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Predicting favorable locations for geothermal development

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Development of renewable geothermal energy resources is hindered by high exploration risk due to limited economic profitability and high subsurface uncertainty. In order to reduce exploration risk, conventional methodologies have sought to predict geothermal resources using spatial aggregation of data thought to be indicators of geothermal potential. However, this approach is problematic because it requires a large dataset of catalogued geothermal resources which does not exist. In our approach, using Bayesian Evidential Learning, uncertainty is reduced by directly learning how data, such as shallow temperature wells or geophysical data, is related to temperatures deeper in the subsurface. Noah Athens 
Paper: Sensitivity of Temperature Predictions in Basin-Scale Hydrothermal Models