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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
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
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
0 Stars
Publicly accessible
3 Resources
0 Stars
Publicly accessible
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
0 Stars
Publicly accessible
8 Resources
0 Stars
Publicly accessible
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
Publicly accessible
11 Resources
0 Stars
Publicly accessible
Machine Learning Model Geotiffs Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Ma...
Faulds, J. et al Nevada Bureau of Mines and Geology
Jun 01, 2021
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
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
4 Resources
0 Stars
Publicly accessible
Tularosa Basin Play Fairway: Weights of Evidence Models
These models are related to weights of evidence play fairway anlaysis of the Tularosa Basin, New Mexico and Texas. They were created through Spatial Data Modeler: ArcMAP 9.3 geoprocessing tools for spatial data modeling using weights of evidence, logistic regression, fuzzy logic a...
Brandt, A. University of Utah
Dec 01, 2015
2 Resources
0 Stars
Publicly accessible
2 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
Publicly accessible
4 Resources
0 Stars
Publicly accessible
BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset
The BUTTER-E Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,52...
Tripp, C. et al National Renewable Energy Laboratory
Dec 30, 2022
9 Resources
1 Stars
Curated
9 Resources
1 Stars
Curated
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
Publicly accessible
1 Resources
0 Stars
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BUTTER Empirical Deep Learning Dataset
The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four le...
Tripp, C. et al National Renewable Energy Laboratory
May 20, 2022
4 Resources
0 Stars
Publicly accessible
4 Resources
0 Stars
Publicly accessible
GIS Resource Compilation Map Package Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada
This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups incl...
Brown, S. et al Nevada Bureau of Mines and Geology
Jun 01, 2021
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
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Publications and Datasets from Play-Fairway Retrospective Analysis with Emphasis on Developing Improved Hydrothermal Energy Assessments
Previous moderate and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable mode...
Mordensky, S. et al United States Geological Survey
Feb 07, 2023
7 Resources
0 Stars
Publicly accessible
7 Resources
0 Stars
Publicly accessible
Sup3rWind Data (CONUS)
This data contains paired European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) and the Wind Integration National Dataset Toolkit (WTK) images for 2007 and 2010 over two regions in the US, with domain sizes ~800x800 (latitudes from 25.89 to 41.58, and long...
Sinha, S. et al National Renewable Energy Lab NREL
Jul 16, 2024
6 Resources
0 Stars
Curated
6 Resources
0 Stars
Curated
Microwave Radiometer CU Radiometrics MWR, Condon Raw Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Microwave Radiometer CU Radiometrics MWR, Condon Reviewed Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Microwave Radiometer ESRL Radiometrics MWR, Troutdale Raw Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Microwave Radiometer ESRL Radiometrics MWR, Troutdale Reviewed Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Microwave Radiometer ESRL Radiometrics MWR, Wasco Airport Raw Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Microwave Radiometer ESRL Radiometrics MWR, Wasco Airport Reviewed Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Microwave Radiometer UND Radiometrics MWR, Rufus Raw Data
**Overview**
These data monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.
**Data Details**
All output files are named automatically using the following format:
yyyy-mm-dd_hh-mm-ss_xxx.csv,
where yyyy...
Bianco, L. Wind Energy Technologies Office (WETO)
Nov 18, 2015
1 Resources
0 Stars
Publicly accessible
1 Resources
0 Stars
Publicly accessible
Training dataset and results for geothermal exploration artificial intelligence, applied to Brady Hot Springs and Desert Peak
The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model:
brady_som_output.gri, brady_som_output.grd, brady_som_output.*
desert_som_output.gri, desert_som_output.grd, desert_som_outpu...
Moraga, J. et al Colorado School of Mines
Sep 01, 2020
16 Resources
0 Stars
Publicly accessible
16 Resources
0 Stars
Publicly accessible
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
0 Stars
Publicly accessible
2 Resources
0 Stars
Publicly accessible
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
Publicly accessible
1 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
Publicly accessible
3 Resources
0 Stars
Publicly accessible