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Machine Learning for Aquifer Thermal Storage by Amal Stalin is a document available to read on EtoBox.

What is Machine Learning for Aquifer Thermal Storage about?

This study presents an innovative approach for repurposing depleted clastic hydrocarbon reservoirs in Hungary as High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems, integrating numerical heat transport modeling and machine learning optimization. A detailed hydrogeological model of the Békési Formation was built using historical well logs, core analyses, and production data. Heat transport simulations using MODFLOW/MT3DMS revealed optimal dual-well spacing and injection strategies,

Author

Amal Stalin

Language

EN