Predicting Thermal Performance of Aquifer Thermal Energy Storage Systems in Depleted Clastic Hydrocarbon Reservoirs via Machine Learning: Case Study from Hungary

Hawkar Ali Abdulhaq (), János Geiger, István Vass, Tivadar M. Tóth, Tamás Medgyes, Gábor Bozsó, Balázs Kóbor, Éva Kun and János Szanyi
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Hawkar Ali Abdulhaq: Department of Atmospheric and Geospatial Data Sciences, University of Szeged, Egyetem Utca, 2, 6722 Szeged, Hungary
János Geiger: Department of Geology, University of Szeged, Egyetem Utca, 2, 6722 Szeged, Hungary
István Vass: MOL Hungary, MOL Plc, H-6701 Algyő, SZEAK épület 2.em 207.sz., 6701 Algyő, Hungary
Tivadar M. Tóth: Department of Geology, University of Szeged, Egyetem Utca, 2, 6722 Szeged, Hungary
Tamás Medgyes: SZETAV District Heating Company of Szeged, Vág u. 4, 6724 Szeged, Hungary
Gábor Bozsó: Department of Geology, University of Szeged, Egyetem Utca, 2, 6722 Szeged, Hungary
Balázs Kóbor: SZETAV District Heating Company of Szeged, Vág u. 4, 6724 Szeged, Hungary
Éva Kun: Szabályozott Tevékenységek Felügyeleti Hatósága, Alkotás Utca 50, 1123 Budapest, Hungary
János Szanyi: Department of Geology, University of Szeged, Egyetem Utca, 2, 6722 Szeged, Hungary

Energies, 2025, vol. 18, issue 10, 1-22

Abstract: 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, achieving peak injection temperatures around 94.9 °C and thermal recovery efficiencies ranging from 81.05% initially to 88.82% after multiple operational cycles, reflecting an efficiency improvement of approximately 8.5%. A Random Forest model trained on simulation outputs predicted thermal recovery performance with high accuracy (R 2 ≈ 0.87) for candidate wells beyond the original modeling domain, demonstrating computational efficiency gains exceeding 90% compared to conventional simulations. The proposed data-driven methodology significantly accelerates optimal site selection and operational planning, offering substantial economic and environmental benefits and providing a scalable template for similar geothermal energy storage initiatives in other clastic sedimentary basins.

Keywords: machine learning; aquifer thermal energy storage; heat transport modeling; geothermal energy; depleted hydrocarbon reservoirs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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