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 Discovery, Exploration, and Development of Hidden Geothermal Resources.
Citation Formats
TY - DATA
AB - 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 Discovery, Exploration, and Development of Hidden Geothermal Resources.
AU - Ahmmed, Bulbul
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO - 10.15121/1869828
KW - geothermal
KW - energy
KW - machine learning
KW - artificial intelligence
KW - AI
KW - exploration
KW - model
KW - modeling
KW - processed data
KW - training data
KW - training dataset
KW - remote sensing
KW - hidden geothermal resources
KW - resource detection
KW - discovery
KW - development
KW - resource
KW - neural network
LA - English
DA - 2022/04/04
PY - 2022
PB - Stanford University
T1 - GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources
UR - https://doi.org/10.15121/1869828
ER -
Ahmmed, Bulbul. GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources. Stanford University, 4 April, 2022, GDR. https://doi.org/10.15121/1869828.
Ahmmed, B. (2022). GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources. [Data set]. GDR. Stanford University. https://doi.org/10.15121/1869828
Ahmmed, Bulbul. GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources. Stanford University, April, 4, 2022. Distributed by GDR. https://doi.org/10.15121/1869828
@misc{OEDI_Dataset_7488,
title = {GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources},
author = {Ahmmed, Bulbul},
abstractNote = {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 Discovery, Exploration, and Development of Hidden Geothermal Resources.},
url = {https://gdr.openei.org/submissions/1377},
year = {2022},
howpublished = {GDR, Stanford University, https://doi.org/10.15121/1869828},
note = {Accessed: 2025-05-04},
doi = {10.15121/1869828}
}
https://dx.doi.org/10.15121/1869828
Details
Data from Apr 4, 2022
Last updated May 26, 2022
Submitted Apr 25, 2022
Organization
Stanford University
Contact
Dimitrios Ioannis Belivanis
302.635.4690
Authors
Original Source
https://gdr.openei.org/submissions/1377Research Areas
Keywords
geothermal, energy, machine learning, artificial intelligence, AI, exploration, model, modeling, processed data, training data, training dataset, remote sensing, hidden geothermal resources, resource detection, discovery, development, resource, neural networkDOE Project Details
Project Name Thermo-hydro-chemical data for machine learning model development
Project Lead Mike Weathers
Project Number 35514
