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Reliability analysis and optimisation of subsea compression system facing operational covariate stresses

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  • Okaro, Ikenna Anthony
  • Tao, Longbin

Abstract

This paper proposes an enhanced Weibull-Corrosion Covariate model for reliability assessment of a system facing operational stresses. The newly developed model is applied to a Subsea Gas Compression System planned for offshore West Africa to predict its reliability index. System technical failure was modelled by developing a Weibull failure model incorporating a physically tested corrosion profile as stress in order to quantify the survival rate of the system under additional operational covariates including marine pH, temperature and pressure. Using Reliability Block Diagrams and enhanced Fusell-Vesely formulations, the whole system was systematically decomposed to sub-systems to analyse the criticality of each component and optimise them. Human reliability was addressed using an enhanced barrier weighting method. A rapid degradation curve is obtained on a subsea system relative to the base case subjected to a time-dependent corrosion stress factor. It reveals that subsea system components failed faster than their Mean time to failure specifications from Offshore Reliability Database as a result of cumulative marine stresses exertion. The case study demonstrated that the reliability of a subsea system can be systematically optimised by modelling the system under higher technical and organisational stresses, prioritising the critical sub-systems and making befitting provisions for redundancy and tolerances.

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  • 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.
    • Handle: RePEc:eee:reensy:v:156:y:2016:i:c:p:159-174
    DOI: 10.1016/j.ress.2016.07.018
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    References listed on IDEAS

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    Cited by:

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    2. Marwa Belhaj Salem & Mitra Fouladirad & Estelle Deloux, 2021. "Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process," Mathematics, MDPI, vol. 9(3), pages 1-25, January.
    3. Silva, L.M.R. & Guedes Soares, C., 2023. "Robust optimization model of an offshore oil production system for cost and pipeline risk of failure," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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    5. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    6. 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.
    7. 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).

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