Chenglong Zhang, Guobing Wang, Peng Dai, Qian Wu, Jinglong Fu, Chao Zhang, Lei Fu
Fiber optic sensing technologies, owing to their intrinsic safety, immunity to electromagnetic interference, and distributed sensing capabilities, demonstrate unique applications in monitoring CO2 geological storage. However, existing review studies often focus on a single sensor type or specific monitoring scenarios, lacking a systematic classification and scenario adaptation analysis of full-spectrum technologies from point and quasi-distributed to fully distributed sensing. To address this research gap, this paper systematically reviews the principles and performance characteristics of full-spectrum fiber optic sensing technologies, covering core mechanisms of distributed temperature, acoustic, and strain sensing, and establishes a technology-adaptation framework for typical monitoring scenarios including wellbore integrity, reservoir dynamics, caprock faults, and near-surface facilities while integrating the latest advances in machine learning and deep learning for fiber optic data processing. Based on systematic analysis, prioritized short-term and long-term recommendations are proposed, covering multitechnology integration, extreme-environment adaptability, intelligent data processing, and standardization. This work aims to provide theoretical support for the design of monitoring schemes in CCUS projects and to promote the large-scale application of fiber optic sensing technologies in carbon sequestration safety monitoring.