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Uncertainty in the Assessment of Corroded Pipelines

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Corrosion and Reliability Assessment of Inspected Pipelines

Abstract

Pipeline corrosion is uncertain by nature. It depends on several uncontrolled parameters, such as the transporting fluid, the surrounding soil, pipeline geometry, and material strength. As mentioned in the previous chapter, pipeline operators can monitor corrosion at the inner/outer wall using ILI measurements every 2 to 6 years. However, these inspections are subject to uncertain measurements as well as how the corrosion will evolve between consecutive inspections. Uncertainties include the degradation model, the location of the defects, and the local uncertainties of the inspection tool. The degradation uncertainties are related to the lack of knowledge (epistemic uncertainty) of how each defect evolves. Also, local variations of features affect the degradation process, e.g., material properties, stress, temperature, or pressure. The corrosion degradation process changes with time, and finally, the data comes from imperfect inspection results. ILI measurements may hide existing defects that did not fulfill the detection requirement of the inspection tool. Also, the reported defects can have inaccurate locations and sizes, depending on the implemented PIG tool. This chapter focuses on the local inspection uncertainties and those associated with the temporal modeling of the degradation corrosion. The corrosion prediction is required to support further decisions to maintain adequate pipeline integrity. ILI measurements indicate the pipeline condition, which allows updating the corrosion predictions between inspections and identifying further critical pipeline segments. Different parameters such as soil aggressiveness, operating parameters, or the fluid would affect the obtained predictions. This chapter introduces the uncertainties associated with ILI inspections and the prediction of the degradation process.

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Notes

  1. 1.

    Risk is defined here as the product of the frequency/failure of occurrence of an event and its severity.

References

  1. M.A. Maes, M.H. Faber, M.R. Dann, Hierarchical modeling of pipeline defect growth subject to ILI uncertainty, in 28th International Conference on Ocean, Offshore Mechanics and Artic Engineering, Hamburg, 2009. OMAE2009-79470

    Google Scholar 

  2. T. Aven, Risk Analysis: Assessing Uncertainties Beyond Expected Values and Probabilities (Wiley, 2008)

    Google Scholar 

  3. M. Sánchez-Silva, G.-A. Klutke, Reliability and Life-Cycle Analysis of Deteriorating Systems. Springer Series in Reliability Engineering (Springer, 2016)

    Google Scholar 

  4. R.H. Dieck, Measurement Uncertainty: Methods and Applications (ISA, 2007)

    Google Scholar 

  5. J.C. Helton, J.D. Johnson, W.L. Oberkampf, An exploration of alternative approaches to the representation of uncertainty in model predictions. Reliab. Eng. Syst. Saf. 85(1), 39–71 (2004). Alternative Representations of Epistemic Uncertainty

    Google Scholar 

  6. F.A. Ocampo, Marco para el tratamiento de incertidumbre en el análisis de riesgo cuantitativo en transporte de material peligroso a través de tuberías. Master’s thesis, 2016

    Google Scholar 

  7. T. Aven, P. Baraldi, R. Flage, E. Zio, Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-probabilistic Methods (Wiley, 2014)

    Google Scholar 

  8. G. Kopp, H. Willems, Sizing limits of metal loss anomalies using tri-axial MFL measurements: a model study. NDT & E Int. 55, 75–81 (2013)

    Article  Google Scholar 

  9. H.R. Vanaei, A. Eslami, A. Egbewande A review on pipeline corrosion, in-line inspection (ILI), and corrosion growth rate models. Int. J. Pressure Vessels Piping 149, 43–54 (2017)

    Google Scholar 

  10. W. Mao, L. Clapham, D.L. Atherton, Effects of alignment of nearby corrosion pits on MFL. NDT & E Int. 36(2), 111–116 (2003)

    Article  CAS  Google Scholar 

  11. B. Eiber, Overview of Integrity Assessment Methods for Pipelines. Technical report, Robert J. Eiber Consultant Inc, 2003

    Google Scholar 

  12. A. Barbian, M. Beller, N. Thielager, H. Willems, A new in-line inspection tool for the quantitative wall thickness measurement of gas pipelines: first results, in 4th Pipeline Technology Conference, Hannover, 2009

    Google Scholar 

  13. K. Reber, M. Beller, Ultrasonic in-line inspection tools to inspect older pipelines for cracks in girth and long-seam welds, in Pigging and Service Association Seminar, Aberdeen, 2003

    Google Scholar 

  14. POF, Specifications and Requirements for Intelligent Pig Inspection of Pipelines. Technical report, Pipeline Operators Forum, 2008

    Google Scholar 

  15. M.D. Pandey, Probabilistic models for condition assessment of oil and gas pipelines. NDT & E Int. 31(5), 349–358 (1998)

    Article  CAS  Google Scholar 

  16. S.P. Kuniewski, J.A.M. van der Weide, J.M. van Noortwijk, Sampling inspection for the evaluation of time-dependent reliability of deteriorating systems under imperfect defect detection. Reliab. Eng. Syst. Saf. 94(9), 1480–1490 (2009). ESREL 2007, the 18th European Safety and Reliability Conference

    Google Scholar 

  17. M.D. Pandey, D. Lu, Estimation of parameters of degradation growth rate distribution from noisy measurement data. Struct. Saf. 43, 60–69 (2013)

    Article  Google Scholar 

  18. H. Qin, Probabilistic Modeling and Bayesian Inference of Metal-Loss Corrosion with Application in Reliability Analysis for Energy Pipelines. Master’s thesis, 2014. Electronic Thesis and Dissertation Repository. Paper 2246

    Google Scholar 

  19. S. Zhang, W. Zhou, H. Qin, Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data. Corrosion Sci. 73, 309–320 (2013)

    Article  CAS  Google Scholar 

  20. ROSEN, Magnetic Flux Leakage. http://www.rosen-group.com/global/solutions/solution-scout.html?tag_technologies=magnetic-flux-leakage, 2014

  21. NACE International, SP0502-2010 Pipeline External Corrosion Direct Assessment Methodology. Standard Recommended Practice. Technical report, Houston, 2010

    Google Scholar 

  22. D. Straub, M.H. Faber, Temporal variability in corrosion modeling and reliability updating. J. Offshore Mech. Arctic Eng. 169(4), 265–272 (2007)

    Article  Google Scholar 

  23. I. Song, S.R. Park, S. Yoon, Probability and Random Variables: Theory and Applications (Springer, Cham, 2022)

    Book  Google Scholar 

  24. V. Krishnan, K. Chandra, Probability and Random Processes (Wiley, 2015)

    Google Scholar 

  25. S.H. Chan, Introduction to Probability for Data Science (Michigan Publishing, 2021)

    Google Scholar 

  26. E. Çinlar, Probability and Stochastics. Graduate Texts in Mathematics (Springer, New York, 2011)

    Google Scholar 

  27. K.M. Ramachandran, C.P. Tsokos (eds.) Mathematical Statistics with Applications in R, 2nd edn. (Academic, Boston, 2015)

    Google Scholar 

  28. E. Lukacs, Stochastic Convergence (Academic, 1975)

    Google Scholar 

  29. M. Sánchez-Silva, G.A. Klutke, D.V. Rosowsky, Life-cycle performance of structures subject to multiple deterioration mechanisms. Struct. Saf. 33(3), 206–217 (2011)

    Article  Google Scholar 

  30. J. Riascos-Ochoa, M. Sánchez-Silva, G.-A. Klutke, Modeling and reliability analysis of systems subject to multiple sources of degradation based on Lévy processes. Prob. Eng. Mech. 45, 164–176 (2016)

    Article  Google Scholar 

  31. J.M. van Noortwijk, A survey of the application of gamma processes in maintenance. Reliab. Eng. Syst. Saf. 94(1), 2–21 (2009). Maintenance Modeling and Application

    Google Scholar 

  32. J.M. van Noortwijk, M.D. Pandey, A stochastic deterioration process for time-dependent reliability analysis, in Eleventh IFIP WG 7.5 Working Conference on Reliability and Optimization of Structural Systems, Banff, 2004

    Google Scholar 

  33. W. Zhou, H.P. Hong, S. Zhang Impact of dependent stochastic defect growth on system reliability of corroding pipelines. Int. J. Pressure Vessels Piping 96 and 97, 68–77 (2012)

    Google Scholar 

  34. S. Zhang, W. Zhou, Cost-based optimal maintenance decisions for corroding natural gas pipelines based on stochastic degradation models. Eng. Struct. 74, 74–85 (2014)

    Article  Google Scholar 

  35. H. Qin, S. Zhang, W. Zhou, Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines. Front. Struct. Civil Eng. 7(3), 276–287 (2013)

    Article  Google Scholar 

  36. W. Zhou, W. Xiang, H.P. Hong, Sensitivity of system reliability of corroding pipelines to modeling of stochastic growth of corrosion defects. Reliab. Eng. Syst. Saf. 167, 428–438 (2017). Special Section: Applications of Probabilistic Graphical Models in Dependability, Diagnosis and Prognosis

    Google Scholar 

  37. N. Chen, Z.-S. Ye, Y. Xiang, L. Zhang, Condition-based maintenance using the inverse Gaussian degradation model. Eur. J. Oper. Res. 243(1), 190–199 (2015)

    Article  Google Scholar 

  38. R.P. Nicolai, R. Dekker, J.M. van Noortwijk, A comparison of models for measurable deterioration: an application to coatings on steel structures. Reliab. Eng. Syst. Saf. 92(12), 1635–1650 (2007) Special Issue on {ESREL} 2005

    Google Scholar 

  39. A.N. Avramidis, P. L’Ecuyer, P.-A. Tremblay, Efficient simulation of gamma and variance-gamma processes, in Proceedings of the 2003 Winter Simulation Conference, New Orleans (2003)

    Google Scholar 

  40. O.E. Farestveit, A Simulation-based approach for exploring the degradation and Maintenance of a Single-Unit System. Master’s thesis, 2015

    Google Scholar 

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Amaya-Gómez, R., Bastidas-Arteaga, E., Sánchez-Silva, M., Schoefs, F., Muñoz, F. (2024). Uncertainty in the Assessment of Corroded Pipelines. In: Corrosion and Reliability Assessment of Inspected Pipelines . Springer, Cham. https://doi.org/10.1007/978-3-031-43532-4_5

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