Recent progress of multi-physics coupling and artificial intelligence in carbon dioxide sequestration and enhanced oil recovery

Abstract

Recent advancements in multi-physics coupling simulations and artificial intelligence (AI) have fundamentally reshaped the integration of carbon dioxide sequestration with enhanced oil recovery (CO₂-EOR) systems. Innovations in computational modeling frameworks, combining advanced fluid dynamics solvers with machine learning-augmented reservoir characterization, have significantly improved the predictive accuracy of multiphase flow and geochemical interactions under complex subsurface conditions. Parallel developments in intelligent decision-support architectures, integrating evolutionary optimization algorithms with federated learning paradigms, now enable real-time operational adjustments that reconcile technical, economic, and environmental objectives. The emergence of self-optimizing monitoring networks and adaptive digital twin systems has further enhanced long-term storage security while driving down lifecycle costs through automated risk management protocols. Persistent challenges remain in extending model generalizability to extreme geological environments and overcoming data scarcity in frontier basins, though next-generation solutions show promising pathways. Quantum-enhanced computational methods and multimodal AI architectures are poised to overcome current limitations in simulating multiscale coupled processes, while hybrid digital-physical validation ecosystems bridge the gap between numerical models and experimental observations. The integration of blockchain-based verification frameworks with intelligent control systems establishes new standards for transparent carbon accounting and regulatory compliance. These interdisciplinary synergies not only advance the technical feasibility of large-scale CO₂ management but also redefine the operational paradigms for achieving climate-positive energy production. As the field evolves, the convergence of physics-informed AI with edge computing infrastructures is catalyzing a paradigm shift toward autonomous, self-learning subsurface management systems capable of balancing hydrocarbon recovery with gigaton-scale carbon drawdown objectives.

Document Type: Invited review

Cited as: Zhang, T., Bai, H., He, S., Zhu. C., Gong, L. Recent progress of multi-physics coupling and artificial intelligence in carbon dioxide sequestration and enhanced oil recovery. Computational Energy Science, 2024, 1(4): 188-197. https://doi.org/10.46690/compes.2024.04.04

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