A dynamic condition assessment model of aging subsea pipelines subject to corrosion-fatigue degradation

Abstract

Extension of operating lifetime of aging subsea pipelines is of great interest to oil and gas sectors. Corrosion and fatigue are the main causations of the condition degradation of subsea pipelines. A dynamic probabilistic condition assessment model based on continuous dynamic Bayesian network (DBN) is developed to support the life extension decision-making of aging subsea pipeline subjected to the corrosion-fatigue degradation. The methodology is built based on equivalent initial flaw size (EIFS) concept and a time-dependent prediction model implemented using DBN. The complex corrosion-fatigue degradation process is simplified by EIFS concept, and the crack propagation due to corrosion-fatigue is modelled using fracture mechanics model and DBN. A limit state function (LSF) is used to express the failure condition of subsea pipeline due to crack propagation. The dynamic reliability of subsea pipeline is estimated using Monte Carlo (MC) method with the probability distributions of the predicted crack sizes at different time slices. The estimated reliability is compared with the acceptable threshold to decide whether any measures are required to extend the life of subsea pipeline. The methodology is tested by a case study, and it is observed that it can be a useful tool to support life extension decision-making of aging subsea pipelines.