Research Papers: Materials and Fabrication

Quantification of Measurement Errors in the Lengths of Metal-Loss Corrosion Defects Reported by Inline Inspection Tools

[+] Author and Article Information
T. Siraj

Department of Civil and
Environmental Engineering,
The University of Western Ontario,
1151 Richmond Street,
London, ON N6A 5B9, Canada
e-mail: tammeen.siraj@yahoo.com

W. Zhou

Department of Civil and
Environmental Engineering,
The University of Western Ontario,
1151 Richmond Street,
London, ON N6A 5B9, Canada
e-mail: wzhou@eng.uwo.ca

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received September 19, 2018; final manuscript received June 23, 2019; published online August 2, 2019. Assoc. Editor: Bostjan Bezensek.

J. Pressure Vessel Technol 141(6), 061402 (Aug 02, 2019) (11 pages) Paper No: PVT-18-1199; doi: 10.1115/1.4044211 History: Received September 19, 2018; Revised June 23, 2019

This paper proposes a framework to quantify the measurement error associated with lengths of corrosion defects on oil and gas pipelines reported by inline inspection (ILI) tools based on a relatively large set of ILI-reported and field-measured defect data collected from different in-service pipelines in Canada. A log-logistic model is proposed to quantify the likelihood of a given ILI-reported defect being a type I defect (without clustering error) or a type II defect (with clustering error). The measurement error associated with the ILI-reported length of the defect is quantified as the average of those associated with the types I and II defects, weighted by the corresponding probabilities obtained from the log-logistic model. The implications of the proposed framework for the reliability analysis of corroded pipelines given the ILI information are investigated using a realistic pipeline example.

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.


CSA, 2015, “ Oil and Gas Pipeline Systems,” Canadian Standards Association, Mississauga, ON, Canada, Standard No. CSA Z662.
Lam, C. , and Zhou, W. , 2016, “ Statistical Analyses of Incidents on Onshore Gas Transmission Pipelines Based on PHMSA Database,” Int. J. Pressure Vessels Piping, 145, pp. 29–40. [CrossRef]
Kiefner, J. F. , and Vieth, P. H. , 1989, A Modified Criterion for Evaluating the Remaining Strength of Corroded Pipe, American Gas Association, Washington, DC.
Benjamin, A. C. , Freire, J. L. F. , Vieira, R. D. , and Cunha, D. J. S. , 2016, “ Interaction of Corrosion Defects in Pipelines—Part 1: Fundamentals,” Int. J. Pressure Vessels Piping, 144, pp. 56–62. [CrossRef]
Fenyvesi, L. , and Dumalski, S. , 2005, “ Determining Corrosion Growth Accurately and Reliably,” NACE International Corrosion2005 (NACE), Houston, TX, Apr. 3–7, Paper No. NACE-05154. https://www.onepetro.org/conference-paper/NACE-05154
Nessim, M. , Dawson, J. , Mora, R. , and Hassanein, S. , 2008, “ Obtaining Corrosion Growth Rates From Repeat in-Line Inspection Runs and Dealing With the Measurement Uncertainties,” ASME Paper No. IPC2008-64378.
NACE, 2010, “ Standard Practice: In-Line Inspection of Pipelines,” NACE, Houston, TX, Standard No. SP0102.
Stephens, M. , and Nessim, M. A. , 2006, “ Comprehensive Approach to Corrosion Management Based on Structural Reliability Methods,” ASME Paper No. IPC2006-10458.
Zhou, W. , Siraj, T. , and Gong, C. , 2015, “ Reliability Consistent Mitigation Criteria for Corrosion Defects on Natural Gas Transmission Pipelines,” Can. J. Civ. Eng., 42(12), p. 1032. [CrossRef]
Cosham, A. , and Hopkins, P. , 2002, “ The Pipeline Defect Assessment Manual,” ASME Paper No. IPC2002-27067.
Al-Amin, M. , Zhou, W. , Zhang, S. , Kariyawasam, S. , and Wang, H. , 2012, “ Bayesian Model for Calibration of ILI Tools,” ASME Paper No. IPC2012-90491.
Caleyo, F. , Alfonso, L. , Espina-Hernández, J. H. , and Hallen, J. M. , 2007, “ Criteria for Performance Assessment and Calibration of In-Line Inspections of Oil and Gas Pipelines,” Meas. Sci. Technol., 18(7), pp. 1787–1799. [CrossRef]
Ellinger, M. A. , Bubenik, T. A., and Moreno, P. J. , 2016, “ ILI-to-Field Data Comparisons—What Accuracy Can You Expect?,” ASME Paper No. IPC 2016-64526.
Wright, C. , Dessein, T. , Li, Y. , and Ward, S. , 2018, “ Evaluation of Corrosion Growth Prediction Methodologies Using Burst Pressure Comparisons From Repeated In-Line Inspections,” ASME Paper No. IPC2018-78294.
ASME, 2016, “ Pipeline Transportation Systems for Liquids and Slurries, ASME Code for Pressure Piping,” American Society of Mechanical Engineers, New York, Standard No. B31.4.
Dawson, J. , Weller, R. , and Rao, G. , 2012, “ Identification of Coincident Features in Pipelines Using ILI Data,” ASME Paper No. IPC2012-90134.
Reas, C. , and Fry, B. , 2014, Processing: A Programming Handbook for Visual Designers and Artists, MIT Press, Cambridge, MA.
Seber, G. A. F. , and Lee, A. J. , 2003, Linear Regression Analysis, Wiley, Hoboken, NJ.
Hosmer, D. W. , and Lemeshow, S. , 2013, Applied Logistic Regression, Wiley & Son, Hoboken, NJ.
Kuhn, M. , and Johnson, K. , 2013, Applied Predictive Modeling, Springer, New York.
Berens, A. P. , 1983, “ NDE Reliability Data Analysis,” Nondestructive Evaluation and Quality Control (ASM Handbook, Vol. 17), 9th ed., ASM International, Novelty, OH.
Cook, D. , Dixon, P. , Duckworth, W. M. , Kaiser, M. S. , Koehler, K. , Meeker, W. Q. , and Stephenson, W. R. , 2000, “ Chapter 3: Binary Response and Logistic Regression Analysis,”University of Wisconsin, Madison, WI, accessed Aug. 5, 2014, http://www.stat.wisc.edu/~mchung/teaching/MIA/reading/GLM.logistic.Rpackage.pdf
Fushiki, T. , 2011, “ Estimation of Prediction Error by Using K-Fold Cross-Validation,” Stat. Comput., 21(2), pp. 137–146. [CrossRef]
Witten, I. H. , and Frank, E. , 2000, Data Mining, Academic Press, San Diego, CA.
Youden, W. J. , 1950, “ Index for Rating Diagnostic Tests,” Cancer, 3(1), pp. 32–35. [CrossRef] [PubMed]
Ang, A. H.-S. , and Tang, W. H. , 1975, Probability Concepts in Engineering Planning and Design, Basic Principles, Vol. 1, Wiley, New York.
Everitt, B. , and Hand, D. , 1981, Finite Mixture Distributions, Chapman and Hall, London.
Melchers, R. E. , 1999, Structural Reliability Analysis and Prediction, Wiley, New York.
Jiao, G. , Sotberg, T. , and Igland, R. , 1995, “ SUPERB 2M Statistical Data-Basic Uncertainty Measures for Reliability Analysis of Offshore Pipelines,” Norwegian Marine Technology Research Institute, Trondheim, Norway, Report No. STF70-F95212.
Zhou, W. , and Huang, G. X. , 2012, “ Model Error Assessments of Burst Capacity Models for Corroded Pipelines,” Int. J. Pressure Vessels Piping, 99–100, pp. 1–8. [CrossRef]
DNV-RP, 2010, “Recommended Practice: Corroded Pipelines,” Det Norske Veritas, Hovik, Norway, Standard No. DNV-RP-F101.
Zhou, W. , and Nessim, M. A. , 2011, “ Optimal Design of Onshore Natural Gas Pipelines,” ASME J. Pressure Vessel Technol., 133(3), p. 031702. [CrossRef]


Grahic Jump Location
Fig. 1

Geometric characterization of corrosion defects

Grahic Jump Location
Fig. 2

A schematic diagram for ILI-reported and laser-scanned corrosion defects (DMA and cluster)

Grahic Jump Location
Fig. 3

A comparison of ILI-reported and laser-scanned geometry for a corrosion anomaly

Grahic Jump Location
Fig. 4

Classification of ILI-reported corrosion defects

Grahic Jump Location
Fig. 5

Histograms of ILI-reported defect sizes: (a) depths of clusters, (b) lengths of clusters, (c) depths of DMA, and (d) lengths of DMA

Grahic Jump Location
Fig. 6

Field-measured defect length versus ILI-reported defect length for (a) all defects (types I and II), (b) type I defects, and (c) type II defects

Grahic Jump Location
Fig. 7

A schematic diagram for calculation of s for (a) DMA and (b) cluster

Grahic Jump Location
Fig. 8

Relationship between defect classification and s for corrosion defect data

Grahic Jump Location
Fig. 9

Framework for determining PID

Grahic Jump Location
Fig. 10

Empirical values of PID versus sk/tn

Grahic Jump Location
Fig. 11

A schematic illustration of k-fold cross validation

Grahic Jump Location
Fig. 12

Log-logistic PID model

Grahic Jump Location
Fig. 13

Empirical cumulative distribution function (CDF) versus the logarithmic value of (a) ε1, (b)  ε2, and (c) ε3



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In