Digital Twin-Based Intelligent Management of Natural Gas Reservoirs

Abstract

Natural gas reservoir management increasingly relies on digital twin technology—dynamic, data-integrated virtual replicas of subsurface reservoirs and surface facilities—to bridge the gap between traditional, periodically updated reservoir simulation and the continuous, real-time decision-making that modern gas field operations demand. By fusing physics-based reservoir models with machine learning, real-time sensor data, and cloud computing infrastructure, digital twins enable operators to monitor reservoir behavior, optimize production and injection strategies, and proactively manage geomechanical and operational risk across the full lifecycle of a natural gas asset. This review examines the state of digital twin-based intelligent management for natural gas reservoirs, synthesizing recent advances in hybrid physics-AI reservoir modeling, machine learning-generated reservoir digital twins, digital twin frameworks for underground gas storage operations, and porous-media digital twin validation platforms. It draws on case studies from heterogeneous depleted gas reservoir optimization, geothermal-analogue petrophysical characterization, and geomechanically informed wellbore stability assessment, and further considers methodological parallels with large-scale AI-driven predictive analytics and economic impact frameworks developed for national healthcare systems. The review concludes by identifying persistent challenges in real-time data integration, cross-domain model coupling, and industry-wide standardization, and proposes a research agenda for advancing digital twin-based natural gas reservoir management toward routine, trustworthy use across commercial gas field and storage operations.