025-09-16 09:56:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":211810,"visible":true,"origin":"","legend":"\u003cp\u003eGeophysical exploration speculated fracture outline map. Map generated using Surfer (https://www.goldensoftware.com/products/surfer/).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/21846156b5f062409fccd767.png"},{"id":91421088,"identity":"4dd7a2be-be64-4b79-8bf7-90ae967ef1d2","added_by":"auto","created_at":"2025-09-16 10:12:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1698093,"visible":true,"origin":"","legend":"\u003cp\u003eGeothermal field map of the study area. Map generated using ArcMap (https://desktop.arcgis.com/zh-cn/desktop/index.html).\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/aec84e3d2585cea98a07bfea.png"},{"id":91419920,"identity":"080911c7-2732-444c-997e-962ce8a03df2","added_by":"auto","created_at":"2025-09-16 10:04:16","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":145686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual model diagram of banded heat reservoir\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/88c03097dbe70199a11fb1e3.jpeg"},{"id":91419509,"identity":"70f27465-c64d-4ea3-8b9b-bc4380f02219","added_by":"auto","created_at":"2025-09-16 09:56:16","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":213579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThree-dimensional geothermal genesis model diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/82461cdebfe518ac0c17fe7a.jpeg"},{"id":91419918,"identity":"081d9cf4-50f7-4be4-99ea-cc2737241809","added_by":"auto","created_at":"2025-09-16 10:04:16","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":150521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKC1 borehole temperature measurement curve diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/a1ee7da10e0b7b74dfe3173f.png"},{"id":91419513,"identity":"512a2be4-3c39-4f64-a67e-fdd11ea8bec1","added_by":"auto","created_at":"2025-09-16 09:56:16","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":165503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePiper trilinear map of geothermal water in the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/195d52e4d0e895fa3df085ff.png"},{"id":97895829,"identity":"7d14fb85-e277-4bf6-af0d-bd467f38088d","added_by":"auto","created_at":"2025-12-10 15:35:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10020851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/dbac011b-e3e4-4ee4-b2a5-4b2028fe1f7f.pdf"},{"id":91421086,"identity":"5fba76b2-c2fa-4ddb-9a97-e0bca8c630c4","added_by":"auto","created_at":"2025-09-16 10:12:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35844,"visible":true,"origin":"","legend":"","description":"","filename":"Rawdata.docx","url":"https://assets-eu.researchsquare.com/files/rs-7251739/v1/bd97128432aa669fe27de69d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genesis Mechanism and Resource Evaluation of Low-Temperature Hydrothermal Geothermal Fields in Wenquan County, Xinjiang","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAs a low-carbon, clean, and renewable energy source, geothermal resources can not only effectively reduce dependence on fossil fuels, but also significantly reduce greenhouse gas emissions, and are of strategic importance in global energy transition and sustainable development.At the same time, accelerating the development and utilization of geothermal energy is of great significance for achieving carbon peak and carbon neutrality goals.\u003c/p\u003e\n\u003cp\u003eThe mechanism of geothermal formation is the theoretical basis for geothermal resource exploration and development. Foreign scholars Isabel P\u0026eacute;rez Mart\u0026iacute;nez et al. (2025) analyzed the fluid and gas geochemical characteristics of the Arar\u0026oacute;-Simirao geothermal system in Mexico and revealed the mechanism by which geothermal fluids rise through fracture zones in this geothermal field.Lo\u0026iuml;c Peiffer et al. (2024) analyzed the geochemical characteristics of hydrothermal fluids at the southern tip of the Baja California Peninsula and revealed the fluid sources, water-rock interactions, and tectonics of two geothermal systems in the coastal region.Currently, domestic research on the formation mechanism of geothermal energy mainly focuses on four aspects: heat source, reservoir, channel, and overburden (Wang Guiling et al., 2020).Mao Xiang et al. (2021) revealed the origin of the Gaoyang geothermal field through the analysis of geological structure, temperature field, and water chemistry characteristics, combined with fracture systems and groundwater circulation paths. Luo Shaoqiang (2020) analyzed the formation background, occurrence conditions, and distribution patterns of geothermal resources in the Chazi geothermal field by combining the regional geological background with the geological and geophysical field research results of the geothermal field.Liu Deming et al. (2025) constructed a four-dimensional model integrating regional tectonic evolution analysis, deep geophysical exploration, fracture system thermal conductivity analysis, and thermodynamic evaluation of the reservoir and overburden to reveal the formation mechanism of deep geothermal resources in the Fen-Wei Graben.Ning Yiwu (2024) revealed the geothermal origin of the Neihuang Uplift through geophysical exploration, hydrogeochemical, and isotope tracing, elucidating the \u0026ldquo;western precipitation recharge-deep heat conduction-tectonic fracture transport-sedimentary layer insulation\u0026rdquo; mechanism of geothermal formation.Du Jiang et al. (2024) revealed the formation mechanism of dry hot rocks in the Rehuo Geothermal Field in Hunan Province through geochemical temperature indicators, radioactive heat generation rate analysis, and multidisciplinary detection using gravity, magnetism, and seismic methods.Dong Dongzhu et al. (2022) revealed the formation mechanism of geothermal resources in the Linfen Basin through hydrogeological mapping, remote sensing interpretation, comprehensive geophysical exploration, drilling, and water quality analysis.\u003c/p\u003e\n\u003cp\u003eGeothermal resource evaluation can quantify resource potential and is the basis for geothermal energy development and utilization. Commonly used evaluation methods mainly include the thermal storage method, numerical generation, and the Monte Carlo method (Wang Guiling et al., 2017; Ma Feng et al., 2020; Han Jun et al., 2022).Due to its advantages of simple calculation, wide applicability, and high cost-effectiveness compared with other methods, the thermal storage method is widely used in the evaluation of geothermal resources (Ren Shuai, 2020; Yang Yunzhan, 2020; Zhou Jianfei, 2023).Foreign scholar Brown Christopher S. (2022) utilized a combination of three-dimensional geological and thermal models with the volume method to complete a potential evaluation of dry hot rocks in northern England; Violine Gascuel et al. (2020) developed a 3D temperature model for the sedimentary basin of Anticosti Island, Canada, and its underlying Precambrian basement, and completed a geothermal resource evaluation for areas with sparse data.Anthony E. Ciriaco et al. (2020) established a proxy numerical model using an improved experimental design and response surface method (ED/RSM) in the Ohaaki and Rotorua geothermal fields. The calculation results were consistent with the traditional volume method, providing an efficient solution for geothermal resource probability assessment.Mar\u0026iacute;a Jos\u0026eacute; Oviedo et al. (2025) conducted a geothermal resource potential assessment of the Charlevoix meteorite impact crater in Canada using numerical simulation methods. Daniel Fari\u0026ntilde;a Gonz\u0026aacute;lez et al. (2025) combined random Monte Carlo simulation methods with the well-known geothermal resource assessment volume method to complete a geothermal resource assessment for power generation on the island of La Palma in the Canary Islands.Mohamed Ayed Elbalawy et al. (2025) established a geothermal favorable zone analysis (GPFA) model, integrated 3D seismic interpretation, GIS technology, and 3DHIP calculations, and identified the optimal exploration and development areas in B\u0026eacute;k\u0026eacute;s County, Hungary, for the first time, assessing the geothermal potential.Sedara Samuel O. et al. (2022) used a combination of geophysical and numerical simulation methods to assess the geothermal resource potential of the Ikogosi hot spring area (IKGWS) in Nigeria. In recent years, Chinese scholars have improved the thermal storage method for calculating geothermal resources based on three-dimensional modeling, enabling more accurate calculations of geothermal resources in different areas studied (Zhao Jie et al., 2023; Zhu Zhenzhou, 2019).Chen Haiwen et al. (2023) used factor analysis to extract independent influencing factors and conducted an evaluation study of 11 dry hot rock areas in China. Hu Jian et al. (2025) conducted a study of the geothermal geological characteristics, resource evaluation, and favorable area prediction of the Zhumadian area based on the geological features of the Henan Zhumadian region and combined with two-dimensional seismic interpretation results.Therefore, the methods used to calculate and study geothermal resources at home and abroad are becoming increasingly diversified based on previous research methods.\u003c/p\u003e\n\u003cp\u003eCurrently, research and technology for the development and utilization of geothermal resources are generally mature in China, but geothermal research in Xinjiang is still in its infancy. Except for the Shengshan geothermal field, the level of exploration in other areas of Wenquan County is generally low, which has certain limitations on the development and utilization of geothermal resources in the study area.Therefore, based on a systematic analysis of the geothermal geological conditions in the study area, this paper mainly analyzes the formation mechanism of geothermal resources and calculates their reserves, thereby providing a scientific basis for the development and utilization of geothermal resources in Wenquan County.\u003c/p\u003e"},{"header":"2. GEOTHERMAL GEOLOGICAL BACKGROUND OF THE STUDY AREA","content":"\u003ch2\u003e2.1. Natural geographical conditions\u003c/h2\u003e\n\u003cp\u003eThe study area is located in the north of Wenquan County, Xinjiang, in the mountain valley between Alatao Mountain and Biezhentao Mountain. The main landforms include eroded hills, erosion valleys, impact valleys, alluvial plains, lacustrine plants, and diluvial plains. The study area is located in the heart of the Eurasian continent and has a typical continental temperate semi-arid climate.The study area is 29.25 km long, 13.65 km wide, and has an area of 399.26 km\u003csup\u003e2\u003c/sup\u003e, as shown in Fig.1.\u003c/p\u003e\n\u003ch2\u003e2.2 Regional geological conditions\u003c/h2\u003e\n\u003cp\u003eThe igneous rocks in the study area are mainly Late Mesoproterozoic granites, Mid-Variscan granites, and vein rocks, with an exposed area of about 120 km\u003csup\u003e2\u003c/sup\u003e. Metamorphic rocks are mainly distributed in Biezhentao Mountain south of Wenquan County and Alatao Mountain in the north, extending in an east-west direction consistent with the regional structural line, with an exposed area of approximately 190 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe study area is located in the western part of the North Tianshan Mountains and belongs to the Junggar-North Tianshan Fold Belt (II) and Tianshan Fold Belt (III) in terms of tectonic unit division. Specifically, it includes the North Tianshan Yudi Graben Fold Belt (II\u003csub\u003e3\u003c/sub\u003e) and the Borokonu Graben Fold Belt (III\u003csub\u003e1\u003c/sub\u003e) in terms of secondary tectonic units.A total of 20 fractures have developed in the study area. Based on the degree of influence of the geothermal system, they can be divided into three levels. Level I fractures are the main heat-controlling and water-conducting fractures, Level II fractures are the main fractures that control the distribution of the structural zone and have a certain influence on the migration of geothermal fluids, and Level III fractures are secondary fractures that restrict the distribution of the structural zone or local structural development, as shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. A list of fault structures in the study area\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eClassification of fracture systems\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumbering\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eStrike\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eAzimuth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eFault characteristics\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eTendency\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eInclination angle\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eFracture level\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eEast-west compressional-extensional fault system\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eApproach EW\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e85°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown nature\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e80°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eLevel I\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eApproach EW\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e85°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtensional concealed normal fault\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e65°±\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eApproach EW\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e70°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eCompressive concealed reverse fault\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e75°±\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF3-1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eApproach EW-NW\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e90°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtensional concealed normal fault\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e80°±\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF12\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eApproach EW-NW\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e85°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown nature\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e60°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel II\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eNorthwest-trending normal fault or unknown nature\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNW-SE\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e290°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtensional concealed normal fault\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e70°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLevel I\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF4-1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eApproach EW\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e85°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtensional concealed normal fault\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e45°±\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNW-SE\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e290°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtensional concealed normal fault\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e85°±\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF6-F11and F13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNW-SE\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e290°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown nature\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouthwest\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e65°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel III\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNortheast-southwest structural system\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF15、F16、F17\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNW-SE\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e45°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown nature\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e50°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel I\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eF18\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNW-SE\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e45°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnknown nature\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e75°±\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eLevel I\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e"},{"header":"3. DELIMITATION OF THE GEOTHERMAL FIELD IN THE STUDY AREA AND ANALYSIS OF ITS GEOTHERMAL RESERVOIR CHARACTERISTICS","content":"\u003ch2\u003e3.1 Delimitation of geothermal field\u003c/h2\u003e\u003cp\u003e(1) Remote sensing interpretation\u003c/p\u003e\u003cp\u003eThermal infrared remote sensing technology captures surface thermal radiation information through sensors and uses atmospheric correction methods, single-channel algorithms, and split-window algorithms to estimate surface temperature (Bian Yu et al., 2021).In this study, WI-DINSAR technology was employed to interpret the geological structure of the study area using eight PALSAR and two ALOS2 data sets from October 2017 to May 2018. Additionally, the temperature-emissivity separation algorithm was applied to 12 ASTER data sets from the summer (June–August) and winter (December–February) of 2007 to perform temperature inversion and thermal infrared interpretation. The specific remote sensing interpretation process is shown in Fig.3.\u003c/p\u003e\u003cp\u003eUsing WI-DINSAR technology, active faults and fractures within the study area were interpreted. The temperature-emissivity separation algorithm was used to obtain the distribution pattern of the surface temperature field. Comprehensive remote sensing information analysis was conducted by integrating thermal infrared spectral information and remote sensing structural interpretation information. The interpretation results are shown in Fig.4.\u003c/p\u003e\u003cp\u003eAs shown in Fig.4, five faults were interpreted in the study area. After excluding false anomaly areas, five geothermal anomaly areas with a total area of 5.928 km\u003csup\u003e2\u003c/sup\u003e were delineated. Furthermore, the remote sensing interpretation map shows that the anomaly areas are mainly distributed around two east-west structures, suggesting that the east-west structure in the central part is the main heat-conducting structure in the study area.\u003c/p\u003e\u003cp\u003eAt the same time, to verify the effectiveness of the temperature inversion results, the inverted temperatures were compared with the actual measured temperatures, and trend analysis and linear correlation analysis were performed, as shown in Fig.5 and Fig.6.\u003c/p\u003e\u003cp\u003eAs can be seen from Fig.5 and Fig.6, the temperatures derived from thermal infrared inversion in the study area generally follow the same trend as the measured temperatures, and there is a good linear correlation between the measured temperatures and the inverted temperatures.\u003c/p\u003e\u003cp\u003e(2) Geochemical prospecting\u003c/p\u003e\u003cp\u003eStudies have shown that elements such as mercury, arsenic, stibium, and bismuth are widely enriched in hot spring sediments, and their distribution patterns have geochemical significance for delineating the boundaries of geothermal fields(Zhu Bingqiu et al., 1983). Given the complex topography of the study area and significant differences in soil layer depths across different topographic units, 262 sampling points were established within the study area, and soil samples were collected at depths of 0 m, 1 m, and 2 m, the correlation coefficients for the four elements at the three depths were calculated and are presented in Table 2.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 2. Statistical table of soil elements in the study area\u003c/strong\u003e\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eQuantity contained\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eElemental\u003c/p\u003e\n \u003c/td\u003e\u003ctd colspan=\"3\" valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eElemental content in soil (μg/g)\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0m\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1m\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2m\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eHg\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMaximum values\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMinimum value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAverage value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAbnormal lower value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eAs\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMaximum values\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e33..4\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMinimum value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAverage value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e13.45\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e12.64\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e12.78\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAbnormal lower value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e23.42\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e24.70\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e34.88\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eSb\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMaximum values\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMinimum value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAverage value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAbnormal lower value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBi\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMaximum values\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eMinimum value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAverage value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAbnormal lower value\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003eAs can be seen from Table 2, the elements show good correspondence at depths of 0 m, 1 m, and 2 m, with a relatively uniform planar distribution pattern. However, the correspondence between different elements in abnormal zones at the same depth is not obvious, with only a few areas showing a match between the two element abnormal zones.\u003c/p\u003e\u003cp\u003eTo study the overlap and adjacency of abnormal areas of different elements in a more intuitive and detailed manner, the abnormal areas of four elements (mercury, arsenic, stibium, and bismuth) at the same soil depth were mapped on a 1:10,000 scale map, as shown in Fig.7.\u003c/p\u003e\u003cp\u003eAnalysis of the distribution of abnormal regions in Fig.7 reveals that, in the element anomaly distribution maps at depths of 1 m and 2 m, the individual element anomaly zones are primarily concentrated around Shengquan Mountain, consistent with the existing hot spring distribution patterns, thereby confirming the accuracy of the test results;In the element anomaly distribution map at a depth of 0 m, there are two locations: one north of the main urban area of Hot Spring County, with an area of 39.7 m\u003csup\u003e2\u003c/sup\u003e, showing a mercury-bismuth element anomaly zone; and another west of Shengquan Mountain, with an area of 3807 m\u003csup\u003e2\u003c/sup\u003e, showing an arsenic-bismuth element anomaly zone. These two areas are designated as geothermal anomaly zones.\u003c/p\u003e\u003cp\u003e(3) Geophysical exploration\u003c/p\u003e\u003cp\u003eWhen exploring deep thermal reservoirs and tectonic structures, techniques such as magnetotelluric (MT) methods, audio-frequency magnetotelluric (AMT) methods, controlled-source audio-frequency magnetotelluric (CSAMT) methods, and seismic exploration are commonly employed (Li Pingping et al., 2019; Zhang Yangyang et al., 2020).This study utilized two geophysical methods, magnetotelluric tomography (MT) and controlled-source audio-frequency magnetotelluric tomography (CSAMT), to infer the morphology of hidden structures and the distribution of faults at depths of 10–15 km in the study area, as shown in Fig.8.\u003c/p\u003e\u003cp\u003eA total of seven faults (F1, F2, F3, F3-1, F4, F4-1, and F5) were identified.By comparing the remote sensing interpretation results of the fault structures in Fig.8 and Fig.4, it can be observed that the fault locations are almost identical to the remote sensing interpretation fault locations.Based on the principle that the stronger the gradient of electrical resistivity differences, the greater the depth, and the larger the range of resistivity influence, the larger the scale of the fault, it can be concluded that F1, F2, F3, and F4 are relatively larger in scale, followed by F3-1, F4-1, and F5. These findings provide foundational data for delineating deep geothermal anomalies in the study area.\u003c/p\u003e\u003cp\u003e(4) Geothermal field area\u003c/p\u003e\u003cp\u003eBy integrating remote sensing, geochemical, and geophysical data, the boundaries of the geothermal anomaly zone in the study area can be delineated as follows: to the north, extending to the F1 fault; to the south, extending to the F4 and F4-1 faults; to the west, based on thermal infrared anomalies and geophysical data, the boundary of the basement uplift is inferred; and to the east, extending to the eastern boundary of the Shengshan uplift. The total area is 58.05 km², as shown in Fig.9.\u003c/p\u003e\u003cp\u003eBased on the geothermal anomaly zone, combined with drilling data, using 15°C (6–12°C higher than the background water temperature) as the benchmark, and comprehensively considering factors such as reservoir temperature, burial depth, tectonic structure, and aquifer boundaries, the final geothermal field was delineated as follows: to the north, extending to the upper plate of the F2 fault; to the south, extending to the upper plate of the F4 and F4-1 faults;westward to the east side of Haotuerha Village, and eastward to the upper part of the F5 fault, forming a long, narrow strip with an area of 35.43 km², as shown in Fig.9.\u003c/p\u003e\u003ch2\u003e3.2 Geothermal reservoir characteristics and distribution\u003c/h2\u003e\u003cp\u003eResearch shows that heat transfer in the geothermal reservoir in this area is mainly convective, extending in strips on a plane. It is a low temperature convective type strip geothermal reservoir composed of fracture zones with effective porosity and permeability. A conceptual model is shown in Fig.10.\u003c/p\u003e\u003cp\u003eAs can be seen from Fig.10: the distribution of geothermal reservoirs in the study area is closely related to fracture zones, and the water content of hot reservoirs is extremely uneven, consisting of rock formations from the Cambrian and Carboniferous periods.Based on the buried depth and thickness of the hot reservoir, the development of fractures, physical properties, thermophysical properties, reservoir temperature, and water content, it was comprehensively determined that the rock groups distributed along the Devonian-Carboniferous period structure are the main hot reservoirs in the study area. The specific characteristics are as follows.\u003c/p\u003e\u003cp\u003e(1) Cambrian period hot reservoirs\u003c/p\u003e\u003cp\u003eThis geothermal reservoir is distributed near the F4 fracture zone on the south side of Shengshan Mountain, with a buried depth of 916.2 to 1355.9 m and a single layer thickness of 169.4 m. The rock type is mainly foliated sandstone and metamorphic siltstone.The fracture development zone accounts for 9.9%, with porosity ranging from 1.43% to 13.92% and permeability from 0.1×10\u003csup\u003e-3\u003c/sup\u003e to 24.45×10\u003csup\u003e-3\u003c/sup\u003e μm\u003csup\u003e2\u003c/sup\u003e, indicating poor water-bearing capacity.The reservoir temperature ranges from 48.9 to 57°C, the geothermal gradient ranges from 1.24 to 3.3°C/100 m, and the rock thermal conductivity ranges from 2.632 to 3.384 W/(m·K), reflecting good thermal conductivity but weak storage and infiltration capacity.\u003c/p\u003e\u003cp\u003e(2) Ordovician period hot reservoirs\u003c/p\u003e\u003cp\u003eThe hot reservoir is distributed on both sides of the north-south uplift in the central part of Shengshan, with a buried depth of 20.22 to 546.84 m and a thickness of 77.4 to 388.2 m.The northern side is mainly quartz sandstone with intact rocks and poor water content. The southern side is affected by the F3-1 and F4 fractures, with 6.5% of the section developed with fractures, porosity of 1.58% to 11.33%, permeability of 0.11×10\u003csup\u003e-3\u003c/sup\u003e to 11.36×10\u003csup\u003e-3\u003c/sup\u003e μm\u003csup\u003e2\u003c/sup\u003e, and good water content.The reservoir temperature is 41.7 to 45.2°C, the geothermal gradient is 1.11 to 2.51°C/100 m, and the thermal conductivity is 1.707 to 3.598 W/(m·K), showing significant differences between the north and south.\u003c/p\u003e\u003cp\u003e(3) Devonian period hot reservoirs\u003c/p\u003e\u003cp\u003eThis hot reservoir is one of the shallowest hot reservoirs in the study area, with a buried depth of 61.53 to 526.12 m on the north side and 1085.6 to 1295.5 m on the south side, and a thickness of 26.2 to 407.5 m.The rock types include silty mudstone, altered tuff, and granite. The northern side has a 34.3% fracture development rate, a permeability of 0.1×10\u003csup\u003e-3\u003c/sup\u003e to 23.33×10\u003csup\u003e-3\u003c/sup\u003e μm\u003csup\u003e2\u003c/sup\u003e, and a reservoir temperature of 24.2 to 55.4°C.The temperature on the south side is 50.8 to 54.1°C, with a geothermal gradient of 0.86 to 6.2°C/100 m. Overall, the water content is average.\u003c/p\u003e\u003cp\u003e(4) Carboniferous period hot reservoirs\u003c/p\u003e\u003cp\u003eThis hot reservoir is mainly distributed in the F3 fracture zone on the north side of Shengshan, with a buried depth of 140 to 582.83 m and a thickness of 442.83 m. The rock type is mainly tectonic breccia and tuffaceous siltstone.The fracture development section accounts for 25.7%, with a porosity of 2.5% to 28.3%, permeability of 0.5×10\u003csup\u003e-3\u003c/sup\u003e to 30×10\u003csup\u003e-3\u003c/sup\u003e μm\u003csup\u003e2\u003c/sup\u003e, reservoir temperature of 41.3 to 69.9°C, and geothermal gradient of 2.53 to 5.15°C/100 m. It is the hot reservoir with the highest temperature and best permeability in the study area.\u003c/p\u003e"},{"header":"4. ANALYSIS OF GEOTHERMAL GENESIS MODELS IN THE STUDY AREA","content":"\u003cp\u003eThe geothermal field in the study area mainly forms convection through deep heat sources connected by fracture structures, and belongs to the low temperature convective type geothermal system.The mountains surrounding the basins, such as Alatao Mountain and Biezhentao Mountain, receive atmospheric precipitation and snowmelt, which are transported deep into the strata, absorbing heat and causing the temperature to rise continuously. When the temperature reaches a certain depth underground and the overburden conditions are favorable, low-temperature hot water is formed, which then rises along the fracture channel to the shallow surface and emerges as hot springs.The study area has a good geothermal system formation mechanism, as shown in Fig.11.\u003c/p\u003e\n\u003cp\u003eThrough the analysis of the geothermal genesis model of the study area in Fig.11, the characteristics of heat source, overburden, fluid, reservoir, and channel can be analyzed as follows.\u003c/p\u003e\n\u003cp\u003e(1) Heat sources: The main heat sources in the study area include three types: geothermal heat, tectonic friction heat, and heat generated by radioactive material decay.Among them, mantle heat generation is the main heat source in the study area. According to the temperature measurement curve of KC1 borehole (as shown in Fig.12), the average geothermal gradient in the study area is 7.4\u0026deg;C/100 m, and the geothermal heat flow value is 210.46 mW/m\u003csup\u003e2\u003c/sup\u003e, which is significantly higher than the average value of the northwestern margin of the Junggar Basin in Xinjiang, indicating that there is stable mantle heat flow heat generation in the study area.Tectonic friction heat is a secondary heat source in the study area, mainly due to the intense uplift of the Biezhentao Mountain and Alatao Mountain blocks and the relative subsidence of the Bortala block, resulting in active neotectonic movement.Radioactive decay heat is also a minor heat source in the study area. The average heat generation rate of igneous rocks in the study area is 1.262 \u0026mu;w/m\u003csup\u003e3\u003c/sup\u003e; that of metamorphic rocks is 1.774 \u0026mu;w/m\u003csup\u003e3\u003c/sup\u003e; and that of sedimentary rocks is 2.934 \u0026mu;w/m\u003csup\u003e3\u003c/sup\u003e, which is higher than that of granite in general, as shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Statistics on rock radiogenic heat generation in the study area\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eRock types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eDensity（kg/m\u003csup\u003e3\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eUranium（ug/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eThorium（ug/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eKalium（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eHeat production rate\u0026mu;w/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eAverage heat generation rate\u0026mu;w/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003einterval value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eaverage value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003einterval value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eaverage value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003einterval value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eaverage value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003einterval value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eaverage value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003eIgneous rock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eFine grained diorite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2740-2860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.4-0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.84-2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eFelsitic rock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2700-2870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4.3-12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e9.83-6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e8.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.07-2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAltered basalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAmphibole lamprophyre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003eMetamorphic rock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLeptynite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2710-2780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.1-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5.5-9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e7.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.09-2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eQuartz schist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2770-2780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.45-9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e7.14-10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.1-2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMetamorphic silty mudstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2540-2770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.6-6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.74-9.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e6.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.56-3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eFlake quartzite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eFelsic hornstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003eSedimentary rock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLithic sandstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2580-2770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.16-5.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1.2-10.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.99-2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eLithic feldspar sandstone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2570-2670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.9-97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e23.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3.24-21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e9.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.296-2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCrystalline vitric tuff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1850-2720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.73-74.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e13.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.33-\u003c/p\u003e\n \u003cp\u003e21.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e6.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.72-3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eMetamorphic argillaceous siltstone\u003c/p\u003e\n \u003cp\u003e(Sandy structure )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2590-2670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.72-4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.63-3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.769-2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eSilicarenite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(2) Overburden: The overburden of the geothermal field in the study area consists of Quaternary silty gravel, recent calcareous sandstone, and Devonian siltstone and tuffaceous sandstone with low thermal conductivity.It has low fracture rate, poor permeability, and poor water-bearing conditions. According to the geothermal well temperature measurement curve, the highest temperature of the stratum 0-358.9 m below the surface is 25.4 m, and the ground temperature increases slowly. Its thermal conductivity is 2.186-2.926 w/(m\u0026middot;℃), which is a good overburden for geothermal fields.\u003c/p\u003e\n\u003cp\u003e(3) Fluids: The main types of groundwater in the study area are bedrock fissure water, karst-fractured groundwater, and loose rock pore diving.The main sources of replenishment are atmospheric precipitation and snowmelt from the northern, southern, and western mountainous areas, torrential rains and floods, and river seepage.It enters the underground aquifer through surface infiltration and underground runoff, and is heated deep in the earth\u0026apos;s crust to form geothermal water, which continuously transfers heat from the lower layers to the surface or to a certain depth below the surface.The Piper diagram of geothermal fluids in the study area is shown in Fig.13. It can be seen that the chemical type of geothermal fluids in the study area is mainly SO\u003csub\u003e4\u003c/sub\u003e-Na type geothermal water.\u003c/p\u003e\n\u003cp\u003e(4) Reservoir: The hot reservoirs in the study area are spatially manifested as two types: strip-shaped geothermal reservoirs and layered geothermal reservoirs. They are mainly located in the structural fracture zone and are composed of a set of Paleozoic coastal and shallow marine sedimentary rocks, metamorphic rocks, and igneous rocks. The average heat flow value is estimated to be about 58 mW/m\u003csup\u003e2\u003c/sup\u003e, which is basically consistent with the geothermal background value of the area.\u003c/p\u003e\n\u003cp\u003e(5) Passageways: Fracture structures in the study area serve as key passageways for deep circulation of geothermal water. During the long process of tectonic evolution, especially during the Zhonghua-Lixi period, intense tectonic activity caused the rock layers to overturn, accompanied by the intrusion of large-scale medium-acidic igneous rocks, forming a nearly vertical fracture group (F17) running north-southeast-west.This fracture zone is well connected to the base\u0026apos;s thermal fracture (F3), and its structural intersection and fragmented area provide ample space for the storage and migration of geothermal fluids, not only providing an effective channel for the upward flow of geothermal fluids, but also creating favorable conditions for groundwater circulation and storage.\u003c/p\u003e"},{"header":"5. EVALUATION OF GEOTHERMAL RESOURCES IN THE STUDY AREA","content":"\u003ch2\u003e5.1 Surface heat flow method\u003c/h2\u003e\n\u003cp\u003eThe surface heat flow method estimates geothermal resource reserves based on the heat emitted from the surface of geothermal fields. This method is mainly used for the evaluation of geothermal resources in areas with low exploration levels. The calculation formula is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"263\" height=\"61\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;(Formula1)\u003c/p\u003e\n\u003cp\u003eIn the formula:\u003cimg width=\"9\" height=\"31\" src=\"data:image/png;base64,R0lGODlhDgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwAOABEAhAAAAAAAAAAAOgAAZgA6ZgBmtjoAADpmkDqQ22YAAGY6AGY6OmaQtma222a2/5A6AJBmOpC225Db/7ZmALZmOrbb/7b//9uQOtu2Ztu2kNvb///bkP/btv/b2///tv//2wVjICAC2RIEwlGNoqcIkchBQjN+z2CxwBQ4oksAwQNsDDpPQiAp4n5HXbEXKER3RaEVieX5Ckrm1EckF5XScJNlFh1hog5kyPtQDKcAITZ1odZTAE8DK4EiGAYCDBqGIhkQBgghADs=\" alt=\"image\"\u003e\u0026nbsp;is the heat emitted during a certain period of time, J;\u003cimg width=\"13\" height=\"31\" src=\"data:image/png;base64,R0lGODlhEwAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwARABEAhAAAAAAAAAAAOgA6ZgA6kABmtjoAADoAOjo6Ojo6ZjqQ22YAAGYAOmY6AGaQ22a2/5A6AJC225Db/7ZmALb//9v///+2Zv/bkP/btv//tv//2wECAwECAwECAwECAwECAwVcIBSMZBIBaJpmC1EBWBMoqqpBLsoKUo3eOdQk8PABgC8h0YjULXhM3AsjKhiPEAFiJHCoLoceNlizBKDjpC/DEDeNbDFrQLnGASKSdd2+8sV+K32BgoCBFwYjDyEAOw==\" alt=\"image\"\u003e\u0026nbsp;is the heat transferred through rock into the air per unit time, W;\u003cimg width=\"13\" height=\"31\" src=\"data:image/png;base64,R0lGODlhEwAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwASABEAhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAADo6Ojo6ZjpmtjqQ22YAAGY6AGY6kGaQ22a2/5A6AJC225Db/7ZmALZmZrb//9uQOtu2ttv///+2Zv/bkP/btv//tv//2wECAwVqYBSMZCIBaKp2TJEBXBMsau1FLsoKU53eORQlAPGhgC8h0QhA6hg8ppMjMjCbEQFiJHikNCPrEZdUbRSAzYGGDaowyUvOedXkWATLFXChiUhiPh0OZXsoFXqGKRdFiigabBqNVxckAQMWIQA7\" alt=\"image\"\u003e\u0026nbsp;is the heat emitted by hot springs, geysers, and fumaroles per unit time, W;\u003cimg width=\"4\" height=\"31\" src=\"data:image/png;base64,R0lGODlhBgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAAGAAwAgwAAAAAAADpmtmaQ22a2/5A6ALZmANuQOv+2Zv/bkP//tv//2wECAwECAwECAwECAwQdEIBDJFilShN0CWA3idJBjhpqmSlrIV0yAMonABEAOw==\" alt=\"image\"\u003e\u0026nbsp;is the calculation time period, s;\u003cimg width=\"27\" height=\"31\" src=\"data:image/png;base64,R0lGODlhKAAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwAoAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAOjo6ADo6kDpmkGYAAGYAZmaQ22a222a2/5A6AJA6OpDb/7ZmALZmOraQZrbb/7b/27b//9uQOtuQkNv///+2Zv/bkP//tv//2wWsIBSMgQEAGRkIiKoOl0iaZzoKEuAtBHb+E8HjtCtsfh2Gr8Pz/U6Txu8DMT5RuBPV+tP4isdrZqithn+ZLGB7foKvKPLaLFazTxz5Wyy/P9M5c1YfE3oLXH99Mi4rgVQuhohoipJYjnRxP3uJU5hodph5nZUAhaNtlmWkcwNOUxGBgqiAqqhoAXInHQlnOz1XQWS+rldUAg5aFQexiyU1KiwqNHAfFC0rChZPIQA7\" alt=\"image\"\u003e\u0026nbsp;is the average geothermal heat flow value = 3.81\u0026deg;C/100 m \u0026times; 2.844 W/m\u0026bull;\u0026deg;C = 108.36 mW/m\u003csup\u003e2\u003c/sup\u003e;\u003cimg width=\"9\" height=\"31\" src=\"data:image/png;base64,R0lGODlhDgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwAOAA0AhAAAAAAAAAAAOgAAZgA6kABmtjoAADo6Ojo6kDqQ22YAAGY6Zma2/5A6AJA6OpDb25Db/7ZmALZmOrb//9uQOtv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwVJICCO1DCN6IgpAZOm1hEU75g5TyNANWDRlECiJ3FdDITK64JQAiKtV3FkmaVWgax2h6IMu0LV4oQ6JgGZyBmVacwqrABXhM0KQgA7\" alt=\"image\"\u003e\u0026nbsp;is the geothermal field area = 35.43 km\u003csup\u003e2\u003c/sup\u003e;\u003cimg width=\"12\" height=\"33\" src=\"data:image/png;base64,R0lGODlhEgAyAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACgASABAAhQAAAAAAAAAAZgA6kABmkABmtjoAADpmtjqQtjqQ22YAAGYAOma2/5A6AJA6ZpBmOpBmkJCQtpDb/7ZmALZmOraQOrb//9uQOtuQZtu2ZtvbkNv///+2Zv+2kP/bkP/btv//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwZtQADg8wgEDpWEcMkEeAyITWgSUDaXoUZh+bRenQZv9yvkBBjcMBlgFqvJIMVWOF5To4DMmxyiGA8ae2tpCVkBAhZPAxtXY3FoABgSX2ZzF1shEV8XRgGLIAsSHZODSxMEEIyldAaQqwAhDhZLQQA7\" alt=\"image\"\u003e\u0026nbsp;is the natural flow rate of hot springs or hot water wells (holes), L/s;\u003cimg width=\"6\" height=\"31\" src=\"data:image/png;base64,R0lGODlhCQAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACwAJAAkAhAAAAAAAAAAAOgA6kABmtjoAADo6ADpmkDqQtjqQ22YAAGY6AGa2/5Db/7ZmOtuQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwUxIABQSxAckjgJDRAFBFApiWg5g0QVjOgDk0DvNyrEiLMAIgWAtB4mkyEnihQCAhQgBAA7\" alt=\"image\"\u003e\u0026nbsp;is the specific heat of water, 1.0 kcal/kg\u0026bull;\u0026deg;C;\u003cimg width=\"7\" height=\"31\" src=\"data:image/png;base64,R0lGODlhCgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACwAKAA0AhAAAAAAAAAAAOgAAZgA6kABmtjoAADpmkDpmtjqQ22YAAGY6AGaQ22a2/5A6AJA6OpC2/5Db/7ZmALaQZra2Zrbb/7b//9uQOtu2Ztv///+2Zv/btv//tv//2wECAwECAwVAIAB0kxEIjDg+ELAtQSJegyV2Tg1I+hw0uB5A8wvaRBpBxKiSFEaOACIDwOiCFNOhcssdVVACFRwegzmKJxkQAgA7\" alt=\"image\"\u003e\u0026nbsp;is the density of water, \u0026rho;=1.0 kg/L;\u003cimg width=\"7\" height=\"31\" src=\"data:image/png;base64,R0lGODlhCwAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAALABAAhAAAAAAAAAAAZgBmtjpmtmYAAGaQvGa2tma2/5A6ALZmALb//9uQOv+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwU4IAAwiGiaUFKephKsbBLMLzvW9g2zDG73uxMwpxM9CofCANB4OQwywcIoIwBSQZSKiOVubalZKQQAOw==\" alt=\"image\"\u003e\u0026nbsp;is the water temperature of hot spring water, ℃;\u003cimg width=\"7\" height=\"31\" src=\"data:image/png;base64,R0lGODlhCgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAAKABAAgwAAAAAAAABmtjpmtmaQ22a2/5A6ALZmANuQOv+2Zv/bkP//tv//2wECAwECAwECAwQwEABUpJXM1CtP2JcRjB+HlNyEmushSCd4xakKulLyKcT5AgvRAPZLIYom5EyZGxUiADs=\" alt=\"image\"\u003e\u0026nbsp;is the local average temperature over many years, taken as the average temperature of 4.1℃ in the plain area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Calculation results of heat release in the study area\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003cp\u003eSpring No.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eNatural discharge\u003c/p\u003e\n \u003cp\u003eqv(l/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSpecific heat of water\u003c/p\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003cp\u003e（kcal/kg\u0026bull;℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eDensity of water\u003c/p\u003e\n \u003cp\u003e\u0026rho;\u003c/p\u003e\n \u003cp\u003e（kg/l）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eHot spring water temperature\u003c/p\u003e\n \u003cp\u003etr(℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eAverage temperature over many years\u003c/p\u003e\n \u003cp\u003eti(℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHeat releaseQ（kcal/s）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e250.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e249.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe calculation results in Table 4 show that the heat emitted into the air through rock conduction in the study area\u0026apos;s geothermal field per unit time is: 108.36 mW/m\u003csup\u003e2\u003c/sup\u003e \u0026times; 3.543 \u0026times; 10\u003csup\u003e7\u003c/sup\u003e m\u003csup\u003e2\u003c/sup\u003e = 3.839 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e mW = 917.57 kcal/s.Therefore, the heat emitted by the geothermal field in the study area is 1167.97 kcal/s = 1.54 \u0026times; 10\u003csup\u003e14\u003c/sup\u003e J/a, which is equivalent to 5260 t/a of standard coal.\u003c/p\u003e\n\u003ch2\u003e5.2 Geothermal reservoir volume method\u003c/h2\u003e\n\u003cp\u003eThe thermal storage method is based on treating the hot reservoir rock and the fluid in its pores as a whole, using the local annual average temperature as the reference temperature to calculate the geothermal resources in the entire hot reservoir. The calculation formula is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"262\" height=\"57\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;(Formula 2)\u003c/p\u003e\n\u003cp\u003eIn the formula:\u003cimg width=\"9\" height=\"31\" src=\"data:image/png;base64,R0lGODlhDgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwAOABEAhAAAAAAAAAAAOgAAZgA6ZgBmtjoAADpmkDqQ22YAAGY6AGY6OmaQtma222a2/5A6AJBmOpC225Db/7ZmALZmOrbb/7b//9uQOtu2Ztu2kNvb///bkP/btv/b2///tv//2wVjICAC2RIEwlGNoqcIkchBQjN+z2CxwBQ4oksAwQNsDDpPQiAp4n5HXbEXKER3RaEVieX5Ckrm1EckF5XScJNlFh1hog5kyPtQDKcAITZ1odZTAE8DK4EiGAYCDBqGIhkQBgghADs=\" alt=\"image\"\u003e\u0026nbsp;is the heat stored in the geothermal reservoir, J;\u003cimg width=\"9\" height=\"31\" src=\"data:image/png;base64,R0lGODlhDgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwAOAA0AhAAAAAAAAAAAOgAAZgA6kABmtjoAADo6Ojo6kDqQ22YAAGY6Zma2/5A6AJA6OpDb25Db/7ZmALZmOrb//9uQOtv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwVJICCO1DCN6IgpAZOm1hEU75g5TyNANWDRlECiJ3FdDITK64JQAiKtV3FkmaVWgax2h6IMu0LV4oQ6JgGZyBmVacwqrABXhM0KQgA7\" alt=\"image\"\u003e is the area of the calculation zone, m\u003csup\u003e2\u003c/sup\u003e;\u003cimg width=\"7\" height=\"31\" src=\"data:image/png;base64,R0lGODlhCgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAKAA4AhAAAAAAAAAA6ZgA6kABmkABmtjoAADoAOjqQ22YAAGY6AGaQtma2/5A6AJBmOrbbkNuQOtuQZtu2kNvbkNv///+2Zv/bkP/btv//tv//2wECAwECAwECAwECAwECAwECAwU9ICCOkoKMIxQEJypaRuvCMkqPlxIIT/z6ORYg0yiMKsLb74RkjG5I2Q2TGFCWIlWBknEYAgRR5DuYHBaUEAA7\" alt=\"image\"\u003e\u0026nbsp;is the thickness of the geothermal reservoir, m; \u003cimg width=\"11\" height=\"31\" src=\"data:image/png;base64,R0lGODlhEQAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACwARAA0AhAAAAAAAAAAAOgAAZgA6kABmtjoAADpmkDpmtjqQ22YAAGY6AGaQ22a2/5A6AJA6OpC2/5Db/7ZmALZmOraQZra2Zrbb27bb/7b//9uQOtu2Ztv///+2Zv/btv//tv//2wVfIAB8lBEIjEgazRQk4wMB3fICUhAcllMAmQFG5RACAw2RSGIUZZDHJLEJ4ECf0lFxKOIIIlHlZ6uU/MLEAGID0Bg/uex4UDEdLlrdTUtV+sUOBGx/hFqChYQeCmeIfiEAOw==\" alt=\"image\"\u003eis the density of the geothermal reservoir rock, kg/m\u003csup\u003e3\u003c/sup\u003e; \u003cimg width=\"9\" height=\"31\" src=\"data:image/png;base64,R0lGODlhDgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACwAOAA0AhAAAAAAAAAAAOgAAZgA6kABmtjoAADo6ADpmkDqQtjqQ22YAAGY6AGa2tma2/5A6AJDb/7ZmOrb//9uQOtuQZtu2Ztv/29v///+2Zv/bkP//tv//2wECAwECAwECAwECAwVJIABkTBAgl6iKmAABVVCsorYo4hYR6ZoZDpoQEwgKfYbZUWULJHoUiK2xmE1MpgPB8ggMJKqKISBAATYP4/KcXufabrT6iDYFQwA7\" alt=\"image\"\u003eis the specific heat of the geothermal reservoir rock, J/kg\u0026middot;℃;\u003cimg width=\"8\" height=\"31\" src=\"data:image/png;base64,R0lGODlhDAAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACwAMAA0AhAAAAAAAAAAAOgAAZgA6OgA6ZgA6kABmtjoAADqQ22YAAGY6AGa222a2/5A6AJBmOpDb/7ZmALbb27bb/7b//9u2Ztu2kNv///+2Zv/bkP/btv//tv//2wECAwECAwECAwVMIABYy5EtQTGJQBUcV9Rw0UABm2JzToMrCQDmBeD5eIbL8FDsiSLJDCJpbDJ5AkhV6gNIC5KeBcFkaR6IQIDAYLmr7rczzoLTm11WCAA7\" alt=\"image\"\u003e\u0026nbsp;is the porosity of the geothermal reservoir rock, dimensionless;\u003cimg width=\"7\" height=\"31\" src=\"data:image/png;base64,R0lGODlhCwAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAALABAAhAAAAAAAAAAAZgBmtjpmtmYAAGaQvGa2tma2/5A6ALZmALb//9uQOv+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwU4IAAwiGiaUFKephKsbBLMLzvW9g2zDG73uxMwpxM9CofCANB4OQwywcIoIwBSQZSKiOVubalZKQQAOw==\" alt=\"image\"\u003e\u0026nbsp;is the reservoir temperature, ℃;\u003cimg width=\"9\" height=\"31\" src=\"data:image/png;base64,R0lGODlhDQAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACAANABAAhAAAAAAAAAA6kABmtjoAZjpmtjqQ22YAZmaQ22a2/5A6ALZmALb//9uQOtv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwVJIAA0iWiegKSU6LkEbAsoQQ3L443nsdzoKZrAIfrFFobRoKiDEBiAyAFqFD2GwdIDBkE0ltkoraAUqXpW7LnlhLZlyFFSpgqAQwA7\" alt=\"image\"\u003e\u0026nbsp;is the local annual average temperature, \u0026deg;C; \u003cimg width=\"14\" height=\"33\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e is the geothermal water density, kg/m\u003csup\u003e3\u003c/sup\u003e;\u003cimg width=\"7\" height=\"31\" src=\"data:image/png;base64,R0lGODlhCwAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEABwAKAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAADoAZjpmkDpmtmY6AJA6AJC225Db/7ZmALZmOrZmZraQOraQZrbb/7b//9uQOtuQkNu2kNv////bkP/btv/b2///2wECAwECAwVJILAtQZBQVwVoh5IB2DGozwzcGkILzn1HKktA0PClAB1GiUDx3TqTQ+nkBHAgJR4A6NsoCxlIz5ecPQzOJPhhanYkWiCGFGDeQgA7\" alt=\"image\"\u003e\u0026nbsp;is the hydraulic conductivity, dimensionless; \u003cimg width=\"9\" height=\"31\" src=\"data:image/png;base64,R0lGODlhDgAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABwAOAA0AhAAAAAAAAAAAOgAAZgA6ZgA6kABmtmYAAGa2/5A6AJDb/7ZmALb//9uQOtv/////tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwVCYBKMgQEADRkMwHMQzCkvAnJCSeHIZyMot9yO5wMCcDoe6hdMEplHkYoERQ5lxea1VxUqs1EnFuqCKWk26ciEUg1CADs=\" alt=\"image\"\u003e\u0026nbsp;is the height above the calculation starting point, m;\u003cimg width=\"13\" height=\"31\" src=\"data:image/png;base64,R0lGODlhFAAvAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAACwAUAA0AhQAAAAAAAAAAOgAAZgA6kABmtjoAADoAOjoAZjo6ADpmkDqQtjqQ22YAAGY6AGaQkGaQ22a2/5A6AJCQkJC2/5Db25Db/7ZmALZmOraQOraQkLb//9uQOtuQZtu2Ztv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwZsQAAg5AgEFB+hcrkECSwAT6DArAJEDYZwhCEkrcqQIQIugwLkslVMVVexgcW3U5FMzwEGdrABcIxGCV4hB1AgXgAgaVEGAQJIQhdaTlAaX2ZeGhkMIRBuACMSDxQhCBN9nxx8oW2fnUIdUEJBADs=\" alt=\"image\"\u003e\u0026nbsp;is the specific heat of water, J/kg\u0026middot;\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Statistics of thickness of thermal pore cap rock in various places\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eNumbering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eJ28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003eJ31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003eJ35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eJ36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eAKT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eAKT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eZK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eZK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eZK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003eKC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003eKC1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eOverburden thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5px;\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e346.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Thermal storage method calculation parameters table\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eParameter name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eParameter symbol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eParameter value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eParameter unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eData source\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eGeothermal field area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e35.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eArea of geothermal fields identified above\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eThermal storage thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1793.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eTake the lower limit of the calculated evaluation depth (2000 m) minus the average thickness of the overburden of each geothermal borehole (see Table 6) to obtain the thickness of the overburden of the geothermal field = 2000 - 206.9 = 1793.1 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eThermal storage temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e60.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eTake the average reservoir temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eBase temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eTake the temperature of the constant temperature layer as 9.83\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eRock voidage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026phi;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eDimensionless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eThe weighted average method was used to calculate the average porosity of the geothermal reservoir rocks in wells ZK1 and ZK2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eGeothermal water density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e983.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003ekg/m\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eThe density of water at 60\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSpecific heat of geothermal water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003ec\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e4180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eJ/(kg\u0026middot;℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eSpecific heat of water\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eSpecific heat of a thermally stored rock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003ec\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e733.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eJ/(kg\u0026middot;℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eTake the average saturated specific heat capacity of sedimentary rocks, metamorphic rocks, and igneous rocks in the hot reservoir\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eThermal reserve rock density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003csub\u003er\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003ekg/m\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eAverage rock density in the Cambrian-Carboniferous period in the study area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCalculation starting point\u003c/p\u003e\n \u003cp\u003eAbove Height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e193.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003em\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eOverburden thickness (206.9 m) minus average geothermal water buried depth (12.98 m)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHydraulic conductivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cem\u003eS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.000184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eDimensionless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eTake the average value of the water storage coefficient calculated using the Tese formula for geothermal wells ZK1, ZK2, and ZK3 in the study area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eRemarks:The lower limit depth for calculating and evaluating the thermal storage method should be determined based on comprehensive considerations such as local economic development, geothermal resource extraction technology conditions, and the economic benefits of geothermal utilization. In this study, the evaluation depth of geothermal resources in the geothermal field was set at 2,000\u0026nbsp;m\u0026nbsp;as the evaluation benchmark.The reservoir temperature was taken as the maximum depth of the borehole (1404.4 m) as the boundary. The upper layer used the average temperature of the geothermal reservoir actually revealed by drilling, which was 52.66\u0026deg;C, and the lower layer used the geothermal gradient (3.81\u0026deg;C/100 m) to calculate the average temperature of 68.41\u0026deg;C. The average of the two layers was taken as 60.54\u0026deg;C.The weighted average porosity of the hot reservoir (the average porosity of the intact section of the study area is 1.89%, and the average porosity of the fractured section is 6.5%; the fractured layers of the ZK1 and ZK2 drill holes account for 18.5% and 60.6%, respectively. The average thickness of the fractured layer of the geothermal field accounts for 40%) is 3.73%.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAccording to calculation formula 2 and Table 7, the water storage in the geothermal reservoir of Wenquan County is 2.37\u0026times;10\u003csup\u003e9\u003c/sup\u003e km\u003csup\u003e3\u003c/sup\u003e, the heat stored in the water is 4.9\u0026times;10\u003csup\u003e17\u003c/sup\u003e J, the heat stored in the rock is 6.029\u0026times;10\u003csup\u003e18\u003c/sup\u003e J, and the geothermal resources of the geothermal field are 6.52\u0026times;10\u003csup\u003e18\u003c/sup\u003e J, which is equivalent to 2.23\u0026times;10\u003csup\u003e8\u003c/sup\u003e t of standard coal.\u003c/p\u003e\n\u003ch2\u003e5.3 Comprehensive determination\u003c/h2\u003e\n\u003cp\u003eSince the spring water points selected for the Surface heat flow method calculation were concentrated in the southeastern part of the study area, the calculation results had excessive errors.At the same time, compared with the thermal storage method, which calculates heat energy reserves directly based on the reservoir temperature, volume, and physical properties measured from boreholes, the surface heat flow method estimates the heat loss rate of deep geothermal reservoirs through heat transfer theory. It is highly dependent on shallow geothermal gradients and rock thermal conductivity and is greatly affected by the surface environment, resulting in inaccurate estimates.Relatively speaking, the thermal storage method provides more accurate results, and the geothermal resources of the study area were finally determined to be 6.52\u0026times;10\u003csup\u003e18\u003c/sup\u003e J/a.\u003c/p\u003e"},{"header":"6. CONCLUSIONS","content":"\u003cp\u003eThe geothermal anomaly area in Wenquan County is an irregular fan-shaped area covering 58.05 km\u003csup\u003e2\u003c/sup\u003e, with a geothermal field area of 35.43 km\u003csup\u003e2\u003c/sup\u003e.The geothermal reservoir is controlled by NE-trending fault structures and exhibits a banded distribution.The rock formations distributed along the structures during the Devonian and Carboniferous periods are the main geothermal reservoirs in the study area,the buried depth ranges from 61.53 to 1295.5 m.Drilling revealed that the reservoir temperature ranges from 33 to 70.1\u0026deg;C, which belongs to low- and medium-temperature geothermal resources.\u003c/p\u003e\n\u003cp\u003eThe maximum circulation depth of geothermal water in the geothermal field of Wenquan County is 3401.1 m. The heat source mainly comes from the heat of the upper mantle lava flow, and the heat is supplied to the geothermal reservoir through faults and magma intrusion contact zones.The geothermal reservoir is continuously distributed, buried at a shallow depth, and locally directly overflowing onto the surface. The thickness of the overburden is no more than 500 m, and the overall analysis of the well depth for geothermal resource development is around 900 to 1400 m. The well construction cost is relatively low, which has significant economic development advantages.\u003c/p\u003e\n\u003cp\u003eThe geothermal field in Wenquan County is a small low-temperature geothermal field. The geothermal resources were calculated using the surface heat flow method and the thermal storage method, with results of 1.54\u0026times;10\u003csup\u003e14\u003c/sup\u003e J/a and 6.52\u0026times;10\u003csup\u003e18\u003c/sup\u003e J/a, respectively. Compared with the surface heat flow method, the thermal storage method is more accurate, so the geothermal resources were taken as 6.52\u0026times;10\u003csup\u003e18\u003c/sup\u003e J/a.If geothermal resources are developed and utilized reasonably, approximately 2.23\u0026times;10\u003csup\u003e8\u003c/sup\u003e t of standard coal can be saved each year, which is of great significance for saving fossil energy and reducing carbon emissions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research is supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region of China(2022D01C360,2022B03017-4), Young Top Talent Project of Xinjiang Uygur Autonomous Region of China(2023TSYCCX0010), 2025 Ximjiang Uygur Autonomous Region Graduate Student Research Innovation Project(XJ2025G101), 2025 National Student Innovation Training Program Project of Xinjiang University, 2024 Professional Degree Graduate Student Teachimg Case Bank Construction Proiect of Xinjiang University.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChenhao Sun: conceived this study and wrote the paper. Kai Chen and Mengmeng Zhao: propose research ideas and participate in paper revision and refinement. Xinchang Wei, Zhilong Qi, Yalu Wang, and Ying Xi: collated data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThermal storage parameters used in this study are included in this published article and available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eIsabel P\u0026eacute;rez Mart\u0026iacute;nez, Ruth Esther Villanueva Estrada, Claudio Inguaggiato, Mario Alberto Hern\u0026aacute;ndez Hern\u0026aacute;ndez, Giovanni Sosa Ceballos. Origin of fluids in the Arar\u0026oacute;-Simirao geothermal system, Central Mexico[J]. Journal of Geochemical Exploration, 2025, 269 107637-107637.\u003c/li\u003e\n\u003cli\u003eLo\u0026iuml;c Peiffer, Claudio Inguaggiato, Jobst Wurl, John M. Fletcher, Maria Guadalupe Olgu\u0026iacute;n Mart\u0026iacute;nez, Daniel Carbajal Mart\u0026iacute;nez, Denis Legrand, Pablo Hern\u0026aacute;ndez Morales, Carlos E. Reinoza, Boris Chako Tchamab\u0026eacute;, Lilia Arana Salinas, Rodolfo Silva Casar\u0026iacute;n. Geochemistry of coastal geothermal systems from southern Baja California peninsula (Mexico): Fluid origins, water-rock interaction and tectonics[J]. Chemical Geology, 2024, 670 122316-122316.\u003c/li\u003e\n\u003cli\u003eWang G L, Lin W J. Formation mechanisms and genetic models of major hydrothermal geothermal systems in China[J]. Acta Geologica Sinica, 2020, 94(7): 1923-1937.\u003c/li\u003e\n\u003cli\u003eMao X, Wang X W, Guo S Y, Bao Z D. Formation mechanism of geothermal resources in Gaoyang geothermal field and its adjacent areas[J]. Carsologica Sinica, 2021, 40(2): 273-280.\u003c/li\u003e\n\u003cli\u003eLuo S Q, Namdawa, Li H, Cheng F J, Gesang Pingcuo, Luosong Qunpei. Geological structural characteristics and formation mechanism of Chazi Geothermal Field in Shigatse City, Tibet[J]. Acta Geologica Sichuan, 2020, 40(1): 30-33.\u003c/li\u003e\n\u003cli\u003eLiu D M, Zhang C S, Lu W L, Wei M H, Qi Y Y, Liu F, Zhao Y, Jiang H. Enrichment background and formation mechanism of medium-deep geothermal resources in Fenwei Graben[J]. Earth Science Frontiers, 2025, 32(1): 367-379.\u003c/li\u003e\n\u003cli\u003eNing Y W. Geothermal characteristics and genetic mechanism of Neihuang Uplift[D]. Zhengzhou University, 2022.\u003c/li\u003e\n\u003cli\u003eDu J, Cai N B, Zhang B J, Wang K Y, He L H, Liao F C. Heat source mechanism and genetic model of hot dry rock formation in Reshuixu geothermal field, Hunan Province[J]. Coal Geology \u0026amp; Exploration, 2024, 52(1): 70-82.\u003c/li\u003e\n\u003cli\u003eDong D Z, Liu X L. Formation mechanism and development-utilization of geothermal resources in Linfen Basin[J]. Energy and Energy Conservation, 2022(7): 57-61.\u003c/li\u003e\n\u003cli\u003eWang G L, Zhang W, Liang J Y, Lin W J, Liu Z M, Wang W L. Evaluation of geothermal resource potential in China[J]. Acta Geoscientica Sinica, 2017, 38(4): 449-450+134+451-459.\u003c/li\u003e\n\u003cli\u003eMa F, Wang G L, Zhang W, Zhu X, Zhang H X, Yue G F. Thermal reservoir spatial structure and resource potential of Rongcheng geothermal field in Xiongan New Area[J]. Acta Geologica Sinica, 2020, 94(7): 1981-1990.\u003c/li\u003e\n\u003cli\u003eHan J, Liu H, Zhen Z, Zhu P F. Application of Monte Carlo method in geothermal resource assessment[J]. Journal of Geology, 2022, 46(2): 158-164.\u003c/li\u003e\n\u003cli\u003eRen S S. Evaluation of geothermal resource potential of Paleogene in Baxian Sag, Jizhong Depression[D]. Northeast Petroleum University, 2020.\u003c/li\u003e\n\u003cli\u003eYang Y Z. Characteristics of Guantao Formation thermal reservoir and evaluation of geothermal resources in Cangzhou section, Huanghua Depression[D]. China University of Geosciences (Beijing), 2020.\u003c/li\u003e\n\u003cli\u003eZhou J F. Evaluation of geothermal resources and utilization direction in Bunanbao geothermal field based on thermal reservoir method[J]. Modern Mining, 2023, 39(10): 52-55+60.\u003c/li\u003e\n\u003cli\u003eBrown Christopher S.. Regional geothermal resource assessment of hot dry rocks in Northern England using 3D geological and thermal models[J]. Geothermics, 2022, 105\u003c/li\u003e\n\u003cli\u003eInstitut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Qu\u0026eacute;bec, QC, Canada, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Qu\u0026eacute;bec, QC, Canada, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Qu\u0026eacute;bec, QC, Canada, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Qu\u0026eacute;bec, QC, Canada, Institut national de la recherche scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, G1K 9A9, Qu\u0026eacute;bec, QC, Canada. Geothermal resource assessment of remote sedimentary basins with sparse data: lessons learned from Anticosti Island, Canada[J]. Geothermal Energy: Science \u0026ndash; Society \u0026ndash; Technology, 2020, 8 (Supplement): 24-39.\u003c/li\u003e\n\u003cli\u003eAnthony E. Ciriaco, Sadiq J. Zarrouk, Golbon Zakeri, Warren I. Mannington. Refined experimental design and response surface methodology workflow using proxy numerical models for probabilistic geothermal resource assessment[J]. Geothermics, 2020, 88\u003c/li\u003e\n\u003cli\u003eMar\u0026iacute;a Jos\u0026eacute; Oviedo, Jasmin Raymond, Daniela Blessent, St\u0026eacute;phanie Larmagnat. Numerical assessment of the geothermal resource potential of the Charlevoix meteorite impact crater, Canada[J]. Geothermics, 2025, 127 103243-103243.\u003c/li\u003e\n\u003cli\u003eDaniel Fari\u0026ntilde;a Gonz\u0026aacute;lez, \u0026Oacute;scar Garc\u0026iacute;a Afonso, Agust\u0026iacute;n M. Delgado Torres. Assessment of geothermal resources for power generation on La Palma Island, Canary Islands[J]. Geothermics, 2025, 127 103263-103263.\u003c/li\u003e\n\u003cli\u003eMohamed Ayed Elbalawy, Mohamed Balash, Mohamed Hamdy Eid, Ernő Tak\u0026aacute;cs, Felicit\u0026aacute;sz Velledits. Innovative method integrates play fairway analysis supported with GIS and seismic modeling for geothermal potential evaluation in a basement reservoir[J]. Scientific Reports, 2025, 15 (1): 1325-1325.\u003c/li\u003e\n\u003cli\u003eSedara Samuel O., Alabi Olusegun O.. Heat flow estimation and quantification of geothermal reservoir of a basement terrain using geophysical and numerical techniques[J]. Environmental Earth Sciences, 2022, 81 (3):\u003c/li\u003e\n\u003cli\u003eZhao J, Guo Q H. Geothermal resource potential assessment based on 3D geological modeling: A case study of Shidian geothermal area[J]. Earth Science, 2023, 48(3): 1107-1117.\u003c/li\u003e\n\u003cli\u003eZhu Z Z, Lei X D, Wu X, Xu N X, Jiang X Y. Evaluation of geothermal resources in Changping New Town, Beijing based on 3D geological modeling[J]. Geoscience, 2020, 34(1): 207-214.\u003c/li\u003e\n\u003cli\u003eChen H W, Song R C, Zhang C, Liang Y, Wang Y C, Zheng F, Wang Y L, Wang H H. Thermal reservoir evaluation of hot dry rock geothermal resources based on factor analysis method[J]. Journal of Chengdu University of Technology (Science \u0026amp; Technology Edition), 2023, 50(3): 333-350.\u003c/li\u003e\n\u003cli\u003eHu J, Wang D C, Xu L, Liu Q, Leng J G, Gao Y, Zhao Y J, Yin C. Integrated information exploration and potential evaluation of medium-deep geothermal resources in Zhumadian area, Henan Province[J]. Science Technology and Engineering, 2025, 25(1): 67-75.\u003c/li\u003e\n\u003cli\u003eBian Y, Yang Y P, Li M, He X, Tang H, Sun A, Ju X. Application of thermal infrared remote sensing technology in geothermal resource exploration[J]. China Mining Magazine, 2021, 30(S2): 153-157.\u003c/li\u003e\n\u003cli\u003eZhu B Q, She Y H, Zhu L X. A promising geochemical method for geothermal exploration[J]. Geophysical and Geochemical Exploration, 1983(5): 258-265.\u003c/li\u003e\n\u003cli\u003eLi P P, Chen H L. Application of geophysical exploration in geothermal exploration[J]. Mineral Exploration, 2019, 10(4): 938-942.\u003c/li\u003e\n\u003cli\u003eZhang Y Y, Pan G, Zhu P, Yao W J, Cui Y Y, Xue W M. Application of CSAMT in geothermal exploration of Moon Bay, northern Jiangsu[J]. Mineral Exploration, 2020, 11(6): 1228-1233.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"geothermal field, formation mechanism, resource evaluation, Wenquan County, Xinjiang","lastPublishedDoi":"10.21203/rs.3.rs-7251739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7251739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Wenquan County area in Xinjiang has a large number of hot springs and rich geothermal resources, with high potential for geothermal resource development and utilization. At present, systematic research on the formation mechanism and resource potential of geothermal fields in this area is relatively weak, which has to some extent restricted the comprehensive development and utilization of geothermal resources in Wenquan County. This paper selects Wenquan County as the research area. Based on the collection and analysis of geophysical, geochemical, remote sensing interpretation, and well logging data, the scope of geothermal anomaly areas and geothermal fields in the research area is comprehensively determined, and the formation mechanism of geothermal fields is revealed. The geothermal resources in the research area are evaluated using the surface heat flow method and the thermal storage method. The results show that: (1) The Wenquan County geothermal field is located in the valley between Alatao Mountain and Biezhentao Mountain. It is roughly irregular in shape and mainly consists of low-temperature convection-type strip geothermal reservoirs with a longitudinal buried depth of about 3000 m; (2) The heat in geothermal fields mainly comes from the heat flow of lava in the upper mantle. Groundwater in the region is heated by deep circulation and forms geothermal fluids, which are transported upward through channels formed by fault structures, continuously transferring heat from the lower layers to the surface to form geothermal fields; (3) The geothermal field has a geothermal reservoir capacity of 2.37×109 km3 and geothermal resources of 6.52×1018 J, equivalent to 2.23×108 t of standard coal. This study basically clarified the geothermal formation model and resource rating of Wenquan County and provided an important basis for the development and utilization of geothermal resources in the study area.","manuscriptTitle":"Genesis Mechanism and Resource Evaluation of Low-Temperature Hydrothermal Geothermal Fields in Wenquan County, Xinjiang","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 09:56:11","doi":"10.21203/rs.3.rs-7251739/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0f67d6d4-6303-423b-bce7-4aebd51045e0","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54538004,"name":"Physical sciences/Energy science and technology"},{"id":54538005,"name":"Earth and environmental sciences/Environmental sciences"},{"id":54538006,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2025-12-08T14:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 09:56:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7251739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7251739","identity":"rs-7251739","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}