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EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog from Double-Difference Seismic Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog obtained for a double-difference seismic tomography study. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-wave velocity (km/s), P-wave velocity quality indicator (1 for we...

Chai, C. et al Oak Ridge National Laboratory

Jun 01, 2020
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EGS Collab: 3D Geophysical Model Around the Sanford Underground Research Facility

This package contains data associated with a proceedings paper (linked below) submitted to the 44th Workshop on Geothermal Reservoir Engineering. The Geophysical Model text file contains density, P and S-wave seismic speeds on a 3D grid. The file has six columns and provides latit...

Chai, C. et al Lawrence Berkeley National Laboratory

Feb 06, 2019
3 Resources
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Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity producti...

Siler, D. et al United States Geological Survey

Oct 01, 2021
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EGS Collab Experiment 1: 3D Seismic Velocity Model and Updated Microseismic Catalog Using Transfer-Learning Aided Double-Difference Tomography

This package contains a 3D Seismic velocity model and an updated microseismic catalog associated with a proceedings paper (Chai et al., 2020) published in the 45th Workshop on Geothermal Reservoir Engineering. The 3D_seismic_velocity_model text file contains x (m), y(m), z(m), P-w...

Chai, C. et al Oak Ridge National Laboratory

Apr 20, 2020
7 Resources
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Soda Lake Geothermal: Raw 3D and 3C Seismic-Reflection Data from 2010 Survey

This dataset contains seismic-reflection records created in 2010 around the Soda Lake geothermal field near Fallon, Nevada. The data was collected by the power plant operator at the time, Magma Energy (CYRQ Energy in 2024). This was a petroleum-industry-quality three-dimensional ...

N. Louie, J. et al University of Nevada Reno

Sep 01, 2010
5 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
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INTEGRATE Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design techno...

Vijayakumar, G. et al National Renewable Energy Laboratory (NREL)

May 04, 2021
8 Resources
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Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

This dataset contains turbine and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in ...

Ramos, D. et al National Renewable Energy Laboratory

Feb 12, 2021
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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
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