Optimization Method for Navigation Risk Assessment of Subsea Wellheads Based on Fuzzy Bayesian Network Under Complex Operating Conditions

To address the challenges of risk assessment for underwater wellhead navigation in complex marine environments, this study proposes an optimized risk assessment method based on an improved fuzzy Bayesian network. Innovatively, a three-layer dynamic risk assessment framework integrating multi-source uncertain information was constructed, which effectively overcomes the limitations of traditional methods in handling complex working conditions through the introduction of an adaptive fuzzy inference mechanism. The study takes an oilfield in the southern Bohai Sea as a case study, establishing a probabilistic reasoning-based risk assessment model by comprehensively considering key risk factors such as traffic flow, route distance, water depth, vessel tonnage, and wellhead layout. Compared with traditional methods, the proposed model innovatively achieves dynamic optimization of the risk assessment process, significantly enhancing the accuracy and reliability of assessment results through the synergistic analysis of Bayesian networks and event trees. The results demonstrate that the proposed method can effectively identify high-risk working conditions, providing a scientific basis for formulating targeted regulatory measures, protective schemes, and emergency strategies. This method offers a new technical approach for shipping safety analysis in offshore oil and gas field development, holding significant practical importance for ensuring maritime operational safety.