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Regression-based finite element machines for reliability modeling of downhole safety valves

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  • Colombo, Danilo
  • Lima, Gilson Brito Alves
  • Pereira, Danillo Roberto
  • Papa, João P.

Abstract

Downhole Safety Valve (DHSV) stands for a device widely used in offshore wells to ensure the integrity and avoid uncontrolled leaks of oil and gas to the environment, known as blowouts. The reliability estimation of such valves can be used to predict the blowout occurrence and to evaluate the workover demand, as well as to assist decision-making actions. In this paper, we introduce FEMaR, a Finite Element Machine for regression problems, which figures no training step, besides being parameterless. Another main contribution of this work is to evaluate several machine learning models to estimate the reliability of DHSVs for further comparison against traditional statistical methods. The experimental evaluation over a dataset collected from a Brazilian oil and gas company showed that machine learning techniques are capable of obtaining promising results, even in the presence of censored information, and they can outperform the statistical approaches considered in this work. Such findings also investigated using uncertainty analysis, evidenced that we can save economic resources and increase the safety at the offshore well operations.

Suggested Citation

  • Colombo, Danilo & Lima, Gilson Brito Alves & Pereira, Danillo Roberto & Papa, João P., 2020. "Regression-based finite element machines for reliability modeling of downhole safety valves," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:reensy:v:198:y:2020:i:c:s0951832018310287
    DOI: 10.1016/j.ress.2020.106894
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    References listed on IDEAS

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    1. Wang, Huiying & Wang, Wenbin & Peng, Rui, 2017. "A two-phase inspection model for a single component system with three-stage degradation," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 31-40.
    2. Hegde, Jeevith & Utne, Ingrid Bouwer & Schjølberg, Ingrid & Thorkildsen, Brede, 2018. "A Bayesian approach to risk modeling of autonomous subsea intervention operations," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 142-159.
    3. Gianluca Bontempi & Souhaib Ben Taieb & Yann-Aël Le Borgne, 2013. "Machine learning strategies for time series forecasting," ULB Institutional Repository 2013/167761, ULB -- Universite Libre de Bruxelles.
    4. Hauge, S. & Hokstad, P. & HÃ¥brekke, S. & Lundteigen, M.A., 2016. "Common cause failures in safety-instrumented systems: Using field experience from the petroleum industry," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 34-45.
    5. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
    6. Kris Boudt & Derya Caliskan & Christophe Croux, 2011. "Robust explicit estimators of Weibull parameters," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 187-209, March.
    7. Selvik, Jon Tømmerås & Signoret, Jean-Pierre, 2017. "How to interpret safety critical failures in risk and reliability assessments," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 61-68.
    8. Antonovsky, A. & Pollock, C. & Straker, L., 2016. "System reliability as perceived by maintenance personnel on petroleum production facilities," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 58-65.
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    Cited by:

    1. Srivastav, Himanshu & Lundteigen, Mary Ann & Barros, Anne, 2021. "Introduction of degradation modeling in qualification of the novel subsea technology," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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