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

Dataset · Mon Apr 04 00:00:00 EDT 2022

DOI:https://doi.org/10.15121/1869828· OSTI ID:1869828

Ahmmed, Bulbul ^[1]

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1. Stanford University

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.

• Research Organization: DOE Geothermal Data Repository; Stanford University
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Program (EE-4G)
• Contributing Organization: Stanford University
• OSTI ID: 1869828
• Report Number(s): 1377
• Availability: [e-mail]
• Country of Publication: United States
• Language: English

Related Subjects

15 GEOTHERMAL ENERGY
AI
artificial intelligence
development
discovery
energy
exploration
geothermal
hidden geothermal resources
machine learning
model
modeling
neural network
processed data
remote sensing
resource
resource detection
training data
training dataset