Transforming Abandoned Hydrocarbon Fields into Heat Storage Solutions: A Hungarian Case Study Using Enhanced Multi-Criteria Decision Analysis–Analytic Hierarchy Process and Geostatistical Methods

This study introduces a robust methodology utilizing Multi-Criteria Decision Analysis (MCDA) combined with an Analytic Hierarchy Process (AHP) to repurpose abandoned hydrocarbon fields for energy storage, supporting the transition to renewable energy sources. We use a geostatistical approach integrated with Python scripting to analyze reservoir parameters—including porosity, permeability, thickness, lithology, temperature, heat capacity, and thermal conductivity—from a decommissioned hydrocarbon field in Southeast Hungary. Our workflow leverages stochastic simulation data to identify potential zones for energy storage, categorizing them into high-, moderate-, and low-suitability scenarios. This innovative approach provides rapid and precise analysis, enabling effective decision-making for energy storage implementation in depleted fields. The key finding is the development of a methodology that can quickly and accurately assess the feasibility of repurposing abandoned hydrocarbon reservoirs for underground thermal energy storage, offering a practical solution for sustainable energy transition.