Predicting Thermal Performance of Aquifer Thermal Energy Storage Systems in Depleted Clastic Hydrocarbon Reservoirs via Machine Learning Case Study from Hungary /
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...
Elmentve itt :
| Szerzők: | |
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2025
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| Sorozat: | ENERGIES
18 No. 10 |
| Tárgyszavak: | |
| doi: | 10.3390/en18102642 |
| mtmt: | 36159591 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/37502 |
| LEADER | 02387nab a2200313 i 4500 | ||
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| 008 | 250822s2025 hu o 000 eng d | ||
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| 024 | 7 | |a 10.3390/en18102642 |2 doi | |
| 024 | 7 | |a 36159591 |2 mtmt | |
| 040 | |a SZTE Publicatio Repozitórium |b hun | ||
| 041 | |a eng | ||
| 100 | 1 | |a Abdulhaq Hawkar | |
| 245 | 1 | 0 | |a Predicting Thermal Performance of Aquifer Thermal Energy Storage Systems in Depleted Clastic Hydrocarbon Reservoirs via Machine Learning |h [elektronikus dokumentum] : |b Case Study from Hungary / |c Abdulhaq Hawkar |
| 260 | |c 2025 | ||
| 300 | |a 22 | ||
| 490 | 0 | |a ENERGIES |v 18 No. 10 | |
| 520 | 3 | |a 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 (R2 ≈ 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. | |
| 650 | 4 | |a Föld- és kapcsolódó környezettudományok | |
| 700 | 0 | 1 | |a Geiger János |e aut |
| 700 | 0 | 1 | |a Vass István |e aut |
| 700 | 0 | 2 | |a M. Tóth Tivadar |e aut |
| 700 | 0 | 2 | |a Medgyes Tamás |e aut |
| 700 | 0 | 2 | |a Bozsó Gábor |e aut |
| 700 | 0 | 2 | |a Kóbor Balázs |e aut |
| 700 | 0 | 2 | |a Kun Éva |e aut |
| 700 | 0 | 2 | |a Szanyi János |e aut |
| 856 | 4 | 0 | |u http://publicatio.bibl.u-szeged.hu/37502/1/energies-18-02642.pdf |z Dokumentum-elérés |