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October 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 31, 2013
1 Resources
0 Stars
Publicly accessible

November 2013 Green Machine Florida Canyon Hourly Data

Thibedeau, J. ElectraTherm, Inc.

Dec 02, 2013
1 Resources
0 Stars
Publicly accessible

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
Publicly accessible

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
Publicly accessible

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
Publicly accessible

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
0 Stars
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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
0 Stars
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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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2017 Western Passage Tidal Energy Resource Characterization Measurements

These data are from tidal resource characterization measurements collected between April and July 2017 in Western Passage near Eastport, Maine, USA. The dataset contains the following four sub-datasets, each of which is described in more detail in the README.pdf. 1. A bottom-mou...

Kilcher, L. et al National Renewable Energy Laboratory

Jul 31, 2017
33 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
Publicly accessible

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
Publicly accessible

Soda Lake Well Lithology Data and Geologic Cross-Sections

Comprehensive catalogue of drill-hole data in spreadsheet, shapefile, and Geosoft database formats. Includes XYZ locations of well heads, year drilled, type of well, operator, total depths, well path data (deviations), lithology logs, and temperature data. Plus, 13 cross-sections...

E., J. University of Nevada

Dec 31, 2013
1 Resources
0 Stars
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Admiralty Inlet Hub-Height Turbulence Measurements from June 2012

This data is from measurements at Admiralty Head, in Admiralty Inlet. The measurements were made using an IMU equipped ADV mounted on a mooring, the 'Tidal Turbulence Mooring' or 'TTM'. The inertial measurements from the IMU allows for removal of mooring motion in post processing....

Kilcher, L. National Renewable Energy Laboratory

Jun 18, 2012
7 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
Publicly accessible

2021 Cook Inlet Tidal Energy Resource Characterization Bottom Lander Measurements

These datasets are from tidal resource characterization measurements collected on the Terrasond High Energy Oceanographic Mooring (THEOM) from 1 July 2021 to 30 August 2021 (60 days) in Cook Inlet, Alaska. The lander was deployed at 60.7207031 N, 151.4294998 W in ~50 m of water. ...

Kilcher, L. and McVey, J. National Renewable Energy Laboratory

Jul 01, 2021
22 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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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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Altona Field Lab Inverse Model WRR 2020

Includes data for measured inert tracer breakthrough curves first reported in Hawkins (2020) (Water Resources Research). In addition, this submission includes the production well temperature measurements first reported in Hawkins et al. (2017a) (Water Resources Research, volume 53...

Tester, J. Cornell University

Jan 01, 2015
3 Resources
0 Stars
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EGS Collab Experiment 1: In-situ observation of pre-, co and post-seismic shear slip preceding hydraulic fracturing

Understanding the initiation and arrest of earthquakes is one of the long-standing challenges of seismology. Here we report on direct observations of borehole displacement by a meter-sized shear rupture induced by pressurization of metamorphic rock at 1.5 km depth. We observed the...

Guglielmi, Y. et al Lawrence Berkeley National Laboratory

May 22, 2018
2 Resources
0 Stars
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Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a n...

Marone, C. et al Pennsylvania State University

May 14, 2021
3 Resources
0 Stars
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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

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

2017 Western Passage Tidal Energy Resource Characterization Measurements

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

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

Soda Lake Well Lithology Data and Geologic Cross-Sections

Admiralty Inlet Hub-Height Turbulence Measurements from June 2012

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

2021 Cook Inlet Tidal Energy Resource Characterization Bottom Lander Measurements

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

Subsurface Characterization and Machine Learning Predictions at Brady Hot Springs Results

Altona Field Lab Inverse Model WRR 2020

EGS Collab Experiment 1: In-situ observation of pre-, co and post-seismic shear slip preceding hydraulic fracturing

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction