Uncertainty management in oil depot risk assessment: Current approaches, challenges, and future directions

This paper presents a systematic review and forward-looking analysis of uncertainty management in oil depot risk assessments. Current research underscores that dynamic risk assessment has become the dominant method for evaluating oil depot risks, enabling effective identification of risk fluctuations and providing dynamic risk warnings and decision support. Given the critical role of oil depots in national energy infrastructure, ensuring their reliability and safety is essential for safeguarding energy security. However, significant challenges persist in addressing cognitive and aleatory uncertainties. Existing approaches often rely on fuzzy logic and expert elicitation to manage cognitive uncertainty, yet the subjectivity and variability among experts can introduce uncertainties in weight assignments, potentially undermining decision accuracy. Future studies should investigate fuzzy cognitive maps and data-driven methods to mitigate these effects. For aleatory uncertainty, while conventional probability statistics are widely used, further advancements are required to enhance computational efficiency and accuracy. Promising solutions include grey system theory, Bayesian networks, and multi-source information fusion techniques, which offer improved approaches for handling aleatory uncertainty. Developing robust risk assessment frameworks is crucial for protecting energy infrastructure, ensuring the continuity of energy supply, and preventing catastrophic failures. Future research should focus on comprehensive uncertainty management frameworks, improved data quality, and the integration of Internet of Things (IoT) and artificial intelligence technologies to enhance the scientific rigor and reliability of oil depot risk assessments. These advancements will not only bolster operational safety but also ensure the long-term stability of energy security.