Prioritizing Retrofits of Non-Piggable Transmission Pipelines Using an Internal Corrosion Structural Reliability Model

Abstract

Abstract Internal Corrosion (IC) is a time dependent threat to pipelines that leads to wall loss due to a reaction between pipe material and the products or contaminants being transported. Certain vintage pipelines are not piggable, meaning that Inline Inspection measurement tools cannot be used to measure IC defects. Furthermore, being older, these pipelines are more susceptible to time dependent threats such as IC due to their longer exposure time. Operators often need to prioritize retrofits between different non-piggable pipelines, and the approach described in this study uses a structural reliability approach to estimate the expected frequency of failure for non-piggable pipeline segments and ranks them accordingly. This model captures the range of potential IC defect severity by using in-line inspection data from different pipeline asset types (e.g., Well Laterals, Transmission) to build empirical distributions of defect length and depth corrosion growth rate (CGR). These asset types serve as an indirect measure of gas quality as evidenced by differences in the defect distributions and anomaly densities. By characterizing CGR as a distribution for different asset types and using the industry standard Growth-by-Rule method to model defect depth, the model considers both asset type and pipe age when predicting the defect depth distribution in the current year. This projected depth distribution is used with physical and operating parameters (e.g., diameter, WT, pressure) in a structural reliability model to estimate the probability of failure for the segment under consideration. The structural reliability-based prioritization approach described in this paper provides a methodology to utilize physical and operating parameters of an un-piggable pipeline together with information from inspected pipelines to rank the expected severity of a given un-piggable line. These segment ranks were finally compared to ranks generated using other heuristic methods to quantify the benefits of this more detailed analysis methodology.