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

Published by Stanford University | Department of Energy | Metadata Last Checked: June 28, 2025 | Last Modified: 2022-05-26T16:04:57Z

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.

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Complete Metadata

bureauCode	[ "019:20" ]
dataQuality	true
DOI	10.15121/1869828
identifier	https://data.openei.org/submissions/7488
issued	2022-04-04T06:00:00Z
landingPage	https://gdr.openei.org/submissions/1377
programCode	[ "019:006" ]
projectLead	Mike Weathers
projectNumber	35514
projectTitle	Thermo-hydro-chemical data for machine learning model development
spatial	{"type":"Polygon","coordinates":[[[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174],[-106.3249889031506,32.67710958643174]]]}

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