A Review of Data-Driven Oil and Gas Pipeline Pitting Corrosion Growth Models Applicable for Prognostic and Health Management
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Published
Nov 19, 2020
Roohollah Heidary
Steven A. Gabriel
Mohammad Modarres
Katrina M. Groth
Nader Vahdati
Abstract
Pitting corrosion is a primary and most severe failure mechanism of oil and gas pipelines. To implement a prognostic and health management (PHM) for oil and gas pipelines corroded by internal pitting, an appropriate degradation model is required. An appropriate and highly reliable pitting corrosion degradation assessment model should consider, in addition to epistemic uncertainty, the temporal aspects, the spatial heterogeneity, and inspection errors. It should also take into account the two well-known characteristics of pitting corrosion growing behavior: depth and time dependency of pit growth rate. Analysis of these different levels of uncertainties in the amount of corrosion damage over time should be performed for continuous and failure-free operation of the pipelines. This paper reviews some of the leading probabilistic data-driven prediction models for PHM analysis for oil and gas pipelines corroded by internal pitting. These models categorized as random variable-based and stochastic process-based models are reviewed and the appropriateness of each category is discussed. Since stochastic process-based models are more versatile to predict the behavior of internal pitting corrosion in oil and gas pipelines, the capabilities of the two popular stochastic process-based models, Markov process-based and gamma process-based, are discussed in more detail.
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Keywords
Prognostic and Health Management, Oil and Gas Pipeline Pitting Corrosion Growth Models
References
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ASTM G46-94. (2005). Standard guide for examination and evaluation of pitting corrosion.
Aziz, P. (1956). Application of the statistical theory of extreme values to the analysis of maximum pit depth data for aluminum. Corrosion, 12(10), 35-46. doi:10.5006/0010-9312-12.10.35
Bazán, F. A., & Beck, A. T. (2013). Stochastic process corrosion growth models for pipeline reliability. Corrosion Science, 74, 50-58. doi:10.1016/j.corsci.2013.04.011
Bhattacharya, R. N., & Waymire, E. C. (2009). Stochastic processes with application. New York: Wiley.
Bogdanoff, J. L., Kozin, F., & Saunders, H. (1985). Probabilistic models of cumulative damage. New York: John Wiley & Sons.
Caleyo, F., Velázquez, J. C., Hallen, J. M., Valor, A., & Esquivel-Amezcua. (2010). Markov chain model helps predict pitting corrosion depth and rate in underground pipelines. International Pipeline Conference (pp. 573-581). Alberta,Canada: American Society of Mechanical Engineers.
Caleyo, F., Velázquez, J. C., Valor, A., & Hallen, J. M. (2009). Markov chain modelling of pitting corrosion in underground pipelines. Corrosion Science, 51, 2197-2207. doi:j.corsci.2009.06.014
Castro, I. T., Caballé, N. C., & Pérez, C. J. (2015). A condition-based maintenance for a system subject to multiple degradation processes and external shocks. International Journal of Systems Science, 46(9), 1692-1704. doi:10.1080/00207721.2013.828796
Cox, D. R., & Miller, H. D. (1965). The theory of stochastic processes. London, UK: Methuen & Co. Ltd,.
Frangopol, D. M., Kallen, M. J., & Van Noortwijk, J. M. (2004). Probabilistic models for life-cycle performance of deteriorating structures: review and future directions. Progress in Structural Engineering and Materials, 6(4), 197-212. doi:10.1002/pse.180
Hong, H. P. (1999). Application of the stochastic process to pitting corrosion. Corrosion, 55(1), 10-16. doi:10.5006/1.3283958
Imanian, A., & Modarres, M. (2017). A thermodynamic entropy-based damage assessment with applications to prognostics and health management. Structural Health Monitoring. doi:10.1177/1475921716689561
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Maes, M. A., Faber, M. H., & Dann, M. R. (2009). Hierarchical modeling of pipeline defect growth subject to ILI uncertainty. Proceedings of the ASME 28th International Conference on Ocean, Offshore and Arctic Engineering, (pp. OMAE2009-79470). Honolulu, Hawaii.
Melchers, R. E. (2004). Pitting corrosion of mild steel in marine immersion environment—Part 2: Variability of maximum pit depth. Corrosion, 937-944. doi:10.5006/1.3287827
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Nuhi, M., Seer, T. A., Al Tamimi, A. M., Modarres, M., & Seibi, A. (2011). Reliability analysis for degradation effects of pitting corrosion in carbon steel pipes. Procedia Engineering(10), 1930-1935. doi:10.1016/j.proeng.2011.04.320
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Ossai, C. I., Boswell, B., & Davies, I. J. (2015). Predictive modelling of internal pitting corrosion of aged non-piggable pipelines. Journal of The Electrochemical Society, 162(6), C251-C259.
Papavinasam, S. (2013). Corrosion control in the oil and gas industry. Elsevier Inc.
Papavinasam, S., Revie, R. W., Friesen, W. I., Doiron, A., & Panneerselvan, T. (2006). Review of models to predict internal pitting corrosion of oil and gas pipelines. Corrosion Reviews, 24(3-4), 173-230.
Provan, J. W., & Rodriguez III, E. S. (1989). Part I: Development of a Markov description of pitting corrosion. Corrosion, 45(3), 178-192. doi:10.5006/1.3577840
Rabiei, E., Droguett, E. L., Modarres, M., & Amiri, M. (2015). Damage precursor based structural health monitoring and damage prognosis framework. Safety and Reliability of Complex Engineered Systems, (pp. 2441-2449).
Romanoff, M. (1957). Underground corrosion. Washington, DC: US Government Printing Office.
Ross, S. (1996). Stochastic processes. John Wiley & Sons,Inc.
Shibata, T. (1996). Statistical and stochastic approaches to localized corrosion. Corrosion, 52(11), 813-830. doi:10.5006/1.3292074
Strutt, J. E., Nicholls, J. R., & & Barbier, B. (1985). The prediction of corrosion by statistical analysis of corrosion profiles. Corrosion science, 305-315. doi:10.1016/0010-938X(85)90109-X
Tarantseva, K. (2010). Models and methods of forecasting pitting corrosion. Protection of metals and physical chemistry of surfaces, 46(1), 139-147. doi:10.1134/S2070205110010211
Tarantseva, K. (2010). Models and methods of forecasting pitting corrosion. Protection of metals and physical chemistry of surfaces, 46(1), 139-147.
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering. doi:10.1155/2015/793161
Valor, A., Caleyo, F., Alfonso, L., Rivas, D., & Hallen, J. M. (2007). Stochastic modeling of pitting corrosion: a new model for initiation and growth of multiple corrosion pits. Corrosion science, 49(2), 559-579. doi:10.1016/j.corsci.2006.05.049
Valor, A., Caleyo, F., Alfonso, L., Velázquez, J. C., & Hallen, J. M. (2013). Markov chain models for the stochastic modeling of pitting corrosion. Mathematical Problems in Engineering. doi:10.1155/2013/108386
Van Noortwijk, J. M. (2009). A survey of the application of gamma processes in maintenance. Reliability Engineering and System Safety, 94, 2-21. doi:10.1016/j.ress.2007.03.019
Velázquez, Caleyo, F., Valor, A., & Hallen, J. (2009). Predictive model for pitting corrosion in buried oil and gas pipelines. Corrosion, 65(5), 332-342. doi:10.5006/1.3319138
Zhang, S., & Zhou, W. (2013). System reliability of corroding pipelines considering stochastic process-based models for defect growth and internal pressure. International Journal of Pressure Vessels and Piping, 111(112), 120-130. doi:10.1016/j.ijpvp.2013.06.002
Zhang, S., & Zhou, W. (2014). Bayesian dynamic linear model for growth of corrosion defects on energy pipelines. Reliability Engineering & System Safety, 128, 24-31. doi:10.1016/j.ress.2014.04.001
Zhang, S., Zhou, W., & Qin, H. (2013). Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data. Corrosion Science, 73, 309-320. doi:10.1016/j.corsci.2013.04.020
Ascher, H., & Feingold, H. (1984). Repairable systems reliability: Modeling, inference, misconceptions and their causes. New York: CRC Press/Marcel Dekker, Inc.
ASTM G46-94. (2005). Standard guide for examination and evaluation of pitting corrosion.
Aziz, P. (1956). Application of the statistical theory of extreme values to the analysis of maximum pit depth data for aluminum. Corrosion, 12(10), 35-46. doi:10.5006/0010-9312-12.10.35
Bazán, F. A., & Beck, A. T. (2013). Stochastic process corrosion growth models for pipeline reliability. Corrosion Science, 74, 50-58. doi:10.1016/j.corsci.2013.04.011
Bhattacharya, R. N., & Waymire, E. C. (2009). Stochastic processes with application. New York: Wiley.
Bogdanoff, J. L., Kozin, F., & Saunders, H. (1985). Probabilistic models of cumulative damage. New York: John Wiley & Sons.
Caleyo, F., Velázquez, J. C., Hallen, J. M., Valor, A., & Esquivel-Amezcua. (2010). Markov chain model helps predict pitting corrosion depth and rate in underground pipelines. International Pipeline Conference (pp. 573-581). Alberta,Canada: American Society of Mechanical Engineers.
Caleyo, F., Velázquez, J. C., Valor, A., & Hallen, J. M. (2009). Markov chain modelling of pitting corrosion in underground pipelines. Corrosion Science, 51, 2197-2207. doi:j.corsci.2009.06.014
Castro, I. T., Caballé, N. C., & Pérez, C. J. (2015). A condition-based maintenance for a system subject to multiple degradation processes and external shocks. International Journal of Systems Science, 46(9), 1692-1704. doi:10.1080/00207721.2013.828796
Cox, D. R., & Miller, H. D. (1965). The theory of stochastic processes. London, UK: Methuen & Co. Ltd,.
Frangopol, D. M., Kallen, M. J., & Van Noortwijk, J. M. (2004). Probabilistic models for life-cycle performance of deteriorating structures: review and future directions. Progress in Structural Engineering and Materials, 6(4), 197-212. doi:10.1002/pse.180
Hong, H. P. (1999). Application of the stochastic process to pitting corrosion. Corrosion, 55(1), 10-16. doi:10.5006/1.3283958
Imanian, A., & Modarres, M. (2017). A thermodynamic entropy-based damage assessment with applications to prognostics and health management. Structural Health Monitoring. doi:10.1177/1475921716689561
Maes, M. A., Dann, M. R., Breitung, K. W., & Brehm, E. (2008). 6th International Probabilistic Workshop. Darmstadt, Germany.
Maes, M. A., Faber, M. H., & Dann, M. R. (2009). Hierarchical modeling of pipeline defect growth subject to ILI uncertainty. Proceedings of the ASME 28th International Conference on Ocean, Offshore and Arctic Engineering, (pp. OMAE2009-79470). Honolulu, Hawaii.
Melchers, R. E. (2004). Pitting corrosion of mild steel in marine immersion environment—Part 2: Variability of maximum pit depth. Corrosion, 937-944. doi:10.5006/1.3287827
Modarres, M., & Nuhi, M. (2010, January 6). Assessment of the integrity of pipelines subject to corrosion-fatigue, pitting corrosion, creep and stress corrossion carcking. 1st Annual PI Partner Schools Research Workshop. Abu Dhabi, U.A.E.
Nešić, S. (2007). Key issues related to modelling of internal corrosion of oil and gas pipelines–A review. Corrosion Science, 49(12), 4308-4338. doi:10.1016/j.corsci.2007.06.006
Nuhi, M., Seer, T. A., Al Tamimi, A. M., Modarres, M., & Seibi, A. (2011). Reliability analysis for degradation effects of pitting corrosion in carbon steel pipes. Procedia Engineering(10), 1930-1935. doi:10.1016/j.proeng.2011.04.320
Nyborg, R. (2010). Co2 Corrosion Models For Oil And Gas Production Systems. Corrosion 2010 NACE International.
Nyborg, R. (2010). CO2 corrosion models for oil and gas production systems. Corrosion. San Antonio, Texas: NACE International.
Ossai, C. I., Boswell, B., & Davies, I. J. (2015). Predictive modelling of internal pitting corrosion of aged non-piggable pipelines. Journal of The Electrochemical Society, 162(6), C251-C259.
Papavinasam, S. (2013). Corrosion control in the oil and gas industry. Elsevier Inc.
Papavinasam, S., Revie, R. W., Friesen, W. I., Doiron, A., & Panneerselvan, T. (2006). Review of models to predict internal pitting corrosion of oil and gas pipelines. Corrosion Reviews, 24(3-4), 173-230.
Provan, J. W., & Rodriguez III, E. S. (1989). Part I: Development of a Markov description of pitting corrosion. Corrosion, 45(3), 178-192. doi:10.5006/1.3577840
Rabiei, E., Droguett, E. L., Modarres, M., & Amiri, M. (2015). Damage precursor based structural health monitoring and damage prognosis framework. Safety and Reliability of Complex Engineered Systems, (pp. 2441-2449).
Romanoff, M. (1957). Underground corrosion. Washington, DC: US Government Printing Office.
Ross, S. (1996). Stochastic processes. John Wiley & Sons,Inc.
Shibata, T. (1996). Statistical and stochastic approaches to localized corrosion. Corrosion, 52(11), 813-830. doi:10.5006/1.3292074
Strutt, J. E., Nicholls, J. R., & & Barbier, B. (1985). The prediction of corrosion by statistical analysis of corrosion profiles. Corrosion science, 305-315. doi:10.1016/0010-938X(85)90109-X
Tarantseva, K. (2010). Models and methods of forecasting pitting corrosion. Protection of metals and physical chemistry of surfaces, 46(1), 139-147. doi:10.1134/S2070205110010211
Tarantseva, K. (2010). Models and methods of forecasting pitting corrosion. Protection of metals and physical chemistry of surfaces, 46(1), 139-147.
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering. doi:10.1155/2015/793161
Valor, A., Caleyo, F., Alfonso, L., Rivas, D., & Hallen, J. M. (2007). Stochastic modeling of pitting corrosion: a new model for initiation and growth of multiple corrosion pits. Corrosion science, 49(2), 559-579. doi:10.1016/j.corsci.2006.05.049
Valor, A., Caleyo, F., Alfonso, L., Velázquez, J. C., & Hallen, J. M. (2013). Markov chain models for the stochastic modeling of pitting corrosion. Mathematical Problems in Engineering. doi:10.1155/2013/108386
Van Noortwijk, J. M. (2009). A survey of the application of gamma processes in maintenance. Reliability Engineering and System Safety, 94, 2-21. doi:10.1016/j.ress.2007.03.019
Velázquez, Caleyo, F., Valor, A., & Hallen, J. (2009). Predictive model for pitting corrosion in buried oil and gas pipelines. Corrosion, 65(5), 332-342. doi:10.5006/1.3319138
Zhang, S., & Zhou, W. (2013). System reliability of corroding pipelines considering stochastic process-based models for defect growth and internal pressure. International Journal of Pressure Vessels and Piping, 111(112), 120-130. doi:10.1016/j.ijpvp.2013.06.002
Zhang, S., & Zhou, W. (2014). Bayesian dynamic linear model for growth of corrosion defects on energy pipelines. Reliability Engineering & System Safety, 128, 24-31. doi:10.1016/j.ress.2014.04.001
Zhang, S., Zhou, W., & Qin, H. (2013). Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data. Corrosion Science, 73, 309-320. doi:10.1016/j.corsci.2013.04.020
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