THE EFFECT OF CORROSION GROWTH MODEL ASSUMPTIONS ON THE RELIABILITY ESTIMATES OF CORRODED PIPELINES

Corrosion growth models are used to estimate future metal loss and the safe remaining lifetime of corrosion features in pipelines. Probabilistic models have become increasingly important in practice for reliability and risk-based pipeline assessments. The unknown model variables are usually determined from in-line inspection (ILI) results. Corrosion growth models exhibit various levels of complexity to account for temporal and spatial uncertainties of the actual corrosion growth process, and measurement uncertainties associated with ILIs. Model diversity leads to significant differences in how the models approach the uncertainty of future corrosion growth. This paper builds upon previous work and provides some theoretical background to an application described in [1]. It compares four common probabilistic corrosion growth models with respect to reliability estimates of leak failure. The four models are two uncertain corrosion rate models and two stochastic process models where the features are considered to be either independent or exchangeable. The unknown random variables of each model are updated in a Bayesian manner using the same ILI results. The key findings of this paper are: Proper truncation at zero of the probability distributions for the unknown random variables is necessary if the measured corrosion growth is near zero or negative. A stochastic process leads to lower uncertainties when determining future metal loss and, consequently, an increased reliability against leak failure than corrosion rate models. The assumption of exchangeable features causes a reduction in the probability of leak failure due to the effect of borrowing information compared to independent features. The four corrosion growth models provide similar results with respect to the probability of failure if the measured corrosion growth is large. As the measured corrosion growth decreases in size, the differences between the reliability estimates increase.