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

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  • Hawkar Ali Abdulhaq

    (Department of Atmospheric and Geospatial Data Sciences, University of Szeged, Egyetem Utca, 2, 6722 Szeged, Hungary
    Department of Geology, University of Szeged, Egyetem Utca, 2, 6722 Szeged, Hungary)

  • János Geiger

    (Department of Geology, University of Szeged, Egyetem Utca, 2, 6722 Szeged, Hungary
    GEOCHEM Ltd., 7673 Kővágószőlős, 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
    Geothermal Energy Applied Research Department, 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
    Geothermal Energy Applied Research Department, University of Szeged, Egyetem utca 2, 6722 Szeged, Hungary)

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.

Suggested Citation

  • Hawkar Ali Abdulhaq & János Geiger & István Vass & Tivadar M. Tóth & Tamás Medgyes & Gábor Bozsó & Balázs Kóbor & Éva Kun & János Szanyi, 2025. "Predicting Thermal Performance of Aquifer Thermal Energy Storage Systems in Depleted Clastic Hydrocarbon Reservoirs via Machine Learning: Case Study from Hungary," Energies, MDPI, vol. 18(10), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2642-:d:1660105
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    References listed on IDEAS

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