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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
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 producti...
Siler, D. et al United States Geological Survey
Oct 01, 2021
6 Resources
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6 Resources
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Self-Healing and Re-Adhering Polymer-Cements with Improved Toughness
Polymer-cement experiments were conducted in order to assess the chemical and thermal properties of various polymer-cement composites. This file set includes the following polymer-cement analyses:
Polymer-Cement Composite Synthesis
Polymer-Cement Interactions by Atomistic Simulat...
Fernandez, C. Pacific Northwest National Laboratory
Nov 11, 2015
10 Resources
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10 Resources
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Play Fairway Analysis Retrospective GeoRePORTs
NREL, as part of the Play Fairway Analysis (PFA) Retrospective and with assistance from PFA PIs, completed GeoRePORTs for the sites identified in Phases 1&2 of the DOE PFA projects.
The GeoRePORT (geothermal resource portfolio optimization and reporting technique) uses two factor...
Kolker, A. et al National Renewable Energy Laboratory
Dec 01, 2020
36 Resources
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36 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results
Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells increasing or decreasing the fluid flow rates across the wells and drilling new wells at appropriate locations. Th...
Beckers, K. et al National Renewable Energy Laboratory
Oct 20, 2021
6 Resources
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6 Resources
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Publicly accessible