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Utah FORGE: Well 16A(78)-32 2022 Stimulation Microseismic Report
This is a Utah FORGE well 16A(78)-32 stimulation microseismic detection and event location report from Silixa LLC. The report covers the digital acoustic sensing (DAS) data acquisition and analysis used to study microseismic events during the April, 2022 stimulations at well 16A(7...
LLC, S. Energy and Geoscience Institute at the University of Utah
Sep 26, 2022
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1 Resources
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Improved Microseismicity Detection During Newberry EGS Stimulations
Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are oft...
Templeton, D. Lawrence Livermore National Laboratory
Oct 01, 2013
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1 Resources
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Improved Microseismicity Detection During Newberry EGS Stimulations
Effective enhanced geothermal systems (EGS) require optimal fracture networks for efficient heat transfer between hot rock and fluid. Microseismic mapping is a key tool used to infer the subsurface fracture geometry. Traditional earthquake detection and location techniques are oft...
Templeton, D. Lawrence Livermore National Laboratory
Nov 01, 2013
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1 Resources
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Thermal Drawdown Induced Flow Channeling in Fractured Geothermal Reservoirs: Rock Mechanics and Rock Engineering
We investigate the flow-channeling phenomenon caused by thermal drawdown in fractured geothermal reservoirs. A discrete fracture network-based, fully coupled thermal "hydrological" mechanical simulator is used to study the interactions between fluid flow, temperature change, and t...
Fu, P. et al Lawrence Livermore National Laboratory
Nov 15, 2015
1 Resources
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1 Resources
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MT Data: Newberry 4D Monitoring EGS Project
This submission contains a link to the EDX Collaborative Workspace where the MT data collected in support of the DOE GTO 4D EGS monitoring project is stored.
Daily production reports- Oregon State University (OSU) had 6 stations running continuously.
--Dynamic survey map, KML f...
Rose, K. National Energy Technology Laboratory
Apr 12, 2016
1 Resources
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1 Resources
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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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1 Resources
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Utah FORGE: GES Well 16A(78)-32 and Well 16B(78)-32 Stimulation Seismic Event Catalogs
This dataset contains seismic event catalogs from the hydraulic stimulation of wells 16A(78)-32 and 16B(78)-32 at the Utah FORGE site in April 2024. The data was collected by Geo Energy Suisse (GES) using a variety of seismic monitoring technologies, including 3-component (3C) geo...
Dyer, B. et al University of Utah Seismograph Stations
Apr 30, 2024
2 Resources
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2 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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6 Resources
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A Thermal-Hydrological-Chemical Model for the EGS Demonstration Project at Newberry Volcano, OR
Newberry Volcano in Central Oregon is the site of a Department of Energy funded Enhanced Geothermal System (EGS) Demonstration Project. Stimulation and production of an EGS is a strong perturbation to the physical and chemical environment, giving rise to coupled Thermal-Hydrologic...
Sonnenthal, E. et al National Energy Technology Laboratory
Jan 30, 2012
1 Resources
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1 Resources
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Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs
Subsurface data analysis, reservoir modeling, and machine learning (ML) techniques have been applied to the Brady Hot Springs (BHS) geothermal field in Nevada, USA to further characterize the subsurface and assist with optimizing reservoir management. Hundreds of reservoir simulat...
Beckers, K. et al National Renewable Energy Laboratory
Feb 18, 2021
1 Resources
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1 Resources
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Life Cycle Water Consumption and Water Resource Assessment for Utility-Scale Geothermal Systems: An In-Depth Analysis of Historical and Forthcoming EGS Projects
This report is the third in a series of reports sponsored by the U.S. Department of Energy Geothermal Technologies Program in which a range of water-related issues surrounding geothermal power production are evaluated. The first report made an initial attempt at quantifying the li...
Schroeder, J. et al Argonne National Laboratory
Aug 31, 2013
11 Resources
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11 Resources
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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
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4 Resources
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Newberry EGS Demonstration: Well 55-29 Stimulation Data
The Newberry Volcano EGS Demonstration in central Oregon, a 3 year project started in 2010, tests recent technological advances designed to reduce the cost of power generated by EGS in a hot, dry well (NWG 55-29) drilled in 2008. First, the stimulation pumps used were designed to ...
T., T. AltaRock Energy Inc
Dec 08, 2012
136 Resources
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136 Resources
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EGS Collab Experiment 1: Microseismic Monitoring
The U.S. Department of Energy's Enhanced Geothermal System (EGS) Collab project aims to improve our understanding of hydraulic stimulations in crystalline rock for enhanced geothermal energy production through execution of intensely monitored meso-scale experiments. The first expe...
Schoenball, M. et al Lawrence Berkeley National Laboratory
Jul 29, 2019
46 Resources
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46 Resources
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