AI-Enabled Risk Assessment and Environmental Monitoring of Natural Gas Storage

Abstract

Subsurface natural gas storage operations, spanning depleted hydrocarbon reservoirs, saline aquifers, and salt caverns, carry inherent risks of methane leakage, ground surface deformation, and geomechanical instability, each with significant safety, environmental, and climate implications given methane's potency as a greenhouse gas. Conventional risk assessment and environmental monitoring approaches for natural gas storage rely on periodic surveys, sparse well-based sensor networks, and expert judgment, methods that are costly, spatially and temporally limited, and increasingly inadequate for the continuous, comprehensive oversight that growing natural gas storage capacity and tightening emissions regulations demand. Artificial intelligence, spanning machine learning-based sensor calibration, deep learning-based leak detection and classification, and AI-accelerated satellite-based ground deformation monitoring, has emerged as a transformative set of tools for closing this oversight gap. This article presents a research synthesis of AI-enabled risk assessment and environmental monitoring for natural gas storage, examining hybrid AI-physics frameworks for operational and environmental risk assessment, machine learning-calibrated methane sensor networks, deep learning-based infrared leak classification, and deep learning-accelerated satellite data assimilation for reservoir pressure and surface deformation monitoring. It draws on case studies from heterogeneous depleted gas reservoir management, geothermal-analogue reactive heat exchanger engineering, high-resolution petrophysical characterization, and geomechanically informed wellbore stability assessment, and further considers methodological parallels with large-scale AI-driven risk stratification and economic impact frameworks developed for national healthcare and public health systems. The article concludes by identifying persistent challenges in sensor network coverage, cross-site model generalizability, and regulatory standardization, and proposes a research agenda for advancing AI-enabled risk assessment and environmental monitoring toward routine, trustworthy use in subsurface natural gas storage operations as the field matures through 2026 and beyond.