Machine learning can help discover and develop new geothermal resources. If successfully applied, it can improve the success rate of exploratory drilling, increase the efficiency of plant operations, and ultimately lead to lower geothermal energy costs.
What is machine learning?
Machines can learn just like humans can learn. Machines “learn” when the algorithms that run the programs are taught to predict, classify, and discover important insights during data mining.Think of the computer learning how to beat the checkers master after playing against him many times. Machine learning is the use of advanced algorithms to identify patterns and make inferences from data.
Machine learning and artificial intelligence can provide impactful insights into large and complex datasets, such as those used to analyze geothermal energy.
Machine learning and geothermal
Starting in 2018, the Geothermal Office (OG), formerly known as the Geothermal Technology Office (GTO), funded early-stage research and development applications in machine learning to develop technical and operational improvements in the exploration of geothermal resources.
The rapidly evolving field of machine learning offers significant opportunities for technological advancement and cost reduction throughout the lifecycle of geothermal projects, from resource exploration to power plant operations.
OG machine learning project goals:
Use datasets to provide impactful insights and encourage diverse machine learning techniques to advance geothermal exploration.
- Helps locate and predict new geothermal resources, increasing the success rate of exploration drilling.
- It increases the efficiency of plant operations and reduces costs for geothermal energy operators.
Over $9 million was invested across Phases 1 and 2 of OG’s machine learning initiative, focusing on two areas:
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- Phase 1 ($5.5 million): Machine learning for geothermal exploration: OG has funded projects to advance geothermal exploration through the application of machine learning techniques to geological, geophysical, geochemical, borehole, and other relevant datasets. Of particular interest was a project aimed at identifying drilling targets for future work.
- Colorado School of Mines (Golden, Colorado)
- Lawrence Livermore National Laboratory (Livermore, California)
- Los Alamos National Laboratory (Los Alamos, New Mexico)
- National Renewable Energy Laboratory (Golden, Colorado)
- Pennsylvania State University (University Park, Pennsylvania)
- University of Arizona (Tucson, Arizona)
- University of Houston (Houston, Texas)
- University of Nevada (Reno, Nevada)
- University of Southern California (Los Angeles, California)
- Upflow Limited (Taupo, New Zealand)
- Phase 2 ($3.5 million) Advanced Analytics for Efficiency and Automation of Geothermal Operations: OG has also funded projects that apply advanced analytics to datasets at power plants and other operators to improve operations and resource management.
- Colorado School of Mines (Golden, Colorado)
- Los Alamos National Laboratory (Los Alamos, New Mexico)
- Pennsylvania State University (University Park, Pennsylvania)
- University of Houston (Houston, Texas)
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Learn more about recent work on hydrothermal resources and other OG priorities.
