Cross-country pipeline inspection data analysis and testing of probabilistic degradation models

Abstract

Pipelines are the most efficient and safest means for the transportation of oil, gas, and refined petroleum products. Potentially severe consequences of pipeline failures make reliability and risk assessment an essential aspect of safe operation. However, due to limited access to industrial data, reliability and risk assessment studies often rely on experimental, synthetic, or unreliable data, which often raises questions on the proposed method’s credibility. The authors had the opportunity to access a comprehensive dataset from consecutive inline inspection (ILI) runs reporting more than seven years of degradation due to external corrosion of more than 200 km of a cross-country pipeline. This paper presents a step-by-step data processing approach and detailed statistical analysis of a cross-country pipeline’s ILI data. The paper presents stochastic models and defines the parameters required for modeling time-dependent structural integrity and risk assessment, i.e., corrosion-induced failure probability, burst pressure assessment, and containment loss. The accompanying dataset and proposed models for stochastic progress of external corrosion are hoped to serve as an essential source for pipeline risk and reliability studies.