Abstract
Maintenance and integrity management of hydrocarbons pipelines face the challenges from uncertainties in the data available. This paper demonstrates a way for pipeline remaining service life prediction that integrates structural reliability analysis, accumulated corrosion knowledge, and inspection data on a sound mathematical foundation. Pipeline defects depth grows with time according to an empirical corrosion power law, and this is checked for leakage and rupture probability. The pipeline operating pressure is checked with the degraded failure pressure given by ASME B31G code for rupture likelihood. As corrosion process evolves with time, Dynamic Bayesian Network (DBN) is employed to model the stochastic corrosion deterioration process. From the results obtained, the proposed DBN model for pipeline reliability is advanced compared with other traditional structural reliability method whereby the updating ability brings in more accurate prediction results of structural reliability. The comparisons show that the DBN model can achieve a realistic result similar to the conventional method, Monte Carlo Simulation with very minor discrepancy.
Highlights
Corrosion represents increasing challenges for the operation of subsea pipelines and become the major threats posed to offshore oil and gas pipelines in relation to operational integrity
The aim of this section was to compare the performance of Dynamic Bayesian Network (DBN) model with existing structural reliability methods such as Monte Carlo Simulation (MCS)
This paper presents the development of a Dynamic Bayesian Networks model to evaluate the time-dependent structural reliability of hydrocarbon pipeline subject to corrosion
Summary
Corrosion represents increasing challenges for the operation of subsea pipelines and become the major threats posed to offshore oil and gas pipelines in relation to operational integrity. Bai & Bai [1] defined corrosion as a deterioration of metal owing to chemical or electrochemical reactions between the metals and its surrounding In this instance, the direct outcome of the corrosion activity would typically result in metal loss directly to the pipelines wall thickness, and as this progress with time, a corresponding reduction in the integrity, safety and the structural reliability of the pipeline to withstand the applied operational stresses. The analysis is performed to evaluate how the integrity of a pipeline is affected by a corrosion defect and predicting the probability to fail as a result of corrosion defect growth This probabilistic approach has been extensively used in the last decades within the pipeline industry as it is proven to provide a reasonable framework to account for various uncertainties that impact the development of suitable maintenance strategies [2]–[6]. A number of researchers have applied BN for deterioration modelling [9; 11,12,13] this paper focuses on the evaluation of a pipeline’s reliability subjected to stochastic corrosion deterioration using DBN approach
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
