A Dynamic Bayesian Pre-Warning Framework for Safety-Critical Barriers in Subsea Production Systems

Safety-critical valves in subsea production systems are essential barriers against catastrophic hydrocarbon releases. However, effective risk pre-warning remains challenging under conditions of sparse test data, infrequent failures, and discrete testing intervals, where conventional static thresholds often lead to delayed warnings or excessive false alarms. To address this gap, this study proposes a dynamic Bayesian pre-warning method for safety-critical barriers, in which valve-level test data are interpreted as observable manifestations of barrier health states. A Beta–Binomial conjugate model is employed to recursively update failure probabilities, while an adaptive upper credible bound derived from the posterior distribution is introduced as a self-adjusting warning threshold that explicitly accounts for epistemic uncertainty and data scarcity. This approach enables early detection of emerging degradation while maintaining statistical rigor. The method establishes a hierarchical barrier-oriented warning structure covering two levels: individual safety-critical valves and the overall subsea barrier system. This design enables continuous monitoring of probabilistic barrier state evolution and supports consistent escalation of early warning signals from the individual valve level to the system level. The method is validated using fifteen years of test records (2010–2024) from subsea production systems in a specific offshore oil field, involving over 1200 functional tests of safety-critical valves. The method consistently identifies incipient anomalies earlier than industrial benchmarks and has never triggered a system-level red warning, demonstrating its robustness and operational suitability. By bridging Bayesian inference with integrity management practices, this method offers a principled, data-efficient solution for risk pre-warning in subsea production systems.