The loess-paleosol profile near the settlement of Pécel has a notable size among the loess-paleosol sequences of the Northern Carpathian territories. Therefore, comprehensive sedimentological examinations were performed to understand the profile and the information preserved in it. The past periodicity and intensity of winds were showed by particle composition studies (GSI, U-ratio). At least two source areas can be presumed based on geochemical indices (CIA, CIW, Rb/Sr, Zr/Rb). Based on the characteristics of the chemical composition of sulphide minerals (P, S, Pb, Ni, As sulphides), the lower 10 m of the profile was supposed to be transported from the NW direction (Buda Thermal Karst, Börzsöny, Cserhát). Sufficient information is not yet available in order to determine the source area of the upper 10 m. By using the mentioned indexes, major developing and weathering horizons also could be identified.

This study explores a novel strategy to repurpose depleted clastic sediment hydrocarbon reservoirs in Hungary as High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems, incorporating machine learning to enhance system optimization. Hungary's extensive inventory of depleted fields, predominantly featuring clastic formations, presents significant potential for geothermal energy storage applications. Initially, detailed reservoir models were constructed by analyzing existing well logs and core data. Subsequent advanced numerical simulations of heat transport and groundwater flow were performed within the Bekesi Formation, concentrating on a dual-well configuration—one dedicated to hot fluid injection and extraction and the other to managing cold fluids. State-of-the-art simulation tools, including SGeMS, RockWorks, Python, MODFLOW, and GMS MT3DMS, were utilized to pinpoint optimal brine injection sites by evaluating critical parameters such as thermal conductivity, porosity, and permeability; additional core analyses filled essential gaps in thermal conductivity data. The study's central innovation lies in deploying a Random Forest algorithm to optimize thermal recovery efficiency. Data generated from comprehensive simulations across multiple wells were used to train the model, which then predicted and refined thermal performance for the remaining wells in the field. The outcomes are expected to yield precise identification of optimal injection locations, rigorous heat transport analyses, accurate estimates of storage capacities, and improved predictions of thermal recovery efficiency, thereby establishing a sustainable and data-driven methodology for converting depleted hydrocarbon reservoirs into effective thermal energy storage systems.

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.

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.

Scaling in geothermal production wells poses a critical challenge to sustainable energy production, particularly in vapor-dominated systems where scaling mechanisms are less understood. This study investigates scale treatment planning using the broaching method in Well X at Indonesia’s Kamojang geothermal field. Through well integrity testing, geochemical analysis, and XRD characterization, silica (quartz) scale formations were identified in the production casing. Performance monitoring revealed gradual decreases in steam production and wellhead pressure over a three-year period. The selection of the broaching method was validated through analysis of scale characteristics, well geometry, and economic feasibility, offering a significantly more cost-effective solution compared to conventional methods with a substantially shorter payback period. Broaching has effectively operated on multiple geothermal wells, restoring significant production capacity at approximately half the expense of conventional well workover methods. Our results challenge accepted assumptions on scaling in vapor-dominated systems and provide a methodical framework for scale treatment planning. This study demonstrates how strategic scale management can efficiently preserve well productivity while lowering operating costs, thus enabling sustainable geothermal resource development for operators worldwide.

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.