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

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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

Stanford University. (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]. Retrieved from https://dx.doi.org/10.15121/1869828.
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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., 04 Apr, 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. https://dx.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. https://dx.doi.org/10.15121/1869828. https://gdr.openei.org/submissions/1377.
@div{oedi_1377, 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}, url = {https://gdr.openei.org/submissions/1377}, journal = {}, number = , volume = , place = {United States}, year = {2022}, month = {04}}
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

Bulbul Ahmmed

Los Alamos National Laboratory

DOE Project Details

Project Name Thermo-hydro-chemical data for machine learning model development

Project Lead Mike Weathers

Project Number 35514

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