Application of Extreme Value Statistics to the Prediction of Maximum Pit Depth in Non-Piggable, Buried Pipelines

Abstract

Currently, the reliability of non-piggable pipelines is mainly assessed either from historical failure data or from the results of direct assessment evaluations. When external, localized corrosion is the main threat to the pipeline integrity, the most important factor in assessing the reliability of a pipeline segment is the distribution of maximum pit depths. This distribution cannot be directly derived from historical failure data, nor from the information obtained from external corrosion direct assessment. In contrast, the statistical modeling of extreme values could be used to predict the distribution of pit depth maxima in a pipeline from a relatively small number of maximum pit depths measured at excavation sites along its length. Despite of the large number of works aimed at the application of the extreme value statistics, there is a lack of studies devoted to the applicability of the method for prediction of the maximum pit depth for the pit densities and pit spatial patterns typical of long buried pipelines. In this work, Monte Carlo simulations were conducted in order to assess the statistical errors associated with the prediction of the maximum pit depth for a wide range of the number and size of the inspection areas, pits per unit area and pit spatial patterns. As a result, the optimum area of inspection is proposed. The Monte Carlo numerical experiments were run by using synthetic and real corrosion data acquired by magnetic flux leakage and ultrasonic in-line inspection (ILI) tools, an approach that has not been reported in previous studies. The ILI data was sampled using standard methods of extreme value analysis, and the predicted maximum pit depth was compared with that reported by the in-line inspection. Monte Carlo simulations with synthetic and real corrosion data have allowed assessing the influence of the number and size of the inspected areas on the accuracy of predictions when pits distribute homogeneously and non-homogeneously in the pipeline. It is shown that, when the distribution of pits is homogeneous, the accuracy in the maximum pit depth prediction using the proposed method is similar to the measurement errors associated with magnetic flux leakage ILI tools.