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<p>Hydrothermal systems in mountainous areas controlled by faults commonly exist in China. Effectively managing existing exploration wells is crucial for utilizing geothermal resources. This study applied an integrated approach combining numerical simulation, artificial neural networks (ANN), and genetic algorithms (GA) to optimize the flow rates of those wells. Firstly, a three-dimensional thermo-hydraulic coupled numerical model of the Hongjiang geothermal field was established. Secondly, an ANN was applied to replicate the behaviour of numerical simulators. Finally, with the objective of maximizing extraction temperature, the genetic algorithm was employed to optimize the well flow rates. The results show that the increase in distance between the production wells and reinjection wells enhances the production temperature. When cold water is injected into deeper sections of the geothermal reservoir, the production temperature decreases by up to 11.5 ℃ compared to injection in shallower sections. Therefore, the wells exposing the shallower zone of the geothermal reservoir should be selected as reinjection wells. The proposed optimization modelling technique can be utilized as a base to derive elaborate models that include more operational scenarios, and the optimized results can be a reference for similar geothermal sites.</p>

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Heat extraction strategies in Hongjiang geothermal field insight from numerical simulation and artificial neural network

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Junliang Sun,
  • Kai Liu,
  • Qingcheng He,
  • Shouchuan Zhang,
  • Luyao Wang,
  • Tingxi Yu

摘要

Hydrothermal systems in mountainous areas controlled by faults commonly exist in China. Effectively managing existing exploration wells is crucial for utilizing geothermal resources. This study applied an integrated approach combining numerical simulation, artificial neural networks (ANN), and genetic algorithms (GA) to optimize the flow rates of those wells. Firstly, a three-dimensional thermo-hydraulic coupled numerical model of the Hongjiang geothermal field was established. Secondly, an ANN was applied to replicate the behaviour of numerical simulators. Finally, with the objective of maximizing extraction temperature, the genetic algorithm was employed to optimize the well flow rates. The results show that the increase in distance between the production wells and reinjection wells enhances the production temperature. When cold water is injected into deeper sections of the geothermal reservoir, the production temperature decreases by up to 11.5 ℃ compared to injection in shallower sections. Therefore, the wells exposing the shallower zone of the geothermal reservoir should be selected as reinjection wells. The proposed optimization modelling technique can be utilized as a base to derive elaborate models that include more operational scenarios, and the optimized results can be a reference for similar geothermal sites.