IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v234y2023ics0951832023000856.html
   My bibliography  Save this article

Modeling and analysis of internal corrosion induced failure of oil and gas pipelines

Author

Listed:
  • Dao, Uyen
  • Sajid, Zaman
  • Khan, Faisal
  • Zhang, Yahui
  • Tran, Trung

Abstract

Internal corrosion is a complex phenomenon that includes under-deposit corrosion (UDC), microbially influenced corrosion (MIC), erosion, and localized and uniform corrosion mechanisms. For robust risk management of pipelines, there is a need to study the interactions of risk factors in internal corrosion. It is necessary to monitor the variations of corrosion risk factors and assess the pipeline's failure likelihood as a function of time. A Dynamic Object-Oriented Bayesian network (DOOBN) model is developed for this purpose. The DOOBN model has helped represent the probabilistic relationships among prevalent influencing risk factors and define their conditional dependencies. There are 94 risk factors considered for the different internal corrosion mechanisms. Results show that the probability of UDC occurrence for a given pipeline is 58%, while MIC is 48%. The study also confirms the increase in asset failure rate with the rise in internal corrosion. Results of the model are validated using filed data, and an accuracy of 93.22% is observed. The study serves as an early warning guide for the integrity management of pipelines against internal corrosion.

Suggested Citation

  • Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui & Tran, Trung, 2023. "Modeling and analysis of internal corrosion induced failure of oil and gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
  • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000856
    DOI: 10.1016/j.ress.2023.109170
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023000856
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109170?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bruce G. Marcot & Anca M. Hanea, 2021. "What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?," Computational Statistics, Springer, vol. 36(3), pages 2009-2031, September.
    2. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Liu, Cuiwei & Wang, Yazhen & Li, Xinhong & Li, Yuxing & Khan, Faisal & Cai, Baoping, 2021. "Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    4. Andreini, Marco & Gardoni, Paolo & Pagliara, Stefano & Sassu, Mauro, 2019. "Probabilistic models for the erosion rate in embankments and reliability analysis of earth dams," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 142-155.
    5. Fergus Bolger, 2018. "The Selection of Experts for (Probabilistic) Expert Knowledge Elicitation," International Series in Operations Research & Management Science, in: Luis C. Dias & Alec Morton & John Quigley (ed.), Elicitation, chapter 0, pages 393-443, Springer.
    6. Sajid, Zaman, 2021. "A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    7. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    8. Sajid, Zaman & Khan, Faisal & Zhang, Yan, 2018. "A novel process economics risk model applied to biodiesel production system," Renewable Energy, Elsevier, vol. 118(C), pages 615-626.
    9. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    10. Heidary, Roohollah & Groth, Katrina M., 2021. "A hybrid population-based degradation model for pipeline pitting corrosion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    11. Sidum Adumene & Rabiul Islam & Ibitoru Festus Dick & Esmaeil Zarei & Morrison Inegiyemiema & Ming Yang, 2022. "Influence-Based Consequence Assessment of Subsea Pipeline Failure under Stochastic Degradation," Energies, MDPI, vol. 15(20), pages 1-10, October.
    12. Yang, Yongsheng & Khan, Faisal & Thodi, Premkumar & Abbassi, Rouzbeh, 2017. "Corrosion induced failure analysis of subsea pipelines," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 214-222.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Guohua & Li, Geliang & Xie, Mulin & Xu, Qiming & Zhang, Geng, 2024. "A probabilistic analysis method based on Noisy-OR gate Bayesian network for hydrogen leakage of proton exchange membrane fuel cell," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui, 2023. "Dynamic Bayesian network model to study under-deposit corrosion," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Miao, Xingyuan & Zhao, Hong, 2024. "Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Woloszyk, Krzysztof & Garbatov, Yordan, 2024. "A probabilistic-driven framework for enhanced corrosion estimation of ship structural components," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Zerouali, Bilal & Sahraoui, Yacine & Nahal, Mourad & Chateauneuf, Alaa, 2024. "Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 249(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui, 2023. "Dynamic Bayesian network model to study under-deposit corrosion," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Na, Kyumin & Yoon, Heonjun & Kim, Jaedong & Kim, Sungjong & Youn, Byeng D., 2023. "PERL: Probabilistic energy-ratio-based localization for boiler tube leaks using descriptors of acoustic emission signals," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    6. Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Guo, Jian & Ma, Kaijiang, 2024. "Risk analysis for hazardous chemical vehicle-bridge transportation system: A dynamic Bayesian network model incorporating vehicle dynamics," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    8. Li, Pengyu & Wang, Xiufang & Jiang, Chunlei & Bi, Hongbo & Liu, Yongzhi & Yan, Wendi & Zhang, Cong & Dong, Taiji & Sun, Yu, 2024. "Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    9. Kamei, Sayaka & Taghipour, Sharareh, 2023. "A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    10. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    11. Xia, Huosong & Wang, Yuan & Zhang, Justin Zuopeng & Zheng, Leven J. & Kamal, Muhammad Mustafa & Arya, Varsha, 2023. "COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    12. Belton, Ian & Wright, George & Sissons, Aileen & Bolger, Fergus & Crawford, Megan M. & Hamlin, Iain & Taylor Browne Lūka, Courtney & Vasilichi, Alexandrina, 2021. "Delphi with feedback of rationales: How large can a Delphi group be such that participants are not overloaded, de-motivated, or disengaged?," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    13. Xie, Shuyi & Huang, Zimeng & Wu, Gang & Luo, Jinheng & Li, Lifeng & Ma, Weifeng & Wang, Bohong, 2024. "Combining precursor and Cloud Leaky noisy-OR logic gate Bayesian network for dynamic probability analysis of major accidents in the oil depots," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    14. Wang, WuChang & Zhang, Yi & Li, YuXing & Hu, Qihui & Liu, Chengsong & Liu, Cuiwei, 2022. "Vulnerability analysis method based on risk assessment for gas transmission capabilities of natural gas pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    15. Sadeghi Darvazeh, Saeed & Mansoori Mooseloo, Farzaneh & Gholian-Jouybari, Fatemeh & Amiri, Maghsoud & Bonakdari, Hossein & Hajiaghaei-Keshteli, Mostafa, 2024. "Data-driven robust optimization to design an integrated sustainable forest biomass-to-electricity network under disjunctive uncertainties," Applied Energy, Elsevier, vol. 356(C).
    16. Zaman Sajid & Nicholas Lynch, 2018. "Financial Modelling Strategies for Social Life Cycle Assessment: A Project Appraisal of Biodiesel Production and Sustainability in Newfoundland and Labrador, Canada," Sustainability, MDPI, vol. 10(9), pages 1-19, September.
    17. Zhang, Hua & Li, Zongkun & Ge, Wei & Zhang, Yadong & Wang, Te & Sun, Heqiang & Jiao, Yutie, 2024. "An extended Bayesian network model for calculating dam failure probability based on fuzzy sets and dynamic evidential reasoning," Energy, Elsevier, vol. 301(C).
    18. Amaya-Gómez, Rafael & Schoefs, Franck & Sánchez-Silva, Mauricio & Muñoz, Felipe & Bastidas-Arteaga, Emilio, 2022. "Matching of corroded defects in onshore pipelines based on In-Line Inspections and Voronoi partitions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    19. Salem, Marwa Belhaj & Fouladirad, Mitra & Deloux, Estelle, 2022. "Variance Gamma process as degradation model for prognosis and imperfect maintenance of centrifugal pumps," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    20. Qianying Jin & Kristiaan Kerstens & Ignace Van de Woestyne, 2024. "Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(4), pages 1213-1239, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000856. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.