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Raft River Idaho Magnetotelluric Data

Raw magnetotelluric (MT) data covering the geothermal system at Raft River, Idaho. The data was acquired by Quantec Geoscience. This is a zipped file containing .edi raw MT data files.

Nash, G. University of Utah

May 13, 2015
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EGS Collab Experiment 2: Hydraulic Pressure Test Results

The EGS Collab experiment 2 was focused on testing shear stimulation techniques. Shear stimulation, in this case, means using hydraulic pressure to cause shear slip on preexisting fracture or fault planes such that the hydraulic conductivity of the fracture or fault increases. The...

Burghardt, J. et al Lawrence Berkeley National Laboratory

Feb 17, 2023
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Gd Uptake Experiments for Preliminary Set of Functionalized Adsorbents (With Content Model)

These data summarize adsorption experiments conducted with Gd in 0.5 M NaCl. Results represent preliminary, proof-of-concept data utilizing fine-powder silica gel as the adsorbent support. Future testing will focus on larger, application-appropriate beads.

Noack, C. Carnegie Mellon University

Jul 01, 2015
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Dynamic Earth Energy Storage: Terawatt-Year, Grid-Scale Energy Storage using Planet Earth as a Thermal Battery (GeoTES): Seedling Project Final Report

Grid-scale energy storage has been identified as a needed technology to support the continued build-out of intermittent renewable energy resources. As of April 2017, the U.S. had approximately 24.2 GW of energy storage on line, compared to 1,081 GW of installed generation capacity...

McLing, T. et al Idaho National Laboratory

May 31, 2019
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Geothermal Thermoelectric Generation (G-TEG) with Integrated Temperature Driven Membrane Distillation and Novel Manganese Oxide for Lithium Extraction Final Report

This project aims to develop an innovative Geothermal ThermoElectric Generation (G-TEG) system specially designed to both generate electricity and extract high-value lithium (Li) from low-temperature geothermal brines. The process combined five modular technologies including silic...

Renew, J. et al Southern Research Institute

Mar 01, 2018
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Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions

This report reviews the training of machine learning algorithms to laboratory triaxial ultrasonic velocity data for Utah FORGE Well 16A(78)-32. Three machine learning (ML) predictive models were developed for the prediction of vertical and two orthogonally oriented horizontal str...

Kelley, M. et al Battelle Memorial Institute

Jun 19, 2023
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Energy Innovation Portal

About the Energy Innovation Portal. The Energy Innovation Portal is a one-stop resource for Department of Energy (DOE) Energy Efficiency and Renewable Energy (EERE) technologies. This application enables users to locate technologies developed with DOE funding and available for lic...

Webmaster, . and Laboratory, N. National Renewable Energy Laboratory

Nov 25, 2014
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SMP and Fracture Modeling

The problem of loss circulation in geothermal wells is inherently challenging due to high temperatures, brittle rocks, and presence of abundant fractures. Because of the inherent challenges in geothermal environments, there are limitations in selecting proper lost circulation mate...

Salehi, S. et al University of Oklahoma

Oct 01, 2021
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Utah FORGE Project 2439: A Multi-Component Approach to Characterizing In-Situ Stress

Core-based in-situ stress estimation, Triaxial Ultrasonic Velocity (labTUV) data, and Deformation Rate Analysis (DRA) data for Utah FORGE well 16A(78)-32 using triaxial ultrasonic velocity and deformation rate analysis. Report documenting a multi-component approach to characterizi...

Bunger, A. et al Battelle Memorial Institute

Dec 13, 2022
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Oilwell Conversion (Well API 121913310501) to Geothermal Heat Storage Well for Flexible Electricity Storage

Geothermal growth is limited by a lack of geographically dispersed high-temperature thermal resources and high initial upfront investment in characterization and well construction. This project intended to address the challenges of energy supply intermittency and enhance grid resi...

Malkewicz, N. et al University of Illinois

May 05, 2021
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