The U.S. Department of Energy (DOE) awarded up to $5.5 million for 10 new projects to apply machine learning techniques to geothermal exploration and production, the DOE said in a statement late Friday.
According to the statement, the machine learning technique, which involves the use of advanced algorithms to identify patterns in and make inferences from data, could assist in finding and developing new geothermal resources.
"If applied successfully, machine learning could lead to higher success rates in exploratory drilling, greater efficiency in plant operations, and ultimately lower costs for geothermal energy," the DOE said.
The projects selected by the Office of Energy Efficiency and Renewable Energy’s Geothermal Technologies Office are mainly for U.S. universities and laboratories and focus on two areas: machine learning for geothermal exploration and advanced analytics for efficiency and automation in geothermal operations.
The project recipients include the Colorado School of Mines, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, Pennsylvania State University, University of Arizona, University of Houston, University of Nevada, University of Southern California and New Zealand-based Upflow Limited.
The statement shows that geothermal energy is an important part of the DOE’s strategy to advance toward energy dominance and ensure a secure, reliable, resilient, affordable, and enduring supply of American energy.
By Gulsen Cagatay