A Data-Driven ML Model for Sand Channel Prediction from Well Logs for UTES Site Optimization and Thermal Breakthrough Prevention: Hungary Case Study

Hawkar Ali Abdulhaq (), János Geiger, István Vass, Tivadar M. Tóth (), Gábor Bozsó and János Szanyi
Additional contact information
Hawkar Ali Abdulhaq: Department of Atmospheric and Geospatial Data Sciences, University of Szeged, 6722 Szeged, Hungary
János Geiger: 2 GEOCHEM Ltd., Kővágószőlős, Szeged University, 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, 6722 Szeged, Hungary
Gábor Bozsó: Department of Geology, University of Szeged, 6722 Szeged, Hungary
János Szanyi: Department of Geology, University of Szeged, 6722 Szeged, Hungary

Energies, 2025, vol. 18, issue 16, 1-26

Abstract: This study presents a data-driven approach to predict the three-dimensional distribution of sand-rich channels in hydrocarbon reservoirs using well log data, aiming to optimize site selection for Underground Thermal Energy Storage (UTES) and manage hot and cold well pairs effectively. Leveraging detailed petrophysical datasets from 128 hydrocarbon exploration wells within the Szolnok Formation in southern Hungary, the developed machine-learning workflow—combining XGBoost regression and spatial residual correction—accurately delineated permeable channel systems suitable for thermal energy injection and extraction. The model achieved robust predictive performance (R 2 = 0.92; RMSE = 0.24), and correlation analyses confirmed significant relationships between predicted channels and sand content and shale content. Clearly identified high-permeability channel zones facilitated strategic well placement, significantly reducing the risk of premature thermal breakthrough and enhancing the reliability and efficiency of UTES operations.

Keywords: sand-rich channels; UTES; thermal breakthrough; szolnok formation; machine learning (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
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/16/4230/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/16/4230/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:16:p:4230-:d:1720704

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().