Work

Robust optimization of fully coupled geothermal reservoir and power plant system based on deep learning

Year: 2026

Type: article

Abstract: In geothermal recovery, the reservoir production temperature decline can affect the power plant’s efficiency and electricity output. Therefore, the coupling between the reservoir and the power plant is crucial for accurate estimation of the power plant’s performance. Current studies couple the numerical simulation models of the reservoir and the power plant developed based on physics. However, such simulations are usually computationally inefficient when performing predictions, thus becoming bottleneck in both forward and inverse modeling tasks. Therefore, we aim to accelerate the forward and inverse modeling of the coupled model by replacing the numerical simulation models with deep learning-based surrogate models. In this study, we first develop surrogate models of the geothermal reservoir and power plant, and further couple them into as an integrated forward model through heat source conditions. Further, a multi-objective optimizer combining the forward model is applied to optimize the coupled system. Surrogate models of the reservoir and power plant can predict the wellhead production temperature and electricity with mean relative errors of 0.49% and 1.67% while achieving CPU speedup at 6.92×104 and 1.77×105 times compared to physics simulators, respectively. Besides, the surrogate-based optimization is 6.05×105 times faster than the simulation-based one. The results demonstrate much higher computational efficiency of our coupled model in both the forward and inverse modeling with negligible trade-off in accuracy, as compared to the current physics-based coupled simulation models. This workflow significantly accelerates the procedures of feasibility assessments of geothermal projects as well as the decision making of the geothermal reservoir and the power plant.

Source: Energy Conversion and Management X

DOI: 10.1016/j.ecmx.2026.101556

Authors: Ziyou Liu, Manojkumar Gudala, Klemens Katterbauer, Bicheng Yan

Language: English

Topic: Geothermal Energy Systems and Applications

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $3,170

Funders: King Abdullah University of Science and Technology, Saudi Aramco