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Utah FORGE: Reports on Northwestern Nevada Well Doublet Drilling and Testing by Fervo Energy

These reports review Fervo Energy's construction of a commercial enhanced geothermal system (EGS). Fervo has qualified full functionality of the system through production testing at commercially relevant operating conditions. The project site is located in a nearfield setting ad...

Dadi, S. et al Fervo Energy

Sep 05, 2023
2 Resources
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University of Illinois Campus Deep Direct-Use Feasibility Study Preliminary Geothermal Reservoir Model

Preliminary geothermal reservoir simulations were performed using a homogeneous static model to evaluate and understand the effects of fluid and rock properties that could influence the delivery of thermal energy in a doublet system. A 5000 feet by 5100 feet by 500 feet homogeneou...

Okwen, R. University of Illinois

May 08, 2018
4 Resources
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Utah FORGE: Well 16B(78)-32 Logs from Schlumberger Technologies

This dataset is a collection of well logs provided by Schlumberger Technologies from the Utah FORGE well 16B(78)-32 drilling project. Information here includes critical borehole information collected by an ultrasonic borehole imager (UBI) and a fullbore formation microimager (FMI)...

May, D. and Jones, D. Energy and Geoscience Institute at the University of Utah

Sep 14, 2023
7 Resources
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Utah FORGE: Well 16B(78)-32 Drilling Data

This drilling data for Utah FORGE well 16B(78)-32 include a well survey, core summary, mud and mud temperature logs, daily reports of the drilling process, and additional data from the Pason oil and gas company. Well 16B(78)-32 serves as the production well for reservoir creation...

McLennan, J. et al Energy and Geoscience Institute at the University of Utah

Jul 03, 2023
9 Resources
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Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

This data set includes the numerical modeling input files and output files used to synthesize data, and the reduced-order machine learning models trained from the synthesized data for reservoir thermal energy storage site identification. In this study, a machine-learning-assiste...

Jin, W. et al Idaho National Laboratory

Apr 15, 2022
4 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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