Machine learning to identify geologic factors associated with production in geothermal fields: A casestudy using 3D geologic data, Brady geothermal field, Nevada
- US Geological Survey, Moffett Field, Ca (United States)
- US Geological Survey, Albuquerque, NM (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in the Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid flow pathways are relatively rare in the subsurface but are critical components of hydrothermal systems like Brady and many other types of fluid flow systems in fractured rock. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of fourteen 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate the macro-scale faults and a local step-over in the fault system preferentially occur along with production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate 1) the specific geologic controls on the Brady hydrothermal system and 2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); US Geological Survey, Moffett Field, Ca (United States); US Geological Survey, Albuquerque, NM (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
- DOE Contract Number:
- 89233218CNA000001
- OSTI ID:
- 1781346
- Report Number(s):
- LA-UR--21-24125
- Country of Publication:
- United States
- Language:
- English