Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

Abstract Geothermal scientists have used bottom-hole temperature data from extensive oil and gas well datasets to generate heat flow and temperature-at-depth maps to locate potential geothermally active regions. Considering that there are some uncertainties and simplifying assumptions associated wit...

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Bibliographic Details
Main Authors: Arya Shahdi, Seho Lee, Anuj Karpatne, Bahareh Nojabaei
Format: Article
Language:English
Published: SpringerOpen 2021-07-01
Series:Geothermal Energy
Subjects:
Renewable energy
Geothermal energy
Machine learning
XGBoost
Subsurface temperature
Geothermal gradient
Online Access:https://doi.org/10.1186/s40517-021-00200-4

Internet

https://doi.org/10.1186/s40517-021-00200-4

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