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Hybrid machine learning model to predict 3D in-situ permeability evolution
Enhanced geothermal systems (EGS) can provide a sustainable and renewable solution to the new energy transition. Its potential relies on the ability to create a reservoir and to accurately evaluate its evolving hydraulic properties to predict fluid flow and estimate ultimate therm...
Elsworth, D. and Marone, C. Pennsylvania State University
Nov 22, 2022
4 Resources
0 Stars
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4 Resources
0 Stars
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Focusing Wave Energy for Wave Energy Converter Applications
Wave tank tests at Stevens Institute of Technology quantified the ability of near-surface platforms to concentrate wave energy over the platform.
Due to the instantaneous change in water depth, mass, energy, and power are conserved in this process. The energy and power concentrat...
Raftery, M. Martin and Ottaway
Aug 10, 2010
7 Resources
0 Stars
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7 Resources
0 Stars
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Snake River Plain FORGE: Site Characterization Data
The site characterization data used to develop the conceptual geologic model for the Snake River Plain site in Idaho, as part of phase 1 of the Frontier Observatory for Research in Geothermal Energy (FORGE) initiative. This collection includes data on seismic events, groundwater,...
Moos, D. and Barton, C. Idaho National Laboratory
Apr 18, 2016
49 Resources
0 Stars
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49 Resources
0 Stars
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INGENIOUS Great Basin Regional Dataset Compilation
This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while re...
Ayling, B. et al GBCGE, NBMG, UNR
Jun 30, 2022
16 Resources
0 Stars
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16 Resources
0 Stars
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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
0 Stars
Publicly accessible
6 Resources
0 Stars
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Unified Field Study 6 (UFS-6)
The Unified Field Studies (UFS), established by the Algae Testbed Public-Private Partnership (ATP3), produced data on the effect of environmental and process conditions on algal growth rates and algal composition. The goal of the sixth Unified Field Study (UFS-6), performed March ...
Wolfrum, E. et al National Renewable Energy Laboratory
Oct 20, 2017
24 Resources
0 Stars
Publicly accessible
24 Resources
0 Stars
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Appalachian Basin Temperature-Depth Maps and Structured Data in support of Feasibility Study of Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy
This dataset contains shapefiles and rasters that summarize the results of a stochastic analysis of temperatures at depth in the Appalachian Basin states of New York, Pennsylvania, and West Virginia. This analysis provides an update to the temperature-at-depth maps provided in the...
Smith, J. Cornell University
Oct 29, 2019
6 Resources
0 Stars
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6 Resources
0 Stars
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DEEPEN 3D PFA Favorability Models and 2D Favorability Maps at Newberry Volcano
DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments.
Part of the DEEPEN project involved developing and testing a methodology for a 3D play fairway analysis (PFA) for multiple play types (conventional hydrothermal, superhot EGS, and supercritical...
Taverna, N. et al National Renewable Energy Laboratory
Jun 30, 2023
27 Resources
0 Stars
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27 Resources
0 Stars
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Research Report of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin
This is a final report summarizing a one-year (2014-15) DOE funded Geothermal Play Fairway Analysis of the Low-Temperature resources of the Appalachian Basin of New York, Pennsylvania and West Virginia. Collaborators included Cornell University, Southern Methodist University, and...
Jordan, T. et al Cornell University
Sep 30, 2015
22 Resources
0 Stars
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22 Resources
0 Stars
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