Extreme value modeling of localized internal corrosion in unpiggable pipelines

Abstract

Localized internal corrosion is one of the main failure mechanisms of energy pipelines. Microbiologically influenced corrosion (MIC) is a major contributor to internal corrosion in pipelines. Existing research uses extreme value analysis to fit extreme value distributions from data collected through either inspection or experiment to predict localized corrosion. The paper introduces an extreme value model to estimate the depth of localized internal corrosion considering MIC for unpiggable pipelines where inspection data is not available. The depth of material loss is estimated though a combination of corrosion analysis and extreme value modeling. A case study is provided to illustrate the proposed model. The application of a probabilistic approach supports risk-based inspection and maintenance planning for pipelines subject to internal corrosion.