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Research Papers: Materials and Fabrication

Hierarchical Bayesian Corrosion Growth Model Based on In-Line Inspection Data

[+] Author and Article Information
Mohammad Al-Amin

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

Wenxing Zhou

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

Shenwei Zhang

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

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 December 14, 2012; final manuscript received January 19, 2014; published online April 3, 2014. Assoc. Editor: Jianmin Qu.

J. Pressure Vessel Technol 136(4), 041401 (Apr 03, 2014) (8 pages) Paper No: PVT-12-1193; doi: 10.1115/1.4026579 History: Received December 14, 2012; Revised January 19, 2014

A hierarchical Bayesian growth model is presented in this paper to characterize and predict the growth of individual metal-loss corrosion defects on pipelines. The depth of the corrosion defects is assumed to be a power-law function of time characterized by two power-law coefficients and the corrosion initiation time, and the probabilistic characteristics of the these parameters are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on in-line inspection (ILI) data collected at different times for a given pipeline. The model accounts for the constant and non-constant biases and random scattering errors of the ILI data, as well as the potential correlation between the random scattering errors associated with different ILI tools. The model is validated by comparing the predicted depths with the field-measured depths of two sets of external corrosion defects identified on two real natural gas pipelines. The results suggest that the growth model is able to predict the growth of active corrosion defects with a reasonable degree of accuracy. The developed model can facilitate the pipeline corrosion management program.

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Figures

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Fig. 1

Graphical representation of a typical hierarchical model

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Fig. 2

Representation of measurement error associated with a typical ILI tool

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Fig. 3

Graphical representation of the full hierarchical Bayesian corrosion growth model

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Fig. 4

Comparison between the predicted and the measured depths in 2010

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Fig. 5

Predicted growth paths for defects #7 and #19 on pipeline of Case 1

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Fig. 6

Comparison between the predicted and field-measured depths in 2011

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Fig. 7

Predicted growth paths for defect #23 and #56 on pipeline of Case 2

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Fig. 8

Comparison between the predicted depths from the linear growth model and field-measured depths in 2010 for the corrosion defects in Case 1

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Fig. 9

Comparison between the predicted depths from the linear growth model and the field-measured depths in 2011 for the corrosion defects in Case 2

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