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G-Function Library for Modeling Vertical Bore Ground Heat Exchanger

This library contains g-functions (thermal response functions) for standard, regularly spaced vertical borehole ground heat exchangers. In total, it contains 34, 321 configurations. To permit interpolation, each configuration has g-functions for heights of 24, 48, 96, 192, and 384...

Spitler, J. et al Oak Ridge National Laboratory

Jun 30, 2021
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Coupling Subsurface and Above-Surface Models for Optimizing the Design of Borefields and District Heating and Cooling Systems

Accurate dynamic energy simulation is important for the design and sizing of district heating and cooling systems with geothermal heat exchange for seasonal energy storage. Current modeling approaches in building and district energy simulation tools typically consider heat conduct...

Hu, J. et al Lawrence Berkeley National Laboratory

Jan 31, 2022
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Regulatory and Permitting Information Desktop (RAPID) Toolkit

The Regulatory and Permitting Information Desktop (RAPID) Toolkit combines the former Geothermal Regulatory Roadmap, National Environmental Policy Act (NEPA) Database, and other resources into a Web-based tool that gives the regulatory and utility-scale geothermal developer commun...

Young, K. and Levine, A. National Renewable Energy Laboratory

Sep 30, 2014
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Rare Earth Geochemistry of Rock Core form WY Reservoirs

These data include major, minor, trace and rare earth element concentration of geologic formations in Wyoming oil and gas fields. *Note Link below contains updated version of spreadsheet (6/14/2017)

Quillinan, S. et al University of Wyoming

Oct 01, 2016
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Hydrothermal mineral alterations in the Brady and Desert Peak geothermal fields

Results of the analysis of HyMap's spectra against know hydrothermally altered minerals in the Brady-Desert Peak Geothermal Areas. The analysis was performed using ENVI's Target Detection process against USGS library spectra for Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite...

Moraga, J. Colorado School of Mines

Oct 01, 2021
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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
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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
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