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On the Applicability of Extreme Value Statistics in the Prediction of Maximum Pit Depth in Heavily Corroded Non-Piggable Buried Pipelines

[+] Author Affiliations
L. Alfonso

Universidad Autónoma de la Ciudad de México, México, DF, México

F. Caleyo, J. M. Hallen, J. Araujo

Instituto Politécnico Nacional, México, DF, México

Paper No. IPC2010-31321, pp. 527-535; 9 pages
doi:10.1115/IPC2010-31321
From:
  • 2010 8th International Pipeline Conference
  • 2010 8th International Pipeline Conference, Volume 4
  • Calgary, Alberta, Canada, September 27–October 1, 2010
  • Conference Sponsors: International Petroleum Technology Institute and the Pipeline Division
  • ISBN: 978-0-7918-4423-6 | eISBN: 978-0-7918-3885-3
  • Copyright © 2010 by ASME

abstract

There exists a large number of works aimed at the application of Extreme Value Statistics to corrosion. However, there is a lack of studies devoted to the applicability of the Gumbel method to the prediction of maximum pitting-corrosion depth. This is especially true for works considering the typical pit densities and spatial patterns in long, underground pipelines. In the presence of spatial pit clustering, estimations could deteriorate, raising the need to increase the total inspection area in order to obtain the desired accuracy for the estimated maximum pit depth. In most practical situations, pit-depth samples collected along a pipeline belong to distinguishable groups, due to differences in corrosion environments. For example, it is quite probable that samples collected from the pipeline’s upper and lower external surfaces will differ and represent different pit populations. In that case, maximum pit-depth estimations should be made separately for these two quite different populations. Therefore, a good strategy to improve maximum pit-depth estimations is critically dependent upon a careful selection of the inspection area used for the extreme value analysis. The goal should be to obtain sampling sections that contain a pit population as homogenous as possible with regard to corrosion conditions. In this study, the aforementioned strategy is carefully tested by comparing extreme-value-oriented Monte Carlo simulations of maximum pit depth with the results of inline inspections. It was found that the variance to mean ratio, a measure of randomness, and the mean squared error of the maximum pit-depth estimations were considerably reduced, compared with the errors obtained for the entire pipeline area, when the inspection areas were selected based on corrosion-condition homogeneity.

Copyright © 2010 by ASME
Topics: Pipelines

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