A Review of smart AI systems for real-time monitoring and optimization of ocean-based carbon capture, utilization, and storage networks

Importance – Ocean-based Carbon Capture, Utilization, and Storage (CCUS) systems are increasingly recognized as a vital solution for mitigating climate change due to their vast storage potential. Yet, their deployment faces significant challenges including harsh marine conditions, biofouling, corrosion, and limited real-time monitoring capabilities, which reduce safety and efficiency. Research Gap – Although land-based CCUS has been extensively studied, research on AI-enabled frameworks for offshore CCUS remains limited. Existing work is often confined to simulations or small-scale pilots, with inadequate attention to adaptive fault-tolerant control, multi-metric performance evaluation, and long-term field validation. Objective – This study aims to develop and validate a smart AI-enabled framework for real-time monitoring, predictive control, and optimization of offshore CCUS networks, with a focus on enhancing safety, efficiency, and environmental sustainability. Methodology – The proposed framework integrates IoT-enabled underwater sensors, autonomous vehicles, satellite imaging, and edge computing with advanced AI models including CNNs, LSTMs, GANs, and reinforcement learning. Validation was performed through a simulation-based case study on an offshore saline aquifer using a digital twin and multi-objective genetic algorithm optimization. Key Findings – The system achieved a 28% reduction in leak detection time, a 31% improvement in injection efficiency, and an 18% reduction in ecological risk compared with conventional monitoring approaches. The digital twin predicted plume migration with 95% accuracy, and robustness tests showed less than 5% performance degradation under sensor faults. Implications – These outcomes demonstrate that AI integration can significantly enhance monitoring, predictive decision-making, and compliance in offshore CCUS systems. The findings provide practical guidance for advancing autonomous and sustainable marine carbon storage, though large-scale deployment will require solutions to data scarcity, energy constraints, and regulatory integration.