A Probabilistic Framework for Predicting Offshore Pipeline Failure under Corrosion: Integrating Semi-Empirical Models and Material Uncertainties

This study presents a novel multi-scale probabilistic framework that couples microstructure-sensitive corrosion kinetics with Monte Carlo-based reliability analysis to predict failure behavior of offshore steel pipelines operating in synergistic multi-agent corrosive environments. Unlike conventional approaches that treat material properties deterministically, this framework explicitly incorporates microstructural heterogeneity (phase distribution, grain boundary characteristics, inclusion density) and compositional variability within nominally identical steel grades (API 5L X52, X65, X70) as stochastic variables influencing localized corrosion initiation and propagation. The methodology integrates four calibrated semi-empirical corrosion models with a dual-mode failure criterion (leak and burst) to evaluate degradation trajectories over 30-year operational lifespans in the South China Sea and Bohai Sea conditions characterized by synergistic Cl⁻-CO₂-H₂S-microbial interactions. Through 10⁶ Monte Carlo iterations, sensitivity decomposition reveals that ultimate tensile strength variability contributes 32.2% to long-term reliability variance, while operating pressure and wall thickness dominate early-stage failure probability (18-24 years). Critically, this study demonstrates that coefficient of variation (COV) in material properties exhibits non-monotonic effects on failure probability: increasing COV elevates lower-bound failure risk but paradoxically reduces upper-bound failure probability by 9.4-18.85% depending on steel grade and exposure duration. The framework enables microstructure-informed material qualification, providing differentiated inspection intervals (4-year for X65, 5-year for X52, 7-year for X70) based on grade-specific vulnerability to synergistic corrosion mechanisms. This approach addresses critical gaps in conventional pipeline integrity management by transitioning from empirical safety factors to physics-informed probabilistic design, particularly relevant for China's expanding offshore energy infrastructure in corrosive marine environments.