Application of Extreme Value Analysis to Corrosion Mapping Data

Extreme Value Analysis can be used to extrapolate sample inspection data into uninspected regions of a component. These methods can be used in conjunction with structural reliability software to predict the probability of future failure of the component. This information can help both operators and regulators make risk-based decisions on the future operation of the plant. This paper illustrates the approach with reference to a case study, based on data collected from a pipeline system in an oil platform using an ultrasonic corrosion mapping technique. Part of the pipeline system was inaccessible for inspection. In this region, therefore, it was necessary to estimate the condition of the pipe based on sample inspections in the accessible area. This was done by partitioning the pipe surface into rectangular blocks and fitting statistical distributions to the minimum wall thicknesses in these blocks. These ‘extreme value’ distributions were used to theoretically derive the corresponding distributions in the inaccessible part of the system. Structural reliability software was then used to predict the probability of future leakage. Analytic approximations were used both to verify the software and to estimate confidence limits for the predicted probabilities. The analysis showed that, for the majority of the line, the risk of leakage within its planned lifetime was negligible. However, the work highlighted the need for a better understanding of the rate of corrosion in two particular regions. As a result, a small number of ultrasonic transducers were permanently attached to these two sections of pipe. These transducers have yielded further information on the subsequent rate of corrosion. The paper will examine the assumptions underlying the analysis. It will discuss how the effect of correlation between neighbouring data points, known as data ‘clustering’, can be assessed and mitigated against. It will also provide guidance on assessing the goodness-of-fit to the distributions used.