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GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Di...

Ahmmed, B. Stanford University

Apr 04, 2022
3 Resources
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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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GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework ...

Buster, G. et al Upflow

Jun 30, 2021
11 Resources
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USDA Census of Irrigation

The 2018 Irrigation and Water Management Survey (formerly called the Farm and Ranch Irrigation Survey) is a follow-on to the 2017 Census of Agriculture by the U.S. Department of Agriculture (USDA). This survey provides the only comprehensive information on irrigation activities an...

Census of Irrigation, U. U.S. Department of Agriculture

Oct 19, 2020
5 Resources
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Utah FORGE LBNL 3-2535 Preliminary Report on Development of a Reservoir Seismic Velocity Model

This report describes the development of a preliminary 3D seismic velocity model at the Utah FORGE site and first results from estimating seismic resolution in the generated fracture volume during Stage 3 of the April 2022 stimulation. A preliminary 3D velocity model for the larg...

Gritto, R. Array Information Technology

Jan 30, 2023
1 Resources
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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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2014 Wind Turbine Gearbox Damage Distribution based on the NREL Gearbox Reliability Database

Despite the improvements in wind turbine gearbox design and manufacturing practices, the wind industry is still challenged by premature wind turbine gearbox failures. To help address this industry-wide challenge, a consortium called the Gearbox Reliability Collaborative (GRC) was ...

Sheng, S. National Renewable Energy Laboratory

Feb 09, 2015
1 Resources
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PNNL Distribution System State Estimator Docker Image

This is the docker image for Pacific Northwest National Laboratory's (PNNL) distribution system state estimator (DSSE) used for the demo of OEDI-SI platform. To support the operation of modern distribution systems, operators require real-time visibility into system states. Due to ...

Bhatti, B. et al Pacific Northwest National Laboratory

Jul 10, 2023
6 Resources
2 Stars
Curated

Paisley Oregon Geothermal Plant Operated by Surprise Valley Electrification 2016 Operational Information

This submission includes an Electricity Generation Summary, Maintenance Logs, Detailed Operations Data, Operating Cost Summary, and an Operations overview at the Paisley Oregon Geothermal Plant. Data uploaded for SVEC by Tom Williams, NREL

Culp, E. Surprise Valley Electrification Corp. (SVEC)

Jan 01, 2017
5 Resources
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Utah FORGE Development of a Reservoir Seismic Velocity Model and Seismic Resolution Study

This is data from and a final report on the development of a 3D velocity model for the larger FORGE area and on the seismic resolution in the stimulated fracture volume at the bottom of well 16A-32. The velocity model was developed using RMS velocities of the seismic reflection su...

Vasco, D. and Chan, C. Array Information Technology

Apr 30, 2022
2 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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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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