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Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

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  • Naseri, Masoud
  • Baraldi, Piero
  • Compare, Michele
  • Zio, Enrico

Abstract

We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating conditions.

Suggested Citation

  • Naseri, Masoud & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2016. "Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 66-82.
    • Handle: RePEc:eee:reensy:v:152:y:2016:i:c:p:66-82
    DOI: 10.1016/j.ress.2016.03.004
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    Cited by:

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    2. Masoud Naseri & Javad Barabady, 2016. "On RAM performance of production facilities operating under the Barents Sea harsh environmental conditions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(3), pages 273-298, September.
    3. Ali N Qarahasanlou & Abbas Barabadi & Yonas Z Ayele, 2018. "Production performance analysis during operation phase: A case study," Journal of Risk and Reliability, , vol. 232(6), pages 559-575, December.
    4. Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
    5. Cai, Baoping & Xie, Min & Liu, Yonghong & Liu, Yiliu & Feng, Qiang, 2018. "Availability-based engineering resilience metric and its corresponding evaluation methodology," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 216-224.
    6. Okaro, Ikenna Anthony & Tao, Longbin, 2016. "Reliability analysis and optimisation of subsea compression system facing operational covariate stresses," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 159-174.
    7. Kumar, Sourabh & Kumar Barua, Mukesh, 2022. "Modeling and investigating the interaction among risk factors of the sustainable petroleum supply chain," Resources Policy, Elsevier, vol. 79(C).
    8. Eichhorn Colombo, Konrad W., 2023. "Financial resilience analysis of floating production, storage and offloading plant operated in Norwegian Arctic region: Case study using inter-/transdisciplinary system dynamics modeling and simulatio," Energy, Elsevier, vol. 268(C).
    9. Kumar, Sourabh & Barua, Mukesh Kumar, 2022. "A modeling framework and analysis of challenges faced by the Indian petroleum supply chain," Energy, Elsevier, vol. 239(PE).

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