Reliability analysis with corrosion defects in submarine pipeline case study: Oil pipeline in Ab-khark island

Abstract

The offshore industry widely uses offshore pipelines to transport petroleum products due to their high capacity and reasonable installation costs. Corrosion is the leading failure cause of offshore pipelines, and their reliability analysis is necessary for reducing maintenance costs. This study presents a corrosion analysis in the bursting mode of the pipeline using a codified method based on probabilistic space theory, considering the monitored data of the oil pipeline and the pigging data of the AB-KHARK pipeline. The relationship between pigging data, parameters affecting corrosion, and the reduction of pipe wall thickness are determined based on the indicators of Regression and Neural Network machine learning methods. A new method is presented for modeling the failure modes of offshore pipelines based on their historical data and statistical computation results. Finally, the system is investigated by the first-order reliability method considering the limit state function provided by evaluating the offshore pipeline reliability. The proposed method is evaluated using a dataset from AB-KHARK island in IRAN indicate zero percentage error in failure prediction versus related works with 1% error for PRCI and MB methods, 6, 8, and 11 percentages error against RITCHE, DNV, and AMSE methods, respectively.