Objective Oil and gas port storage and transportation systems feature water–land coupling, intensive equipment distribution, and highly hazardous media. Accidents in these systems are characterized by multi-hazard interactions, cross-regional propagation, and high uncertainty. Existing accident simulation methods have difficulty accurately capturing accident evolution under complex port operating conditions. Therefore, the application characteristics and suitable scenarios of mainstream accident simulation methods need to be systematically reviewed.

Methods Centered on four procedures including scenario element extraction, element relationship analysis, accident scenario construction, and accident scenario deduction, literature review and cross-domain comparisons are employed. Research progress on dynamic Bayesian networks, Petri nets, Monte Carlo simulations, system safety analysis, and multi-model coupling is summarized. Comparisons are carried out across four dimensions: temporal evolution, spatial coupling, data dependency, and adaptability to port scenarios.

Results Based on the above analysis, key limitations in scenario deduction for complex oil-gas port systems are identified: full-chain modeling deficiencies, dynamic land-water interactions, multi-hazard accident chain synergy, multi-model fusion, and practical engineering support. To address these limitations, a unified deduction framework is constructed, encompassing ships, terminals, storage tanks, pipelines, and marine environments. By combining dynamic Bayesian networks, Petri nets, Monte Carlo simulations, and numerical modeling, the framework characterizes the full accident lifecycle and translates outputs into actionable tools, including critical risk node lists, early-warning thresholds, resource allocation plans, evacuation routes, and emergency response measures.

Conclusion Future oil-gas port scenario deduction should advance toward coordinated deduction of full-chain storage and transportation systems, dynamic environment-coupled deduction, collaborative deduction of multi-hazard accident chains, digital-twin-driven real-time deduction, and integration of deduction and decision-making. Developing a whole-process dynamic deduction and intelligent decision-support system for complex port accidents will provide essential theoretical grounding and practical engineering references for risk control, accident evolution prediction, and full-chain emergency decision-making of complex storage and transportation systems in oil-gas ports.