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September 2013 Green Machine Florida Canyon Hourly Data

Employing innovative product developments to demonstrate financial and technical viability of producing electricity from low temperature geothermal fluids, coproduced in a mining operation, by employing ElectraTherm's modular and mobile heat-to-power "micro geothermal" power plant...

Thibedeau, J. ElectraTherm, Inc.

Oct 25, 2013
1 Resources
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October 2013 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Oct 31, 2013
1 Resources
0 Stars
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November 2013 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Dec 02, 2013
1 Resources
0 Stars
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December 2013 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Jan 08, 2014
1 Resources
0 Stars
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February 2014 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Mar 03, 2014
1 Resources
0 Stars
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March 2014 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Apr 07, 2014
1 Resources
0 Stars
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April 2014 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

May 05, 2014
1 Resources
0 Stars
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May 2014 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Jun 02, 2014
1 Resources
0 Stars
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June 2014 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Jun 30, 2014
1 Resources
0 Stars
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July 2014 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Jul 31, 2014
1 Resources
0 Stars
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LCOE Baseline for OE Buoy WEC Device

Capex numbers are in $/kW, Opex numbers in $/kW-yr. Cost Estimates provided herein are based on concept design and basic engineering data and have high levels of uncertainties embedded. This reference economic scenario was done for a very large device version of the Ocean Energy ...

Previsic, M. et al Re Vision Consulting

Jul 26, 2017
2 Resources
0 Stars
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Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32

This report presents the far-field stress predictions at two locations along the vertical section of Utah FORGE Well 16A (78)-32 using a physics-based thermo-poro-mechanical model. Three principal stresses in far-field were obtained by solving an inverse problem based on the near-...

Lu, G. et al University of Pittsburgh

Aug 30, 2024
2 Resources
0 Stars
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Python Codebase and Jupyter Notebooks Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada" project. This software is licensed as free to use, modify, a...

Brown, S. and Smith, C. Nevada Bureau of Mines and Geology

Jun 30, 2022
4 Resources
0 Stars
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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
1 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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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
0 Stars
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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
0 Stars
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Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies

This is the companion dataset to the presentation NREL/PR-6A20-77485, which was presented at the 2020 Joint Statistical Meeting on August 3, 2020. Developed for the machine-learning predictive modeling of power-system responses to disruptions, it contains results of power-system c...

BushNREL

Aug 01, 2020
2 Resources
0 Stars
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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
6 Resources
0 Stars
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Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions September 2023 Report

This task completion report documents the development and implementation of machine learning (ML) models for the prediction of in-situ vertical (Sv), minimum horizontal (SHmin) and maximum horizontal (SHmax) stresses in well 16A(78)-32. The detailed description of the experimental...

Mustafa, A. et al Battelle Memorial Institute

Sep 28, 2023
3 Resources
0 Stars
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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
0 Stars
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Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to...

Zhu, T. Pennsylvania State University

May 05, 2021
4 Resources
0 Stars
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Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements 2024 Annual Workshop Presentation

This is a presentation on A Multi-Component Approach to Characterizing In-Situ Stress at the Utah FORGE Site: Laboratory Modelling and Field Measurements project by The University of Pittsburgh, presented by Andrew Bunger. The project characterizes the stress in the Utah FORGE EGS...

Bunger, A. Energy and Geoscience Institute at the University of Utah

Sep 04, 2024
1 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
0 Stars
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GeoVision: Harnessing the Heat Beneath Our Feet Analysis Inputs and Results

This submission includes input and results data from analysis done as part of the Geothermal Technology Office's Geothermal Vision Study (GeoVision). The submission includes data for both analysis of the electricity sector and the heating and cooling sector. For the electricity se...

Geothermal Technologies Office, U. National Renewable Energy Laboratory

Sep 30, 2019
16 Resources
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September 2013 Green Machine Florida Canyon Hourly Data

October 2013 Green Machine Florida Canyon Hourly Data

November 2013 Green Machine Florida Canyon Hourly Data

December 2013 Green Machine Florida Canyon Hourly Data

February 2014 Green Machine Florida Canyon Hourly Data

March 2014 Green Machine Florida Canyon Hourly Data

April 2014 Green Machine Florida Canyon Hourly Data

May 2014 Green Machine Florida Canyon Hourly Data

June 2014 Green Machine Florida Canyon Hourly Data

July 2014 Green Machine Florida Canyon Hourly Data

LCOE Baseline for OE Buoy WEC Device

Utah FORGE 2-2439v2: Report on Predicting Far-Field Stresses Using Finite Element Modeling and Near-Wellbore Machine Learning for Well 16A(78)-32

Python Codebase and Jupyter Notebooks Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs

Machine Learning-Assisted High-Temperature Reservoir Thermal Energy Storage Optimization: Numerical Modeling and Machine Learning Input and Output Files

Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies

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

Utah FORGE Project 2439: Machine Learning for Well 16A(78)-32 Stress Predictions September 2023 Report

Hybrid machine learning model to predict 3D in-situ permeability evolution

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

Utah FORGE 2-2439v2: Characterizing In-Situ Stress with Laboratory Modelling and Field Measurements 2024 Annual Workshop Presentation

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

GeoVision: Harnessing the Heat Beneath Our Feet Analysis Inputs and Results