Genetic Analysis of Geothermal Resources and Geothermal Geological Characteristics in Datong Basin, Northern China

Zhou, Wenlong; Hu, Xiangyun; Yan, Shilong; Guo, Hongdang; Chen, Wei; Liu, Sijing; Chen, Miao

doi:10.3390/en13071792

Cited by 17 publications

(14 citation statements)

References 33 publications

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“…The Yanggao geothermal field, located in the Datong Basin, northern Shanxi Province, has produced geothermal resources with temperatures ranging from 28 to 45 • C. These resources are Neogene and Quaternary low-temperature geothermal resources, and are primarily used for bathing. Based on the analysis of geological conditions, seismic activity, volcanic activity, terrestrial heat flow, and thermal springs, as well as geophysical exploration, it is proposed that the Datong Basin enjoys favorable geological conditions for geothermal resources and exhibits significant research value [18,20]. As regards the Yanggao geothermal field, its geological, structural, and cap rock distribution characteristics have been identified through geophysical exploration [19], and the hydrochemical characteristics and other general properties of its geothermal water have been analyzed [16,17].…”

Section: Introductionmentioning

confidence: 99%

Reservoirs and Hydrogeochemical Characterizations of the Yanggao Geothermal Field in Shanxi Province, China

Yan,

Wei,

Zhang

et al. 2024

Water

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Geothermal water is the product of deep circulation within the crust, and the understanding of its hydrogeochemical process can provide effective information for integrated research on its circulation pattern and formation mechanism. Based on the geothermal geological conditions of the Yanggao geothermal field, this study analyzed water samples from thermal springs and geothermal wells in the geothermal field, ascertaining their hydrochemical components, along with their hydrogen and oxygen isotopes. Using methods like piper diagrams, ionic component ratio characterization, Na–K–Mg equilibrium diagrams, and reverse path simulations, this study elucidated the recharge source of geothermal water in the study area, revealed the water–rock interactions the geothermal water experienced, and evaluated the geothermal reservoir temperatures. The results show that the geothermal water has hydrochemical types of Na–Cl–HCO3 and Na–HCO3–Cl, and is primarily recharged by the atmospheric precipitation in the northern mountainous area. The geothermal water has experienced extended water runoff and deep thermal circulation, and its hydrochemical composition primarily results from the weathering and dissolution of silicate rocks and evaporites. The major hydrogeochemical processes of the geothermal water involve the dissolution of calcite, dolomite, gypsum, and kaolinite. In addition, the canon-exchange also changes the chemical component of the geothermal water. The SiO2 Geothermometer, a multimineral equilibrium diagram, and the silica–enthalpy model reveal the presence of deep and shallow geothermal reservoirs in the study area, which exhibit temperatures of 73 °C and ranging from 125 to 150 °C, respectively. The open geothermal reservoir environment results in the mixing of geothermal water and cold water, with shallow and deep geothermal water mixing with cold water at ratios of 57% and 76%, respectively.

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Section: Introductionmentioning

confidence: 99%

Reservoirs and Hydrogeochemical Characterizations of the Yanggao Geothermal Field in Shanxi Province, China

Yan,

Wei,

Zhang

et al. 2024

Water

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“…Shallow geothermal energy, as a new low-carbon, clean natural energy source, is thermal energy stored in the rock-soil body, groundwater, and surface water at a specific depth range (<200 m) below the ground surface [1] that has value for exploitation. The temperature of this energy is usually below 25 • C [2], with abundant resources in China.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Layout of a Ground Source Heat Pump System with a Groundwater–Thermal Coupling Model

Li,

Liu,

Wang

et al. 2023

Energies

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The exploitation and utilization of shallow geothermal energy are of great significance to realizing China’s “double carbon” goal and promoting a green economy and social development. However, many projects using ground source heat pumps to exploit shallow geothermal energy have disrupted the thermal balance of the geothermal field due to insufficient preliminary research, affecting the sustainable utilization of shallow geothermal energy. Therefore, a 3D groundwater–thermal coupling model was established in this paper using the geotemperature data of a ground source heat pump system in Xi’an. This study investigated the response characteristics of the groundwater–thermal system to the ground source heat pump system using the numerical simulation method and discussed the optimal layout scheme of the system on this basis. After years of simulation, it was found that long-term operation of the ground source heat pump system under actual operation produces “cold accumulation”. In addition to artificial intervention of the groundwater flow field, the effects of the system operating parameters and layout settings are also investigated to alleviate this cold accumulation. The results show that changing the operating parameters so that the heat transfer is the same in winter and summer, cross-locating the cooling holes with the heating holes, and placing multiple pumping and recharge wells downstream can alleviate the cold accumulation in the heat exchange zone. The results of this numerical simulation study provide an important reference for solutions to mitigate the accumulation of ground cold and heat in developing shallow geothermal energy using borehole heat exchangers and to suppress the downstream geotemperature disturbance via the ground source heat pump system.

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“…Many GSMs, especially warm and hot springs, geysers, and fumaroles, have drawn plenty of attention from the geothermal community in recent decades. Many researchers investigated geothermal anomalies related to GSMs such as hot springs using geophysics [9][10][11], geochemistry [12][13][14], remote sensing [15][16][17][18][19], Geographic Information System (GIS) [20], statistical modeling [21] and conventional Machine Learning (ML) [6,22,23]. For example, Gentana et al (2019) demonstrated that the fault system is correlated with the appearances of the GSMs in the Indonesia volcanic zone [24]; Freski et al (2021) tested the effects of alteration degree, moisture, and temperature on laser return intensity for the GSMs.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Preprint

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Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using Deep Learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three Machine Learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of Overall Accuracy (OA), Overall F1 score (OF) and Computational Time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers with the highest OA and the biggest OF for both validation and test, and the fastest CT for both validation and test. On the contrary, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. It suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain.

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Genetic Analysis of Geothermal Resources and Geothermal Geological Characteristics in Datong Basin, Northern China

Cited by 17 publications

References 33 publications

Reservoirs and Hydrogeochemical Characterizations of the Yanggao Geothermal Field in Shanxi Province, China

Reservoirs and Hydrogeochemical Characterizations of the Yanggao Geothermal Field in Shanxi Province, China

Optimizing the Layout of a Ground Source Heat Pump System with a Groundwater–Thermal Coupling Model

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

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