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

Ahmmed, Bulbul

doi:10.15121/1869828

Title: 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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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:: Ahmmed, Bulbul

Publication Date:: Mon Apr 04 00:00:00 EDT 2022

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.

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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.  doi:https://doi.org/10.15121/1869828

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                    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

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@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         = {Mon Apr 04 00:00:00 EDT 2022},

  month        = {Mon Apr 04 00:00:00 EDT 2022}

}

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DOI: https://doi.org/10.15121/1869828

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