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Improved Comparison of ILI Data and Field Excavations

[+] Author Affiliations
William V. Harper, David J. Stucki

Otterbein University, Westerville, OH

Thomas A. Bubenik, Clifford J. Maier

Det Norske Veritas (USA), Inc., Dublin, OH

David A. R. Shanks, Neil A. Bates

Det Norske Veritas (Canada) Ltd., Calgary, AB, Canada

Paper No. IPC2012-90440, pp. 175-178; 4 pages
doi:10.1115/IPC2012-90440
From:
  • 2012 9th International Pipeline Conference
  • Volume 2: Pipeline Integrity Management
  • Calgary, Alberta, Canada, September 24–28, 2012
  • Conference Sponsors: International Petroleum Technology Institute, Pipeline Division
  • ISBN: 978-0-7918-4513-4
  • Copyright © 2012 by ASME

abstract

The importance of comparing in-line inspection (ILI) calls to excavation data should not be underestimated. Neither should it be undertaken without a solid understanding of the methodologies being employed. Such a comparison is not only a key part of assessing how well the tool performed, but also for an API 1163 evaluation and any subsequent use of the ILI data. The development of unity (1-1) plots and the associated regression analysis are commonly used to provide the basis for predicting the likelihood of leaks or failures from unexcavated ILI calls. Combining such analysis with statistically active corrosion methods into perhaps a probability of exceedance (POE) study helps develop an integrity maintenance plan for the years ahead.

The theoretical underpinnings of standard regression analysis are based on the assumption that the independent variable (often thought of as x) is measured without error as a design variable. The dependent variable (often labeled y) is modeled as having uncertainty or error. Pipeline companies may run their regressions differently, but ILI to field excavation regressions often use the ILI depth as the x variable and field depth as the y variable. This is especially the case in which a probability of exceedance analysis is desired involving transforming ILI calls to predicted depths for a comparison to a threshold of interest such as 80% wall thickness. However, in ILI to field depth regressions, both the measured depths can have error. Thus, the underlying least squares regression assumptions are violated. Often one common result is a regression line that has a slope much less than the ideal 1-1 relationship.

Reduced Major Axis (RMA) Regression is specifically formulated to handle errors in both the x and y variables. It is not commonly found in the standard literature but has a long pedigree including the 1995 text book Biometry by Sokal and Rohlf in which it appears under the title of Model II regression. In this paper we demonstrate the potential improvements brought about by RMA regression.

Building on a solid comparison between ILI data and excavations provides the foundation for more accurate predictions and management plans that reliably provide longer range planning. This may also result in cost savings as the time between ILI runs might be lengthened due to a better analysis of such important data.

Copyright © 2012 by ASME

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