An AI-Driven Smart Reservoir Gas Water Shut-Off System for Enhanced Production Efficiency for Optimizing Gas Recovery in Complex Reservoirs

Abstract

Abstract The efficient recovery of natural gas from complex reservoirs, particularly in heterogeneous geological environments, remains a persistent challenge in the energy sector. Suboptimal production, driven by water intrusion, coning, and premature breakthrough, significantly curtails recovery factors and escalates operational expenditures. This paper introduces a novel Artificial Intelligence (AI)-driven Smart Reservoir Gas Water Shut-Off System (SRGWSS) designed to autonomously optimize gas recovery. The SRGWSS integrates real-time sensor data analytics, predictive machine learning models, and automated control mechanisms for proactive water management. By continuously monitoring parameters such as reservoir pressure, bottomhole flowing pressure, gas and water rates, and coning risk indices, the system predicts water breakthrough events and dynamically executes optimized interventions, including chemical injection and mechanical isolation. A comprehensive simulation dataset, modeled after complex carbonate reservoirs in the UAE, demonstrates the system's efficacy. Results indicate a marked enhancement in incremental gas recovery, significant suppression of water cut, and improved stability in production rates. The SRGWSS's intelligent feedback loop ensures continuous model refinement, leading to adaptive reservoir management that extends field life, reduces water handling costs, and maximizes economic returns. This work presents a transformative, data-driven framework that advances the paradigm of reservoir management from reactive to prescriptive, offering a scalable solution for sustainable gas production in increasingly challenging subsurface environments.