TY - JOUR
T1 - Simulation-optimization with machine learning for geothermal reservoir recovery
T2 - Current status and future prospects
AU - Rajabi, Mohammad Mahdi
AU - Chen, Mingjie
N1 - Publisher Copyright:
© The Author(s) 2022.
PY - 2022/7/7
Y1 - 2022/7/7
N2 - In geothermal reservoir management, combined simulation-optimization is a practical approach to achieve the optimal well placement and operation that maximizes energy recovery and reservoir longevity. The use of machine learning models is often essential to make simulation-optimization computational feasible. Tools from machine learning can be used to construct data-driven and often physics-free approximations of the numerical model response, with computational times often several orders of magnitude smaller than those required by reservoir numerical models. In this short perspective, we explain the background and current status of machine learning based combined simulation-optimization in geothermal reservoir management, and discuss several key issues that will likely form future directions.
AB - In geothermal reservoir management, combined simulation-optimization is a practical approach to achieve the optimal well placement and operation that maximizes energy recovery and reservoir longevity. The use of machine learning models is often essential to make simulation-optimization computational feasible. Tools from machine learning can be used to construct data-driven and often physics-free approximations of the numerical model response, with computational times often several orders of magnitude smaller than those required by reservoir numerical models. In this short perspective, we explain the background and current status of machine learning based combined simulation-optimization in geothermal reservoir management, and discuss several key issues that will likely form future directions.
KW - Geothermal energy
KW - data-driven modeling
KW - optimal well placement
KW - optimization algorithms
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U2 - 10.46690/ager.2022.06.01
DO - 10.46690/ager.2022.06.01
M3 - Short survey
AN - SCOPUS:85143605498
SN - 2207-9963
VL - 6
SP - 451
EP - 453
JO - Advances in Geo-Energy Research
JF - Advances in Geo-Energy Research
IS - 6
ER -