GOOML - Real World Applications of Machine Learning in Geothermal Operations

Paul Siratovich, Andrea Blair, Andrew Marsh, Grant Buster, Nicole Taverna, Jon Weers, Christine Siega, Alex Urgel, Warren Mannington, Jonathan Cen, Jaime Quinao, Robbie Watt, John Akerley

Research output: Contribution to conferencePaperpeer-review

1 Scopus Citations


GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.

Original languageAmerican English
Number of pages8
StatePublished - 2022
Event2022 Geothermal Rising Conference: Using the Earth to Save the Earth, GRC 2022 - Reno, United States
Duration: 28 Aug 202231 Aug 2022


Conference2022 Geothermal Rising Conference: Using the Earth to Save the Earth, GRC 2022
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2022 Geothermal Resources Council. All rights reserved.

NREL Publication Number

  • NREL/CP-6A20-86437


  • forecasting
  • Machine Learning
  • operations
  • optimization
  • shut planning


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