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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.

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References

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Figures

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Fig. 1

Geometric characterization of corrosion defects

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Fig. 2

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

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Fig. 3

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

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Fig. 4

Classification of ILI-reported corrosion defects

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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

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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

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Fig. 7

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

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Fig. 8

Relationship between defect classification and s for corrosion defect data

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Fig. 9

Framework for determining PID

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Fig. 10

Empirical values of PID versus sk/tn

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Fig. 11

A schematic illustration of k-fold cross validation

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Fig. 12

Log-logistic PID model

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Fig. 13

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

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