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Utah FORGE 6-3712: Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks 2024 Annual Workshop Presentation
This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Jesse Williams. This video slide presentation discusses the development of machine learning-based predictive tools to estimate...
Williams, J. Energy and Geoscience Institute at the University of Utah
Sep 17, 2024
1 Resources
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Curated
1 Resources
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Curated
Utah FORGE: 2024 Annual Report on Activities and Advancements
This 2024 annual report for Phase 3B Year 2 at Utah FORGE provides an in-depth account of activities and advancements made at the site. Key achievements include drilling and stimulating the production well 16B(78)-32, creating a geothermal reservoir, and achieving commercial-scale...
McLennan, J. et al Energy and Geoscience Institute at the University of Utah
Jan 27, 2025
1 Resources
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Curated
1 Resources
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Curated
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
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Publicly accessible
4 Resources
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Publicly accessible