INNOVATIVE AND TECHNOLOGICAL DIRECTIONS FOR IMPROVING THE SECONDARY ENERGY USE OF DECOMMISSIONED OIL WELLS

Abstract

The article explores comprehensive innovative and technological solutions aimed at intensifying the geothermal reutilization of depleted oil and gas wells. The purpose of the study is to verify technological approaches to the repurposing of well infrastructure in order to mobilize its geothermal potential and integrate it into low-carbon energy systems. The methodological framework is based on a synthesis of structural-functional analysis of modern well-modernization technologies with numerical modelling of thermohydrodynamic processes that determine the mechanisms of energy extraction. Additionally, a comparative assessment of the efficiency of alternative heat-exchange configurations, Organic Rankine Cycle (ORC) units, and underground thermal storage systems is conducted. The results demonstrate that the implementation of closed-loop geothermal systems combined with optimized heat-exchanger architectures significantly increases the stability and intensity of heat flow without risking geological degradation. It is established that the integration of low-temperature ORC modules enables efficient conversion of thermal energy from bottom-hole zones into electrical power, expanding the operational range for wells with limited temperature conditions. Modelling of interseasonal thermal storage systems (BTES/ATES) confirms their ability to ensure long-term energy retention, forming the foundation for autonomous energy microsystems based on decommissioned wells. The practical significance of the study lies in the development of scientifically grounded technological solutions that optimize the costs of well decommissioning by transforming these structures into elements of renewable energy infrastructure, achieving both economic and environmental benefits. The proposed technological approaches are applicable in industrial-site redevelopment and in shaping regional energy strategies. Prospects for further research include improving predictive models of thermal performance based on machine-intelligence algorithms.