Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines

Abstract

This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines. The model parameters are evaluated using the Bayesian methodology by combining the inspection data obtained from multiple inspections with the prior distributions. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to numerically evaluate the posterior marginal distribution of each individual parameter. The measurement errors associated with the ILI tools are considered in the Bayesian inference. The application of the growth model is illustrated using an example involving real inspection data collected from an in-service pipeline in Alberta, Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. Parametric analyses associated with the growth model as well as reliability assessment of the pipeline based on the growth model are also included in the example. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

Keywords

pipeline / metal-loss corrosion / inverse Gaussian process / measurement error / hierarchical Bayesian / Markov Chain Monte Carlo (MCMC)

References

[1] Hong H P. Application of stochastic process to pitting corrosion. Corrosion, 1999, 55(1): 10–16

[2] Maes M A, Faber M H, Dann M R. Hierarchical modeling of pipeline defect growth subject to ILI uncertainty. In: Proceedings of the ASME 28th International Conference on Ocean. Offshore and Arctic Engineering, Honolulu, Hawaii, USA, 2009, OMAE2009–79470

[3] Zhang S, Zhou W, Al-Amin M, Kariyawasam S, Wang H. Time-dependent corrosion growth modeling using multiple ILI data. In: Proceedings of IPC 2012, IPC2012-90502. ASME, Calgary, 2012

[4] Wang X, Xu D. An inverse Gaussian process model for degradation data. Technometrics, 2010, 52(2): 188–197

[5] Chikkara R S, Folks J L. The Inverse Gaussian Distribution, New York: Marcell Dekker, 1989

[6] Al-Amin M, Zhou W, Zhang S, Kariyawasam S, Wang H. Bayesian model for the calibration of ILI tools, In: Proceedings of IPC 2012, IPC2012-90491. ASME, Calgary, 2012

[7] Fuller W A. Measurement Error Models. New York: John Wiley & Sons, Inc, 1987

[8] Jaech J L. Statistical Analysis of Measurement Errors. New York: John Wiley & Sons, Inc, 1985

[9] Bernardo J, Smith A F M. Bayesian Theory. New York: John Wiley & Sons Inc, 2007

[10] Gelman A, Carlin J B, Stern H S, Rubin D B. Bayesian Data Analysis, 2nd edition, New York: Chapman & Hall/CRC, 2004

[11] Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: Evolution, critique and future directions. Statistics in Medicine, 2009, 28(25): 3049–3082

[12] CSA-Z662 Oil and Gas Pipeline Systems. Canadian Standards Association, Rexdale, Ontario, 2007

[13] Zhou W, Hong H P, Zhang S. Impact of dependent stochastic defect growth on system reliability of corroding pipelines. International Journal of Pressure Vessels and Piping, 2012, 96-97: 68–77

[14] Zhou W. Reliability evaluation of corroding pipelines considering multiple failure modes and time-dependent internal pressure. Journal of Infrastructure Systems, 2011, 17: 216–224

[15] Leis B N, Stephens D R. An alternative approach to assess the integrity of corroded line pipe- part II: alternative criterion. In: Proceedings of the 7th International Offshore and Polar Engineering Conference. Honolulu, 1997, 635–640

[16] Kiefner J F, Maxey W A, Eiber R J, Duffy A R. Failure stress levels of flaws in pressurized cylinders, Progress in flaw growth and fracture toughness testing, ASTM STP 536. American Society of Testing and Materials; 1973, 461–481

[17] Zhou W. System reliability of corroding pipelines. International Journal of Pressure Vessels and Piping, 2010, 87(10): 587–595

[18] Jiao G, Sotberg T, Igland. RSUPERB. 2M- statistical data: basic uncertainty measures for reliability analysis of offshore pipelines, SUPERB JIP report no. STF70 F952112, SUPERB project 700411, 1995

[19] Zhou W, Huang G. Model error assessments of burst capacity models for corroded pipelines. International Journal of Pressure Vessels and Piping, 2012, 99-100: 1–8

Acknowledgements

The authors gratefully acknowledge the financial support provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada and TransCanada Corporation through the Collaborative Research and Development (CRD) program. The helpful comments provided by the anonymous reviewer are gratefully appreciated.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg