Digital Twin-Enabled Environmentally Resilient Recovery of Geo-Energy Infrastructure after Disasters

Abstract

Geo-energy infrastructure—comprising oil and gas systems, CCUS, geothermal installations, and subsurface storage—is critical for energy security and low-carbon transitions. Yet disasters and engineering failures can trigger hazardous leaks and contaminant transport through complex geological media, posing cascading risks to subsurface and surface ecosystems. While urban resilience has been widely studied, environmentally resilient recovery strategies that explicitly integrate leakage risk, contaminant migration, and resource optimization remain underexplored. This review presents a digital twin framework for disaster-driven environmental recovery, unifying real-time monitoring, geological modeling, leakage-risk assessment, contaminant transport analysis, and intelligent resource optimization within a single decision-support architecture. The framework constructs an integrated system linking geo-energy infrastructure, subsurface environment, and surface receptors, enabling dynamic interaction modeling. Representative scenarios—including CH$_4$, CO$_2$, H$_2$S, hydrocarbons, and BTEX—illustrate diverse monitoring, simulation, and decision needs. Emphasis is placed on digital twins for coupling multi-source sensing, subsurface simulation, environmental risk assessment, and adaptive planning. The framework offers a conceptual foundation for embedding digital twins into resilient geo-energy management. It also outlines key challenges and future directions in data integration, geological uncertainty, transport modeling, intelligent decision support, and interdisciplinary collaboration, providing practical guidance for sustainable recovery and long-term resilience of next-generation geo-energy systems.