Research Papers: Design and Analysis

Time-Dependent Corrosion Growth Modeling Using Multiple In-Line Inspection Data

[+] Author and Article Information
Shenwei Zhang

Department of Civil and
Environmental Engineering,
Western University,
London, ON N6A 5B9, Canada
e-mail: szhan85@ uwo.ca

Wenxing Zhou

Assistant Professor
Department of Civil and
Environmental Engineering,
Western University,
London, ON N6A 5B9, Canada
e-mail: wzhou@eng.uwo.ca

Mohammad Al-Amin

TransCanada Corporation,
450-1st Street SW,
Calgary, AB T2P 5H1, Canada
e-mail: mohammad_al-amin@transcanada.com

Shahani Kariyawasam

Principal Engineer
TransCanada Corporation,
450-1st Street SW,
Calgary, AB T2P 5H1, Canada
e-mail: shahani_kariyawasam@transcanada.com

Hong Wang

Risk Engineer
TransCanada Corporation,
450-1st Street SW,
Calgary, AB T2P 5H1, Canada
e-mail: hong_wang@transcanada.com

1Corresponding author.

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received November 26, 2012; final manuscript received February 5, 2014; published online April 16, 2014. Assoc. Editor: Roman Motriuk.

J. Pressure Vessel Technol 136(4), 041202 (Apr 16, 2014) (7 pages) Paper No: PVT-12-1177; doi: 10.1115/1.4026798 History: Received November 26, 2012; Revised February 05, 2014

This paper describes a nonhomogeneous gamma process-based model to characterize the growth of the depth of corrosion defect on oil and gas pipelines. All the parameters in the growth model are assumed to be uncertain; the probabilistic characteristics of these parameters are evaluated using the hierarchical Bayesian methodology by incorporating the defect information reported by the multiple in-line inspections (ILIs) as well as the prior knowledge about these parameters. The bias and random measurement error associated with the ILI tools as well as the correlation between the measurement errors associated with different ILI tools are taken into account in the analysis. The application of the model is illustrated using an example involving real ILI data on a pipeline that is currently in service. The results suggest that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

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Grahic Jump Location
Fig. 1

Structure of the hierarchical Bayesian corrosion growth model

Grahic Jump Location
Fig. 6

PDF curves for the depth of defects #1–#10 in 2009

Grahic Jump Location
Fig. 5

Time-dependent PDF for the depth of defect #2

Grahic Jump Location
Fig. 4

Predicted defect depth growth from the year of defect initiation up to 2010

Grahic Jump Location
Fig. 3

Comparison of the predicted depth with the field measure depth in 2010

Grahic Jump Location
Fig. 2

Apparent growth paths of individual defects characterized by the ILI-reported and field-measured depths



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