Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

Dataset · Fri Oct 01 04:00:00 EDT 2021

DOI:https://doi.org/10.15121/1832133· OSTI ID:1832133

Siler, Drew ^[1]; Pepin, Jeff D.; Vesselinov, Velimir V.; Mudunuru, Maruti K.; Ahmmed, Bulbul

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1. United States Geological Survey

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 Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along 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. This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below).

• Research Organization: DOE Geothermal Data Repository; United States Geological Survey
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
• Contributing Organization: United States Geological Survey
• OSTI ID: 1832133
• Report Number(s): 1344
• Availability: [e-mail]
• Country of Publication: United States
• Language: English

Related Subjects

15 GEOTHERMAL ENERGY
3D geologic map
3D well data
BHS
Brady
Brady Hot Springs
GeoThermalCloud
ML
NMFK
Nonnegative Matrix Factorization k-means
SmartTensors
characterization
clustering
code
energy
faults
geologic model
geologic structure
geology
geothermal
hydrothermal
k-means
machine learning
matrix factorization
nonnegative matrix factorization
production
stress
unsupervised