Front. Earth Sci. Frontiers in Earth Science Front. Earth Sci. 2296-6463 Frontiers Media S.A. 1233026 10.3389/feart.2023.1233026 Earth Science Original Research Evaluation of geothermal resources potential in the uplifted mountain of Guangdong province using the Monte Carlo simulation Li et al. 10.3389/feart.2023.1233026 Li Yongyi Long Xiting Lu Jun * Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization, Institute of Deep Earth Sciences and Green Energy, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China

Edited by: Haibing Shao, Helmholtz Association of German Research Centres (HZ), Germany

Reviewed by: Muhsan Ehsan, Bahria University, Pakistan

Xiaoji Shang, China University of Mining and Technology, China

 *Correspondence: Jun Lu, junlu@szu.edu.cn 03 08 2023 2023 11 1233026 01 06 2023 24 07 2023 Copyright © 2023 Li, Long and Lu. 2023 Li, Long and Lu

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Geothermal energy is a kind of renewable energy with the characteristics of stability, zero carbon emissions, less land occupation, and localization. It has enormous development potential in the transition from traditional fossil energy to renewable energy, especially in Guangdong, geothermal resources are also abundant. However, the existing resource potential evaluation is relatively broad, and the uncertainty of the distribution of geothermal resources is not considered. Therefore, accurately and scientifically assessing the potential of geothermal resources is currently a research focus, Monte Carlo simulation is an ideal solution to quantitatively measure the distribution of geothermal resources through probability distributions. In this study, based on the volume method, considering the uncertainty of geothermal resource distribution parameters, Monte Carlo simulation was introduced, and the triangular distribution and uniform distribution model were used to simulate the input parameters of geothermal fields, and the potential of uplifted mountain geothermal resources in this area was evaluated. The results show that the fracture-banded reservoir geothermal resources are 5.648–5.867 × 1016 kJ (mean 5.743 × 1016 kJ), the karst-layered reservoir geothermal resources are 5.089–5.536 × 1015 kJ (mean 5.328 × 1015 kJ), finally the uplifted mountain geothermal resources potential of Guangdong are 6.176–6.399 × 1016 kJ (mean 6.275 × 1016 kJ). It quantitatively shows that the existing uplifted mountainous geothermal resources potential in Guangdong Province is enormous, the total amount of uplifted mountainous geothermal resources is equal to 2.11–2.18 × 105 Ten thousand tons of standard coal (mean 2.14 × 105 Ten thousand tons of standard coal).

 resources assessment geothermal resources Monte Carlo simulation volume method uplifted mountain section-at-acceptance Economic Geology 1 Introduction

Geothermal energy is a kind of natural renewable energy from the inside of the Earth, mainly from the original heat accumulated when the planet was born and the decay of radioactive isotopes such as uranium, thorium, and potassium in the mantle and crust (Liu, et al., 2023). The heat and energy of the Earth’s interior are transported to the Earth’s surface by thermal convection in the mantle and core, which eventually drives the plate tectonics, continental drift, and major orogeny in geological timescale (i.e., over tens of million years), as well as volcanic eruptions and earthquakes in the timescale of human activities (Yoshida and Santosh, 2020). The Earth is a huge thermal reservoir, the inner core temperature at the deepest part of the Earth is about 7,000°C, it has been estimated that the total heat available within the upper 5 km of the Earth’s subsurface is about 140×106 EJ (Trumpy, et al., 2015). Driven by the temperature difference, heat flows continuously from the deep part of the Earth to the surface, and dissipates in the atmosphere and hydrosphere in the form of terrestrial heat flow, the total heat flow is huge, about 1.4×103 EJ yr-1 (Yoshida and Santosh, 2020). Therefore, geothermal energy has not only an enormous potential in the resources reserve, but has some unique superiority such as local energy, good stability, zero carbon emissions, and less land occupation (Alqahtani, et al., 2023b), compared with other renewable energy, like hydropower, solar power, wind energy, and bio-fuels.

The area of Guangdong is one of the most economically vigorous and most densely populous, as well as the most industrialized areas with high energy demand in China. The utilization of geothermal energy can help to decrease its dependence on conventional fossil fuel, reduce its greenhouse gas emissions and improve Guangdong’s energy transition towards China’s pledge to carbon peak by 2030 and carbon neutrality by 2060 (Zhong, et al., 2023). The whole province of Guangdong has over 300 hot springs with abundant medium and deep geothermal resources (Song, et al., 2005), which ranks third in the whole country, making it an ideal area for the exploitation of geothermal resources. It can also be found that the density of hot springs in Guangdong is an anomaly higher than other areas in China except for Yunnan and Tibet province, the fracture-banded reservoir geothermal resources are also plenteous as Figure 1. Shows. And there is sketchy geothermal resources evaluation that has been conducted in Guangdong, either as a part of the hot dry rock or geothermal resources evaluation of the whole of China (Wang, et al., 2012; Jiang, et al., 2016; Wang, et al., 2017) or only assessment of geothermal resources potential in the Guangdong-Hong Kong-Macao Greater Bay Area (Xie, et al., 2019a). Therefore, it is necessary to assess the potential of geothermal resources in Guangdong more accurately and comprehensively.

Distribution of geothermal resources in Guangdong province. (A) Distribution of geothermal resources in China (data from Wang et al. (2017)). (B) Distribution of geothermal resources in Guangdong province.

However, there are currently some primary issues with the assessment of geothermal resources potential using the volumetric method in Guangdong, 1) the high uncertainty and uneven distribution of uplifted mountain geothermal resources, 2) the limited availability of geological and geothermal information and data in geothermal resources, 3) the volumetric method disregards parameter uncertainties and instead assigns fixed values to thermal storage geometry and physical property parameters, above all make it difficult to an assessment of geothermal resources reserves (Xi, et al., 2018; Witter, et al., 2019; Whealton, et al., 2020; Yuan, et al., 2022). But Monte Carlo simulations can give better help to reduce the variability and uncertainty associated with geothermal resource reserves estimation (Aravena, et al., 2016b; Trota, et al., 2019; Wang et al., 2021). Monte Carlo simulation is a statistical method, that involves the generation of multiple simulations that reflect the variability and uncertainty in parameters such as rock density, rock porosity, reservoir temperature, area, and thickness, all of those parameters follow the probability distribution such as uniform distribution, triangular distribution, normal distribution and so on.

This study employs the Monte Carlo simulation method to explore the geothermal resources potential of Guangdong province. The study proceeds by introducing the geothermal geologic setting of the research area in Section 2. Followed by an outline of the various geothermal resources assessment methods, with an emphasis on analyzing the advantages and limitations of the volumetric method in Section 3, then describes how the Monte Carlo simulation was applied to uplifted mountain geothermal resources assessment. While Section 4 presents the data required for the simulation, including the probability distribution of density, specific heat capacity, and porosity of the geothermal reservoir, as well as the data of Guangdong geothermal fields. In Section 5, the evaluation results of the geothermal resources reserve based on Monte Carlo simulation are presented, with a breakdown of the fracture-banded reservoir and the karst-layered reservoir, and then the uplifted mountain geothermal resources reserve of Guangdong is summarized. At last, the conclusion of this study is concluded in Section 6.

 2 Geothermal geologic setting

Guangdong province is located at the southern edge of the hills along the southeast coast of China, with high terrain in the north and low in the south, mainly medium and low hills, and plains along the coast of the Pearl River and coastline of the Pacific Ocean. The study area has a subtropical monsoon climate, with an annual average temperature of 22°C, an annual average rainfall of 2,300 mm, and a rainy season mainly from annual April to September. Most of discovered geothermal resources belong to hydrothermal type, all of which are low-medium temperature geothermal resources (Ying, et al., 2020). The classification of geothermal resources in the Guangdong province is based on the geomorphological structure characteristics and heat transfer mode and can be categorized into two types: uplifted mountain and sedimentary basin, among which uplifted mountain geothermal resources are widely distributed. Moreover, according to the characteristics of geothermal reservoirs, the geothermal reservoir types of Guangdong can be divided into three types: the pore-layered reservoir, the fracture-banded reservoir, and the karst-layered reservoir, and their distribution areas are shown in Figure 1.

2.1 Geological structure

Guangdong province is located at the intersection of the Eurasian Plate, the Pacific Plate, and the Philippine Plate. In the process of plate movement for a long time, the research area has been subducted by the Pacific plate, resulting in the sinking of the cold ocean lithospheric plate but the rise of hot mantle materials, forming a thermo-tectonic zone (Xie, et al., 2019b). The area has experienced many violent tectonic movements and multi-stage magmatic invasions, such as the Indosinian (late Permian–middle Triassic) orogeny and the Yanshanian (Jurassic–Cretaceous) tectonic-magmatic events (Xi, et al., 2018), resulting in secondary faults or rock mass fragmentation and tensile joint fissures in and near the fault zone, which provides space and channels for the storage and migration of geothermal fluids. At the same time, the tectonic activity of deep and large faults not only promotes the formation of thermal storage space, but also communicates the spatial connection between deep geothermal fluid and shallow thermal storage, and becomes an important heat transfer channel in geothermal fields.

Figure 2 can indicate that the distribution of geothermal fields (hot springs) in Guangdong province is mainly controlled by the deep and large faults structure in the NNE-trending and the NW-trending faults. The deep and large NNE-trending faults provide the heat source that mainly comes from the mantle, and the NW-trending fault provides the drainage conditions for the upwelling of hot springs (Zheng, et al., 2021). We can also find that most of the geothermal fields are distributed in bands along deep fault zones in Figure 2, mainly exposed in and around the deep fault axis and at the intersection of faults, while the rest are mostly distributed between various deep and large faults.

Distribution of main faults in Guangdong province (modified after (Wang, et al., 2020)).

In the long period of geological history, the distribution of geothermal fields is not only related to the fragmentation of the original rock mass and the fracture of joints in the contact zone caused by the multi-stage intrusion of magma or the influence of deep and large faults structure but also the geological age of the intrusive rock. Many strong tectonic and magmatic activities occurred in the study area, and the intrusive rock was widely distributed (Figure 3), with acid granite in the Yanshanian period being the most developed (Wang and Shen, 2003). It can also be found that the distribution of geothermal fields is closely related to magma intrusion activity, most of the geothermal fields in the study area are distributed in the outcropping magmatic mass, near late-intrusive dikes or in the contact zone between the rock mass and the surrounding rock.

Distribution of intrusive rock in Guangdong province (data source: Geological map of Guangdong Province, Hong Kong, and Macao Special Administrative Regions).

 2.2 Geothermal background

Terrestrial heat flow is the most direct display of the Earth’s internal heat on the Earth’s surface, which contains a wealth of geological, geophysical, and geodynamic information, and its value is proportional to the geothermal gradient and the thermal conductivity of rocks (Alqahtani, et al., 2023a). It is a key parameter to evaluate the potential of geothermal resources, the southeastern coast, especially in Guangdong province (Figure 4), has a very high geothermal flow background, with an average value of more than 70 mW m-2 (Jiang, et al., 2019). Terrestrial heat flow shows the characteristics of high middle and low north and south. Due to the large number of intrusive rocks distributed along the area of Guangdong, mainly granite in the Yanshanian period, because granite rocks are highly radioactive and their heat generation rate is also high, which provides a direct heat source for crustal heat generation (WENJING, et al., 2016). This also provides an abundant heat source for the geothermal resources background in Guangdong province.

Distribution of heat flow in Guangdong province (data source: The Global Geothermal Flow Database recently updated by the International Heat Flow Commission (Fuchs, et al., 2021)).

The geothermal systems of uplifted mountain geothermal resources in Guangdong province are hydrothermal type, which is mainly distributed in and near deep and large fault zones or in the contact zone between magmatic rock mass and surrounding rock. The geothermal system of uplifted mountain geothermal resources has obvious characteristics of fault control, which is closely related to the distribution of granite in the Yanshanian period. The formation mechanism of uplifted mountain geothermal resources in Guangdong province is that atmospheric precipitation, surface water body, or normal temperature groundwater infiltrates into bedrock fractures, and is heated or evaporated by a hot rock during the deep cycle, and it rises and exposes at appropriate locations (such as fault fractures connecting the surface and deep areas) or is revealed by human beings, as shown in Figure 5.

The formation schematic diagrams of uplifted mountain geothermal resources (modified after White (White, 1968)).

 3 Methodology

Geothermal resources evaluation is one of the earlier tasks of geothermal projects in evaluating the potential of geothermal resources and plays an important role in the decision-making, financing, development, and operation of geothermal resources development and utilization in the next stage. In this investigation, the volume method and Monte Carlo simulation are combined to evaluate the potential of the geothermal resource in Guangdong.

3.1 Evaluation method

Evaluation of geothermal resources has developed from the initial qualitative evaluation to include numerical simulation, analytical solutions, and other quantitative methods, mainly including the surface heat flow method (Muffler and Cataldi, 1978), magma heat budget method (Noguchi, 1970), total good flow method (Huttrer, 1996), dynamic mining method (Kiran, et al., 2022), reservoir volume method (Aravena, et al., 2016a; Palmer-Wilson, et al., 2018; Gao, et al., 2022)and so on, but reservoir volume method is the most widely used. As more and more geothermal fields are discovered and developed, it is increasingly recognized that the exact conditions of many reservoirs are difficult to determine, so Monte Carlo simulation is a good idea to simulate the uncertainty of various parameters, this method mainly combines Monte Carlo simulation and reservoir volume method, which belongs to an improved reservoir volume method (Iglesias and Torres, 2003). Moreover, with the development of computers, numerical simulation of geothermal fields is also used in geothermal resource evaluation, and many calculation methods for geothermal resources are improved based on reservoir volume method (Tian, et al., 2020). Probabilistic evaluation of geothermal resources using design of experiments (DE) and response surface method (RSM) is a more promising technique that can be implemented more easily and quickly (Quinao and Zarrouk, 2018; Pratama, et al., 2020). The general calculation formula of the reservoir volume method is as follows equations (Eq. 1). Q h = ρ r C r 1 − ϕ V T r − T s + ρ w C w ϕ V T r − T s

Where Q h (J) represents the total geothermal resources stored in water and rock, ρ r , ρ w are the density of rock and geothermal fluid respectively, C r , C w (J·(m3·°C)−1) are the specific heat capacity of rock and geothermal fluid respectively, ϕ (%)is rock porosity, V (m3)and T r (°C) are the volume, and temperature of the geothermal reservoir respectively, T s (°C) is the local average surface temperature.

 3.2 Monte Carlo simulation

Monte Carlo simulation is one of the oldest and most widely used statistical procedures for making inferences based on a small sample under various conditions (Sortino, et al., 2010). It uses diverse probability distribution, which is close to the real distribution of input variables, to imitate the variability and uncertainty of a given model with different input variables. Monte Carlo simulation has been used in geothermal resources potential calculation (Palmer-Wilson, et al., 2018; Sutopo, et al., 2019; Wang, et al., 2019; Wang et al., 2021), geothermal power generation capacity (Quinao and Zarrouk, 2018; Miranda, et al., 2020; Ciriaco, et al., 2022), and techno-economic assessment of geothermal resources (Miranda, et al., 2021; Frey, et al., 2023), and it is based on the reservoir volume method, comprehensively considering the random characteristics of each input variables, using repetitive iteration of random variable sampling sequences which follow different probability distributions, such as uniform, triangular, Gaussian, and pert distribution. At last, the probability distribution and cumulative frequency distributions of geothermal resources potential can be provided.

 4 Geothermal features data and other input variables 4.1 The density, porosity, and specific heat capacity of geothermal reservoir rock

Uplifted mountain geothermal resources in Guangdong can be divided into two types according to the geothermal reservoir, one is the most widely distributed the fracture-banded reservoir, and the other is the relatively concentrated distribution of the karst-layered reservoir. However, the medium rock that constitutes the fracture-banded reservoir is mainly intrusive rock bodies formed by magmatic activity, including the Caledonian, the variscan, the Indosinian, the Yanshanian, the Himalayan, and some clastic rock layers of Cretaceous and the tertiary sedimentary rocks, etc., it is diversity. And the medium rock that constitutes the karst-layered reservoir is mainly carbonate rock, including sandstone, limestone, mudstone, and so on. Therefore, according to the uncertainty of, the specific heat capacity and porosity of geothermal reservoir rock, the density (ρ r), specific heat capacity (C r), and porosity ϕ) of geothermal reservoir rock in Guangdong are given a triangular distribution model. The characteristic value of several different reservoir lithologies has been collected from existing literature. The results are shown in Table 1.

The characteristic value of the density, specific heat capacity, and porosity in Reservoir lithology.

Distribution model Reservoir lithology Rock density (kg·m3) Porosity Specific heat capacity (kJ·kg·°C−1)
Min value Max value Most likely value Min value Max value Most likely value Min value Max value Most likely value
Triangular distribution model Sandy conglomerate 2,100 2,700 2,400 0.03 0.1 0.05 0.78 1.25 1.103
Sandstone 2,200 2,750 2,600 0.03 0.10 0.05 0.73 1.2 0.879
Limestone 2,400 2,870 2,700 0.05 0.15 0.08 0.68 0.95 0.921
Mudstone 2,200 2,750 2,500 0.1 0.22 0.15 0.76 1.2 0.921
Granite 2,250 2,740 2,700 0.015 0.035 0.025 0.6 0.9 0.796
Gneiss 2,600 2,950 2,700 0.015 0.032 0.022 0.7 0.98 0.796
4.2 The features of geothermal fields

According to the 4th Geological Brigade of Guangdong Geological Bureau (Zeng, 2015; Wang, et al., 2017), geothermal fields including hot springs and geothermal wells have been successfully discovered and drilled in Guangdong. Tables 2, 3. Provides details on the uplifted mountain geothermal resources, including 315 geothermal fields in Guangdong province, such as the geothermal reservoir type, hot water temperature, hot water density ( ρ w ), geothermal reservoir temperature ( T r ), and geothermal reservoir lithology. Based on the studies by (Moxiang and Jiyang, 1994; Zeng, 2015), the reservoir volume of each geothermal field within uplifted mountain geothermal resources that is controlled by the fault zone is generally not greater than 1×109 m3. To reflect this, the Monte Carlo simulation assumed a uniform distribution model for geothermal reservoir volume ( V ) with a value of 1×109 m³. And the specific heat capacity ( C w ) of geothermal fluid is also given a uniform distribution model with a value of 4.187 kJ kg·°C−1 in the Monte Carlo simulation.

Geothermal fields data of uplifted mountain in Guangdong province (revised from The 4th Geological Brigade of Guangdong Geological Bureau (Zeng, 2015), and also referenced by ((Wang, et al., 2017)), number 1 in the Reservoir column of the table represents the fracture-banded reservoir, and number 2 represents the karst-layered reservoir).

Field Reservoir type Hot water temperature (°C) Reservoir temperature (°C) Hot water density (kJ·kg·°C−1) Reservoir lithology Field Reservoir type Hot water temperature (°C) Reservoir temperature (°C) Hot water density (kJ·kg·°C−1) Reservoir lithology
T-GD1 1 47 116 989.35 granite T-GD46 1 45 90 990.23 granite
T-GD2 1 56 122 985.17 granite T-GD47 2 35.4 66 994.09 Limestone
T-GD3 1 54.3 100 986.14 sandstone T-GD48 1 66.9 129 979.3 granite
T-GD4 1 53 147 986.61 granite T-GD49 1 60.6 117 982.6 granite
T-GD5 1 58.6 123 983.65 granite T-GD50 1 66.6 133 979.3 granite
T-GD6 1 50.5 138 988.03 granite T-GD51 1 54.7 111 985.67 granite
T-GD7 1 58.6 116 983.65 granite T-GD52 1 69.5 136 978.21 granite
T-GD10 1 58.9 155 983.65 sandstone T-GD53 1 51 111 987.56 sandstone
T-GD11 1 42 92 991.44 granite T-GD54 2 45 91 990.23 Limestone
T-GD12 1 55 133 985.67 granite T-GD55 1 41.8 122 991.44 granite
T-GD13 1 43 143 991.04 granite T-GD56 2 48 90 988.91 Limestone
T-GD14 1 50.2 111 988.03 granite T-GD57 1 68.2 126 978.76 granite
T-GD15 1 44.1 120 990.63 sandstone T-GD58 1 35.4 130 994.09 sandstone
T-GD16 1 42.9 95 991.04 sandstone T-GD59 1 48.8 98 988.47 granite
T-GD17 2 60.3 133 983.15 Limestone T-GD60 2 46.1 105 989.79 Limestone
T-GD19 1 30 80 995.17 sandstone T-GD61 1 71.1 145 976.97 granite
T-GD21 1 54.5 116 986.14 granite T-GD62 1 70.7 151 976.97 granite
T-GD23 1 48 80 988.91 sandstone T-GD63 1 53.6 98 986.14 granite
T-GD24 1 56 130 985.17 sandstone T-GD64 1 25 118 997.12 granite
T-GD25 2 41 90 991.85 Limestone T-GD65 1 43.8 95 990.63 granite
T-GD27 1 47 96 989.35 granite T-GD66 2 52 82 987.09 Limestone
T-GD28 2 33 72 994.52 Limestone T-GD67 1 29.2 102 995.56 granite
T-GD29 1 85.5 133 968.44 sandstone T-GD68 1 58.7 126 983.68 granite
T-GD30 1 45.9 127 989.79 granite T-GD69 1 35.3 108 994.09 granite
T-GD31 1 66.5 139 979.85 sandstone T-GD71 1 56.7 111 984.66 granite
T-GD32 2 65.4 112 980.4 Limestone T-GD72 1 50 111 988.03 granite
T-GD33 1 53 132 986.61 sandstone T-GD73 1 34.3 100 994.31 granite
T-GD34 1 51.5 107 987.56 granite T-GD74 2 36 67 993.72 Limestone
T-GD35 1 78 97 972.66 Limestone T-GD75 1 64.7 140 980.95 granite
T-GD36 2 40.5 95 992.25 Limestone T-GD76 2 33.5 55 994.52 Limestone
T-GD37 1 61.4 118 982.6 granite T-GD77 1 43.8 82 990.63 sandstone
T-GD38 1 50 104 988.03 granite T-GD78 1 46.9 105 989.35 sandstone
T-GD39 2 46 108 989.79 Limestone T-GD79 2 30.1 72 995.17 Limestone
T-GD40 1 45.1 101 990.23 granite T-GD80 1 51.7 141 987.09 granite
T-GD41 1 45.3 100 990.23 sandstone T-GD81 1 77.3 166 973.18 granite
T-GD42 2 43 82 991.04 Limestone T-GD82 1 83.9 133 969.08 granite
T-GD43 1 48 92 988.91 granite T-GD83 1 90.5 144 965.12 granite
T-GD44 1 82.8 149 969.72 sandstone T-GD84 2 41.4 134 991.85 Limestone
T-GD45 2 68.8 123 978.21 Limestone T-GD85 1 40.3 110 992.25 granite
T-GD86 1 50.7 121 987.56 granite T-GD137 1 61.3 117 982.6 granite
T-GD87 1 35 73 994.09 sandstone T-GD138 1 30.2 60 995.17 granite
T-GD88 1 82.5 153 970.36 granite T-GD139 1 32 60 994.74 granite
T-GD89 1 55.5 159 985.67 granite T-GD140 1 43 92 991.04 granite
T-GD90 1 52 118 987.09 granite T-GD141 1 47 128 989.35 granite
T-GD91 1 89.5 150 965.12 sandstone T-GD143 1 42 76 991.44 granite
T-GD93 2 51.2 86 987.56 Limestone T-GD144 1 61.8 106 982.05 granite
T-GD94 1 34.3 137 994.31 sandstone T-GD145 1 38.5 112 992.99 granite
T-GD95 1 35.4 98 994.09 sandstone T-GD146 1 31.5 63 994.95 granite
T-GD96 2 48 122 988.91 Limestone T-GD147 1 55.2 139 985.67 granite
T-GD97 1 29.5 100 995.56 sandstone T-GD148 1 49.7 112 988.03 granite
T-GD98 1 48.3 100 988.91 granite T-GD149 1 78 133 972.66 granite
T-GD100 2 59 125 983.65 Limestone T-GD150 1 27.5 62 996.34 sandstone
T-GD101 1 34 72 994.31 sandstone T-GD151 1 82.4 131 970.36 granite
T-GD102 1 42 121 991.44 granite T-GD152 1 31 100 994.95 granite
T-GD103 2 48 96 988.91 Limestone T-GD153 1 48 111 988.91 sandstone
T-GD105 1 70 139 977.66 granite T-GD154 1 60 133 983.15 granite
T-GD106 1 61.3 164 982.6 granite T-GD155 1 57.4 132 984.66 granite
T-GD107 1 48.2 111 988.91 granite T-GD156 1 72.3 122 976.28 sandstone
T-GD109 1 80.5 136 971.64 sandstone T-GD157 1 72 114 976.28 sandstone
T-GD110 1 48.7 132 988.47 granite T-GD158 1 50 100 988.03 granite
T-GD111 1 52 135 987.09 granite T-GD159 1 63.1 125 981.5 granite
T-GD112 2 48 124 988.91 Limestone T-GD160 1 33 105 994.52 granite
T-GD113 1 47.2 128 989.35 granite T-GD161 1 34.5 67 994.31 granite
T-GD114 1 34 90 994.31 granite T-GD162 1 94.1 180 962.35 sandstone
T-GD115 1 28.5 60 995.95 granite T-GD163 1 87 102 967.11 granite
T-GD116 1 41.5 98 991.85 granite T-GD164 1 42.7 114 991.04 granite
T-GD117 1 32.7 106 994.52 granite T-GD165 1 59.5 122 983.65 sandstone
T-GD118 1 78.3 153 972.66 granite T-GD166 1 62.2 124 982.05 granite
T-GD119 1 35 94 994.09 granite T-GD167 1 38.8 127 992.62 granite
T-GD120 1 41 109 991.85 sandstone T-GD168 1 45 100 990.23 granite
T-GD121 1 55 108 985.67 granite T-GD169 1 75 146 974.2 granite
T-GD122 1 36 86 993.72 sandstone T-GD170 1 103 111 967.78 granite
T-GD123 1 71.6 118 976.28 granite T-GD171 1 56.8 161 984.66 granite
T-GD124 1 54.3 136 986.14 granite T-GD172 1 65.4 160 980.4 granite
T-GD125 1 63.5 110 981.5 sandstone T-GD173 1 55 111 985.67 granite
T-GD126 1 38.2 122 992.99 sandstone T-GD174 1 53.8 180 986.14 granite
T-GD127 1 72 135 976.28 sandstone T-GD175 1 65 128 980.4 granite
T-GD128 1 50 75 988.03 granite T-GD176 1 56.5 131 985.17 sandstone
T-GD129 1 47.1 119 989.35 granite T-GD177 1 59.3 125 983.65 granite
T-GD130 1 91.8 136 963.74 sandstone T-GD178 1 45.5 92 990.23 sandstone
T-GD131 1 46.8 107 989.35 granite T-GD179 1 59.1 106 983.65 sandstone
T-GD132 1 40.7 114 991.85 granite T-GD180 1 62 156 982.05 granite
T-GD133 1 94 144 967.11 sandstone T-GD181 1 33 102 994.52 granite
T-GD134 1 40.2 145 992.25 granite T-GD182 1 60.8 131 982.6 sandstone
T-GD135 1 54.4 126 986.14 granite T-GD183 1 34.5 88 994.31 sandstone
T-GD136 1 40.4 89 992.25 granite T-GD184 1 50.5 112 988.03 sandstone
T-GD185 1 60.8 57 994.52 sandstone T-GD237 1 71.3 113 976.97 granite
T-GD186 1 73 150 975.58 sandstone T-GD238 1 58.6 113 983.65 sandstone
T-GD187 1 78 110 972.66 granite T-GD239 2 39.8 79 992.25 Limestone
T-GD188 1 61.6 155 982.05 sandstone T-GD240 1 29 48 995.56 mudstone
T-GD189 1 55 118 985.67 sandstone T-GD242 1 28.5 90 995.95 geneiss
T-GD190 1 32 53 994.74 sandstone T-GD243 1 68 111 978.76 sandstone
T-GD191 1 58.2 114 984.16 granite T-GD244 1 47 146 989.35 granite
T-GD192 1 30 95 995.17 sandstone T-GD245 2 30 63 995.17 Limestone
T-GD193 1 26 39 996.73 sandstone T-GD246 1 55 92 985.67 granite
T-GD194 2 32 68 994.74 Limestone T-GD247 1 47.8 146 988.91 granite
T-GD196 1 101 131 957.38 granite T-GD248 1 65 160 980.4 granite
T-GD197 1 52 100 987.09 granite T-GD249 1 30 90 995.17 granite
T-GD198 1 38.9 110 992.62 granite T-GD250 1 42 100 991.44 granite
T-GD199 1 44 120 990.63 granite T-GD251 1 51.8 139 987.09 granite
T-GD200 1 37.7 110 992.99 granite T-GD252 2 40.2 76 992.25 Limestone
T-GD201 1 91 120 967.78 granite T-GD253 1 29.5 90 995.56 granite
T-GD202 1 81.3 131 971 sandstone T-GD254 2 35.7 70 993.72 Limestone
T-GD203 1 28 91 995.95 granite T-GD255 1 101 153 965.12 granite
T-GD204 1 56.3 118 985.17 granite T-GD256 1 34 90 994.31 granite
T-GD206 1 47 118 989.35 granite T-GD257 1 42 113 991.44 granite
T-GD207 1 56.2 118 985.17 granite T-GD258 1 48.9 152 988.47 granite
T-GD208 1 64 118 980.95 granite T-GD259 1 95 131 974.2 granite
T-GD209 1 47 145 989.35 granite T-GD260 1 48.2 96 988.91 granite
T-GD210 1 42 100 991.44 granite T-GD261 1 78.5 130 972.66 granite
T-GD211 1 44 102 990.63 granite T-GD262 1 74.5 138 974.89 granite
T-GD212 1 118.2 150 959.54 sandstone T-GD263 1 70.6 130 976.97 granite
T-GD213 2 31.2 70 994.95 Limestone T-GD264 1 55 143 985.67 granite
T-GD214 1 67.6 120 978.76 granite T-GD265 1 56.2 160 985.17 granite
T-GD215 1 55.5 118 985.67 granite T-GD266 1 68.9 105 978.21 granite
T-GD216 1 30 72 995.17 granite T-GD267 1 27 90 996.34 granite
T-GD217 1 51.2 92 987.56 granite T-GD268 2 47.9 96 988.91 Limestone
T-GD218 1 60 106 983.15 granite T-GD269 2 51.1 102 987.56 Limestone
T-GD219 1 37 111 993.35 granite T-GD270 1 59 149 983.65 sandstone
T-GD220 1 68 110 978.76 granite T-GD271 1 51.4 100 987.56 granite
T-GD221 1 58.2 145 984.16 granite T-GD272 1 28.5 90 995.95 granite
T-GD222 1 52.7 120 986.61 granite T-GD273 1 70.7 163 976.97 sandstone
T-GD223 1 31.5 63 994.95 sandstone T-GD274 1 28 90 995.95 granite
T-GD225 1 31 65 994.95 sandstone T-GD275 1 56.9 160 984.66 granite
T-GD226 1 48 100 988.91 sandstone T-GD276 1 53.1 136 986.61 granite
T-GD227 1 71.5 110 976.97 sandstone T-GD277 1 51.7 192 987.09 granite
T-GD229 1 48 110 988.91 granite T-GD278 1 60.7 160 982.6 sandstone
T-GD230 1 69.6 106 995.56 granite T-GD279 1 40.2 180 992.25 granite
T-GD231 1 48.2 110 988.91 granite T-GD280 1 86.1 110 967.78 granite
T-GD233 1 37.7 110 992.99 sandstone T-GD281 1 66.5 110 979.85 granite
T-GD234 1 60.7 120 982.6 sandstone T-GD282 1 59 132 983.65 granite
T-GD235 1 65.5 120 980.4 granite T-GD283 1 30 90 995.17 granite
T-GD236 1 36.5 86 993.72 granite T-GD284 1 78 142 972.66 granite
T-GD285 1 76 130 973.69 granite T-GD301 1 51.2 111 987.56 granite
T-GD286 1 83 129 969.72 granite T-GD302 1 45.5 80 990.23 sandstone
T-GD287 2 70.7 137 976.97 granite T-GD303 1 41.7 63 991.44 sandstone
T-GD288 1 50.5 134 988.03 sandstone T-GD304 1 83.6 194 969.08 granite
T-GD289 1 56.2 163 985.37 sandstone T-GD305 1 31.5 63 994.95 granite
T-GD290 1 70 110 977.66 granite T-GD306 1 70.8 108 976.67 sandstone
T-GD291 1 54.8 162 985.67 sandstone T-GD308 1 53.9 108 986.14 sandstone
T-GD292 1 66 165 979.85 sandstone T-GD309 1 76.3 152 973.69 granite
T-GD293 1 40 100 992.25 granite T-GD310 1 47.5 67 989.35 granite
T-GD294 1 68.8 120 978.21 granite T-GD311 1 36 97 993.72 granite
T-GD295 1 97.8 143 959.54 granite T-GD312 1 36.5 88 993.72 granite
T-GD296 1 71.6 172 976.28 sandstone T-GD313 1 52.8 100 986.61 granite
T-GD297 1 64 155 980.95 granite T-GD314 1 43 100 991.04 granite
T-GD298 1 110.2 167 959.54 granite T-GD315 1 77.4 120 973.18 sandstone
T-GD299 1 63.5 108 981.5 granite T-GD316 1 79 136 972.15 granite
T-GD300 1 59.8 107 983.15 granite T-GD317 1 76.5 186 973.69 granite

Geothermal fields data of uplifted mountain in Guangdong province (revised from The 4th Geological Brigade of Guangdong Geological Bureau (Zeng, 2015), and also referenced by ((Wang, et al., 2017)), number 1 in the Reservoir column of the table represents the fracture-banded reservoir, and number 2 represents the karst-layered reservoir).

Field Reservoir type Hot water temperature (°C) Reservoir temperature (°C) Hot water density (kJ·kg·°C−1) Reservoir lithology Field Reservoir type Hot water temperature (°C) Reservoir temperature (°C) Hot water density (kJ·kg·°C−1) Reservoir lithology
T-GD8 1 40 79 992.25 sandy conglomerate T-GD104 1 42.8 76 991.04 sandy conglomerate
T-GD9 1 28 79 995.95 sandy conglomerate T-GD108 1 30 60 995.17 sandy conglomerate
T-GD18 1 58.5 107 984.16 sandy conglomerate T-GD142 1 62.2 111 982.05 sandy conglomerate
T-GD20 1 56.8 107 984.66 sandy conglomerate T-GD205 1 42.3 64 991.44 sandy conglomerate
T-GD22 1 48 109 988.91 sandy conglomerate T-GD224 1 49.5 80 988.47 sandy conglomerate
T-GD26 1 42 100 991.44 sandy conglomerate T-GD228 1 68 100 978.76 sandy conglomerate
T-GD70 1 32.4 122 994.74 sandy conglomerate T-GD232 1 28.8 48 995.56 sandy conglomerate
T-GD92 1 43.8 101 990.63 sandy conglomerate T-GD241 1 30 83 995.17 sandy conglomerate
T-GD99 1 73.2 135 975.58 sandy conglomerate
4.3 Surface temperature

The surface temperature data of Guangdong province comes from the Climatic Research Unit gridded Time Series (CRU TS) climate dataset, CRU TS is one of the most widely used climate datasets in the world, produced by the UK’s National Centre for Atmospheric Science (NCAS), and provides monthly data with 0.5° resolution covering the land surface from 1901 to 2020. The CRU TS dataset has 10 sets of data based on near-surface measurements: temperature, precipitation, humidity, frost days, cloud cover, and potential transpiration (Harris, et al., 2020). To obtain the surface temperature of the whole Guangdong province, this research extracts the global monthly temperature data of this dataset from 2011 to 2020 and then calculates the average temperature of Guangdong Province, Hong Kong, and Macao as shown in Figure 6. The average surface temperature ( T s ) of the Guangdong coastal area can be obtained through spatial statistics calculations, which is 22.025°C. Therefore, in the estimation of the potential of the uplifted mountain geothermal resources, a uniform distribution model is applied to the surface temperature ( T s ) with a value of 22.025°C.

Distribution of average temperature in Guangdong province from 2011 to 2020 (obtained from the CRU TS climate dataset (Harris, et al., 2020)).

 5 Result 5.1 Simulation of the fracture-banded reservoir geothermal resources

To describe the uncertainty and diversity of input parameters, the density, specific heat capacity, and porosity in reservoir lithology are given to the triangle distribution model as shown in Table 1. The specific heat capacity of hot water and the volume of the geothermal reservoir are given to a uniform distribution model. The Montel Carlo simulation iteration is set as 2000 times. The Monte Carlo simulation results are shown in Figure 7.

Simulation results for the fracture-banded reservoir geothermal resources in Guangdong province. (A) Possibility distribution figure of the fracture-banded reservoir geothermal resources; (B) Cumulative frequency figure of the fracture-banded reservoir geothermal resources).

Figure 7A. indicates that the geothermal resources reserve value of the fracture-banded reservoir in Guangdong province changed from 5.648×1016 kJ to 5.867×1016 kJ with a mean value of (5.743×1016 kJ). And the highest probability exceeds 8.5% with geothermal resources reserves value of approximately 5.743×1016 kJ. Figure 7B presents that a 90% probability of geothermal resources reserves value ranges from 5.6897×1016 kJ to 5.7932×1016 kJ.

 5.2 Simulation of the karst-layered reservoir geothermal resources

As for the karst-layered reservoir geothermal resources, the Monte Carlo simulation results are shown in Figure 8 with 2000 times iterations. Figure 8A. indicates that the geothermal resources reserve value of the karst-layered reservoir in Guangdong province changed from 5.089×1015 kJ to 5.536×1015 kJ with a mean value of (5.328×1015 kJ). And the highest probability approaches 8% with geothermal resources reserves value of approximately 5.345×1015 kJ. Figure 8B. Presents that a 90% probability of geothermal resources reserves value ranges from 5.2126×1015 kJ to 5.4457×1015 kJ.

Simulation results for the karst-layered reservoir geothermal resources in Guangdong province. (A) Possibility distribution figure of the karst-layered reservoir geothermal resources; (B) Cumulative frequency figure of the karst-layered reservoir geothermal resources).

 5.3 Monte Carlo simulation of uplifted mountain geothermal resources in Guangdong province

The finding suggests that uplifted mountain geothermal resources along the Guangdong province are abundant, the geothermal resources reserves value are shown in Figure 9 with 2000 times Monte Carlo simulation iteration. Figure 9A. indicates that uplifted mountain geothermal resources in Guangdong province are estimated to change from 6.176×1016 kJ to 6.399×1016 kJ with a mean value of (6.275×1016 kJ), and the highest probability exceeds 8% with geothermal resources reserves value of approximately 6.28×1016 kJ. Figure 9B. Presents that a 90% probability of uplifted mountain geothermal resources reserves value ranges from 6.2215×1016 kJ to 6.3286×1016 kJ.

Simulation results for uplifted mountain geothermal resources in Guangdong province. (A) Possibility distribution figure of uplifted mountain geothermal resources; (B) Cumulative frequency figure of uplifted mountain geothermal resources).

We can also find that the existing uplifted mountainous geothermal resources potential in Guangdong Province is enormous, and the main geothermal reservoir type of geothermal resources is the fracture-banded reservoir supplemented by the karst-layered reservoir, the geothermal resources reserves of the fracture-banded reservoir are about 10 times that of the karst-layered reservoir. The total amount of uplifted mountainous geothermal resources is equal to 2.11–2.18×105 Ten thousand tons of standard coal (mean 2.14×105 Ten thousand tons of standard coal).

 6 Conclusion

This investigation utilized the volume method as its foundation and accounted for the uncertainty of geothermal reservoir parameters by implementing Monte Carlo simulation. Using both triangular and uniform distribution models to simulate input parameters of geothermal fields in Guangdong province, the study successfully evaluated the potential of uplifted mountain geothermal resources within the area. The findings highlight that the fracture-banded reservoir geothermal resources are estimated to range from 5.648–5.867×1016 kJ (mean 5.743×1016 kJ), while the karst-layered reservoir geothermal resources are approximated to be between 5.089–5.536×1015 kJ (mean 5.328×1015 kJ). Ultimately, these results suggest that there is significant potential for uplifted mountain geothermal resources along the Guangdong province, with estimates ranging from 6.176–6.399×1016 kJ (mean 6.256×1016 kJ). It also shows that uplifted mountain geothermal resources are very rich in Guangdong, and the main type of geothermal reservoir is the fracture-banded reservoir supplemented by the karst-layered reservoir. The geothermal resource reserves of the fracture-banded reservoir are about 10 times that of the karst-layered reservoir. The total amount of uplifted mountainous geothermal resources is equal to 2.11–2.18×105 Ten thousand tons of standard coal (mean 2.14×105 Ten thousand tons of standard coal). Guangdong province’s geothermal resources can provide a new direction for the transformation of Guangdong’s energy structure from fossil energy to renewable energy.

 Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

 Author contributions

YL: conceptualization, methodology, investigation resources, formal analysis, writing–original Draft; XL: data curation, supervision; JL conceptualization, investigation, supervision, writing review and editing. All authors contributed to the article and approved the submitted version.

 Funding

This study was financially supported by the National Natural Science Foundation of China (Grant. Nos 52104209, 52174083, 52172625, and U22A20166).

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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