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 (R2 = 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.
