AUTHOR=Li Yongyi , Long Xiting , Lu Jun TITLE=Evaluation of geothermal resources potential in the uplifted mountain of Guangdong province using the Monte Carlo simulation JOURNAL=Frontiers in Earth Science VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2023.1233026 DOI=10.3389/feart.2023.1233026 ISSN=2296-6463 ABSTRACT=

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).