DOE Data Explorer title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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.

Authors:
Publication Date:
Other Number(s):
1377
DOE Contract Number:  
35514
Research Org.:
USDOE Geothermal Data Repository (United States); Stanford Univ., CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
Collaborations:
Stanford University
Subject:
15 Geothermal Energy
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 network
Geolocation:
32.677109586432, -106.32498890315
OSTI Identifier:
1869828
DOI:
https://doi.org/10.15121/1869828
Project Location:


Citation Formats

Ahmmed, Bulbul. GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources. United States: N. p., 2022. Web. doi: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. United States. doi:https://doi.org/10.15121/1869828
Ahmmed, Bulbul. 2022. "GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources". United States. doi:https://doi.org/10.15121/1869828. https://www.osti.gov/servlets/purl/1869828. Pub date:Mon Apr 04 00:00:00 EDT 2022
@article{osti_1869828,
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.},
doi = {10.15121/1869828},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {4}
}