Optimal Maintenance Policy for Corroded Oil and Gas Pipelines using Markov Decision Processes
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Published
Mar 13, 2022
Roohollah Heidarydashtarjandi
Jubilee Prasad-Rao
Katrina M. Groth
Abstract
This paper presents a novel approach to determine optimal maintenance policies for degraded oil and gas pipelines due to internal pitting corrosion. This approach builds a bridge between Markov process-based corrosion rate models and Markov decision processes (MDP). This bridging allows for the consideration of both short-term and long-term costs for optimal pipeline maintenance operations. To implement MDP, probability transition matrices are estimated to move from one degradation state to the next in the pipeline degradation Markov processes. A case study is also implemented with four pipeline failure modes (i.e., safe, small leak, large leak, and rupture). And four maintenance actions (i.e. do nothing, adding corrosion inhibitors, pigging, and replacement) are considered by assuming perfect pipeline inspections. Monte Carlo simulation is performed on 10,000 initial pits using the selected corrosion models and assumed maintenance and failure costs to determine an optimal maintenance policy.
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Keywords
Markov decision process, Pipeline corrosion, Optimal maintenance policy, Corrosion rate models
References
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Caleyo, F., Vela´ zquez, J., Hallen, J., Valor, A., & Esquivel-Amezcua, A. (2010). Markov chain model helps predict pitting corrosion depth and rate in underground pipelines. In International pipeline conference (Vol. 44236, pp. 573–581).
Dann, M. R., & Maes, M. A. (2018). Stochastic corrosion growth modeling for pipelines using mass inspection data. Reliability Engineering & System Safety, 180, 245–254.
Heidary, R., Gabriel, S. A., Modarres, M., Groth, K. M., & Vahdati, N. (2018). A review of data-driven oil and gas pipeline pitting corrosion growth models applicable for prognostic and health management. International Journal of Prognostics and Health Management, 9(1).
Heidary, R., & Groth, K. M. (2020). A hybrid model of internal pitting corrosion degradation under changing operational conditions for pipeline integrity management. Structural Health Monitoring, 19(4), 1075–1091.
Heidary, R., & Groth, K. M. (2021). A hybrid population-based degradation model for pipeline pitting corrosion. Reliability Engineering & System Safety, 214, 107740.
Imanian, A., & Modarres, M. (2018). A thermodynamic entropy-based damage assessment with applications to prognostics and health management. Structural Health Monitoring, 17(2), 240–254.
Kiefner, J., Maxey, W., Eiber, R., & Duffy, A. (1973). Failure stress levels of flaws in pressurized cylinders. In Progress in flaw growth and fracture toughness testing. ASTM International.
Kishawy, H. A., & Gabbar, H. A. (2010). Review of pipeline integrity management practices. International Journal of Pressure Vessels and Piping, 87(7), 373–380.
Leis, B., Stephens, D., et al. (1997). An alternative approach to assess the integrity of corroded line pipe-part i: current status. In The seventh international offshore and polar engineering conference.
Modarres, M., Kaminskiy, M. P., & Krivtsov, V. (2016). Reliability engineering and risk analysis: a practical guide. CRC press.
Nuhi, M., Seer, T. A., Al Tamimi, A., Modarres, M., & Seibi, A. (2011). Reliability analysis for degradation effects of pitting corrosion in carbon steel pipes. Procedia Engineering, 10, 1930–1935.
Ossai, C. I., Boswell, B., & Davies, I. J. (2015). Predictive modelling of internal pitting corrosion of aged nonpiggable pipelines. Journal of The Electrochemical Society, 162(6), C251.
Papavinasam, S. (2013). Corrosion control in the oil and gas industry. Elsevier.
Provan, J., & Rodriguez III, E. (1989). Part i: Development of a markov description of pitting corrosion. Corrosion, 45(3), 178–192.
Ross, S. M., Kelly, J. J., Sullivan, R. J., Perry, W. J., Mercer, D., Davis, R. M., . . . Bristow, V. L. (1996). Stochastic processes (Vol. 2). Wiley New York.
S´anchez-Silva, M., Frangopol, D. M., Padgett, J., & Soliman, M. (2016). Maintenance and operation of infrastructure systems. Journal of Structural Engineering, 142(9), F4016004.
Shibata, T. (1996). Statistical and stochastic approaches to localized corrosion: 1996 wr whitney award lecture. Corrosion, 52(11).
Timashev, S., Malyukova, M., Poluian, L., & Bushinskaya, A. (2008). Markov description of corrosion defects growth and its application to reliability based inspection and maintenance of pipelines. In International pipeline conference (Vol. 48609, pp. 525–533).
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, 2015.
Valor, A., Alfonso, L., Caleyo, F., Vidal, J., Perez-Baruch, E., & Hallen, J. M. (2015). The negative binomial distribution as a model for external corrosion defect counts in buried pipelines. Corrosion Science, 101, 114–131.
Valor, A., Caleyo, F., Alfonso, L., Rivas, D., & Hallen, J. (2007). Stochastic modeling of pitting corrosion: a new model for initiation and growth of multiple corrosion pits. Corrosion science, 49(2), 559–579.
Valor, A., Caleyo, F., Alfonso, L., Vidal, J., & Hallen, J. M. (2014). Statistical analysis of pitting corrosion field data and their use for realistic reliability estimations in non-piggable pipeline systems. Corrosion, 70(11), 1090–1100.
Zhang, S., & Zhou, W. (2014). Bayesian dynamic linear model for growth of corrosion defects on energy pipelines. Reliability Engineering & System Safety, 128, 24–31.
Zhou, W. (2010). System reliability of corroding pipelines. International Journal of Pressure Vessels and Piping, 87(10), 587–595.
Caleyo, F., Vela´ zquez, J., Hallen, J., Valor, A., & Esquivel-Amezcua, A. (2010). Markov chain model helps predict pitting corrosion depth and rate in underground pipelines. In International pipeline conference (Vol. 44236, pp. 573–581).
Dann, M. R., & Maes, M. A. (2018). Stochastic corrosion growth modeling for pipelines using mass inspection data. Reliability Engineering & System Safety, 180, 245–254.
Heidary, R., Gabriel, S. A., Modarres, M., Groth, K. M., & Vahdati, N. (2018). A review of data-driven oil and gas pipeline pitting corrosion growth models applicable for prognostic and health management. International Journal of Prognostics and Health Management, 9(1).
Heidary, R., & Groth, K. M. (2020). A hybrid model of internal pitting corrosion degradation under changing operational conditions for pipeline integrity management. Structural Health Monitoring, 19(4), 1075–1091.
Heidary, R., & Groth, K. M. (2021). A hybrid population-based degradation model for pipeline pitting corrosion. Reliability Engineering & System Safety, 214, 107740.
Imanian, A., & Modarres, M. (2018). A thermodynamic entropy-based damage assessment with applications to prognostics and health management. Structural Health Monitoring, 17(2), 240–254.
Kiefner, J., Maxey, W., Eiber, R., & Duffy, A. (1973). Failure stress levels of flaws in pressurized cylinders. In Progress in flaw growth and fracture toughness testing. ASTM International.
Kishawy, H. A., & Gabbar, H. A. (2010). Review of pipeline integrity management practices. International Journal of Pressure Vessels and Piping, 87(7), 373–380.
Leis, B., Stephens, D., et al. (1997). An alternative approach to assess the integrity of corroded line pipe-part i: current status. In The seventh international offshore and polar engineering conference.
Modarres, M., Kaminskiy, M. P., & Krivtsov, V. (2016). Reliability engineering and risk analysis: a practical guide. CRC press.
Nuhi, M., Seer, T. A., Al Tamimi, A., Modarres, M., & Seibi, A. (2011). Reliability analysis for degradation effects of pitting corrosion in carbon steel pipes. Procedia Engineering, 10, 1930–1935.
Ossai, C. I., Boswell, B., & Davies, I. J. (2015). Predictive modelling of internal pitting corrosion of aged nonpiggable pipelines. Journal of The Electrochemical Society, 162(6), C251.
Papavinasam, S. (2013). Corrosion control in the oil and gas industry. Elsevier.
Provan, J., & Rodriguez III, E. (1989). Part i: Development of a markov description of pitting corrosion. Corrosion, 45(3), 178–192.
Ross, S. M., Kelly, J. J., Sullivan, R. J., Perry, W. J., Mercer, D., Davis, R. M., . . . Bristow, V. L. (1996). Stochastic processes (Vol. 2). Wiley New York.
S´anchez-Silva, M., Frangopol, D. M., Padgett, J., & Soliman, M. (2016). Maintenance and operation of infrastructure systems. Journal of Structural Engineering, 142(9), F4016004.
Shibata, T. (1996). Statistical and stochastic approaches to localized corrosion: 1996 wr whitney award lecture. Corrosion, 52(11).
Timashev, S., Malyukova, M., Poluian, L., & Bushinskaya, A. (2008). Markov description of corrosion defects growth and its application to reliability based inspection and maintenance of pipelines. In International pipeline conference (Vol. 48609, pp. 525–533).
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, 2015.
Valor, A., Alfonso, L., Caleyo, F., Vidal, J., Perez-Baruch, E., & Hallen, J. M. (2015). The negative binomial distribution as a model for external corrosion defect counts in buried pipelines. Corrosion Science, 101, 114–131.
Valor, A., Caleyo, F., Alfonso, L., Rivas, D., & Hallen, J. (2007). Stochastic modeling of pitting corrosion: a new model for initiation and growth of multiple corrosion pits. Corrosion science, 49(2), 559–579.
Valor, A., Caleyo, F., Alfonso, L., Vidal, J., & Hallen, J. M. (2014). Statistical analysis of pitting corrosion field data and their use for realistic reliability estimations in non-piggable pipeline systems. Corrosion, 70(11), 1090–1100.
Zhang, S., & Zhou, W. (2014). Bayesian dynamic linear model for growth of corrosion defects on energy pipelines. Reliability Engineering & System Safety, 128, 24–31.
Zhou, W. (2010). System reliability of corroding pipelines. International Journal of Pressure Vessels and Piping, 87(10), 587–595.
Section
Technical Papers