Review Article
Open Access
Digital Twin-Enabled Environmentally Resilient Recovery of Geo-Energy Infrastructure after Disasters
1
Lincoln University College, Petaling Jaya 47301, Malaysia
* Corresponding Author:
Jinghan Li,
[email protected]
Volume 2, Issue 4
2026
Received: 24 June 2026
Revised: 16 August 2026
Accepted: 18 August 2026
Published: 01 September 2026
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CC BY 4.0 License
Article Information
Published in
Journal of Geo-Energy and Environment
Volume/Issue
Volume 2, Issue 4, 2026
Pages
300-318
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.
Graphical Abstract
Keywords
digital twin
Geo-Energy infrastructure
environmentally resilient recovery
leakage risk
contaminant transport
carbon capture
utilization and storage (CCUS)
geological environment
resource optimization
decision support
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declares no conflicts of interest.
AI Use Statement
The author declares that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
References
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APA Style
Li, J. (2026). Digital Twin-Enabled Environmentally Resilient Recovery of Geo-Energy Infrastructure after Disasters. Journal of Geo-Energy and Environment, 2(4), 300-318. https://doi.org/10.62762/JGEE.2026.334943
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TY - JOUR AU - Li, Jinghan PY - 2026 DA - 2026/09/01 TI - Digital Twin-Enabled Environmentally Resilient Recovery of Geo-Energy Infrastructure after Disasters JO - Journal of Geo-Energy and Environment T2 - Journal of Geo-Energy and Environment JF - Journal of Geo-Energy and Environment VL - 2 IS - 4 SP - 300 EP - 318 DO - 10.62762/JGEE.2026.334943 UR - https://www.icck.org/article/abs/JGEE.2026.334943 KW - digital twin KW - Geo-Energy infrastructure KW - environmentally resilient recovery KW - leakage risk KW - contaminant transport KW - carbon capture KW - utilization and storage (CCUS) KW - geological environment KW - resource optimization KW - decision support AB - 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. SN - 3069-3268 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Li2026Digital,
author = {Jinghan Li},
title = {Digital Twin-Enabled Environmentally Resilient Recovery of Geo-Energy Infrastructure after Disasters},
journal = {Journal of Geo-Energy and Environment},
year = {2026},
volume = {2},
number = {4},
pages = {300-318},
doi = {10.62762/JGEE.2026.334943},
url = {https://www.icck.org/article/abs/JGEE.2026.334943},
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.},
keywords = {digital twin, Geo-Energy infrastructure, environmentally resilient recovery, leakage risk, contaminant transport, carbon capture, utilization and storage (CCUS), geological environment, resource optimization, decision support},
issn = {3069-3268},
publisher = {Institute of Central Computation and Knowledge}
}
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Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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