Ocean Systems Engineering

  • 제8권1호
  • /
  • Pages.41-55
  • /
  • 2018
  • /
  • 2093-6702(pISSN)
  • /
  • 2093-677X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Fuzzy event tree analysis for quantified risk assessment due to oil and gas leakage in offshore installations

  • Cheliyan, A.S. (Department of Ocean Engineering, Indian Institute of Technology Madras) ;
  • Bhattacharyya, S.K. (Department of Ocean Engineering, Indian Institute of Technology Madras)
  • 투고 : 2017.08.08
  • 심사 : 2018.03.05
  • 발행 : 2018.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

Accidental oil and gas leak is a critical concern for the offshore industry because it can lead to severe consequences and as a result, it is imperative to evaluate the probabilities of occurrence of the consequences of the leakage in order to assess the risk. Event Tree Analysis (ETA) is a technique to identify the consequences that can result from the occurrence of a hazardous event. The probability of occurrence of the consequences is evaluated by the ETA, based on the failure probabilities of the sequential events. Conventional ETA deals with events with crisp failure probabilities. In offshore applications, it is often difficult to arrive at a single probability measure due to lack of data or imprecision in data. In such a scenario, fuzzy set theory can be applied to handle imprecision and data uncertainty. This paper presents fuzzy ETA (FETA) methodology to compute the probability of the outcomes initiated due to oil/gas leak in an actual offshore-onshore installation. Post FETA, sensitivity analysis by Fuzzy Weighted Index (FWI) method is performed to find the event that has the maximum contribution to the severe sequences. It is found that events of 'ignition', spreading of fire to 'equipment' and 'other areas' are the highest contributors to the severe consequences, followed by failure of 'leak detection' and 'fire detection' and 'fire water not being effective'. It is also found that the frequency of severe consequences that are catastrophic in nature obtained by ETA is one order less than that obtained by FETA, thereby implying that in ETA, the uncertainty does not propagate through the event tree. The ranking of severe sequences based on their probability, however, are identical in both ETA and FETA.

키워드

참고문헌

  1. Ferdous, R., Khan, F., Veitch, B. and Amyotte, P. (2009), "Methodology for computer-aided fuzzy fault tree analysis", Process Saf. Environ. Protection J., 87, 217-226. https://doi.org/10.1016/j.psep.2009.04.004
  2. Cheng, Y. (2000), "Uncertainties in fault tree analysis", Tamkang J. Sci. Eng., 3(1), 23-29.
  3. Clemen, R.T. and Winkler, R.L. (1999), "Combining probability distribution from experts in risk analysis", Risk Anal., 19(4), 187-203.
  4. Ericson, C.A. (2005), Event Tree Analysis Hazard Analysis Techniques for System Safety, John Wiley & Sons, Inc.
  5. Hu, X., Zhang, H., Duan, M. and Ni, M. (2012), "Risk analysis of oil/gas leakage of subsea production system based on fuzzy fault tree", Int. J. Energ. Eng., 2(3), 79-85.
  6. Kenarangui, R. (1991), "Event-tree analysis by fuzzy probability", IEEE T. Reliab., 40(1), 12-124.
  7. Klir, J.G. and Yuan, B. (2001), Fuzzy Sets and Fuzzy logic Theory and Applications, Prentice, Hall of India Private, Ltd.
  8. Lai, F.S, Shenoi, S. and Fan, T.L. (1993), Fuzzy fault tree analysis theory and applications, Engineering Risk and Hazard Assessment, CRC Press Inc, Florida, 117-137.
  9. Lavasani, M.R., Wang, J., Yang, Z. and Finlay, J. (2011), "Application of fuzzy fault tree analysis on oil and gas offshore pipelines", Int. J. Marine Sci. Eng., 1(1), 29-42.
  10. Lees, F.P. (2005), Loss Prevention in the Process Industries, Third Edition, Butterworths, London.
  11. Liou, T.S. and Wang, M.J.J. (1992), "Ranking fuzzy numbers with integral value", Fuzzy Set. Syst., 50, 247-255. https://doi.org/10.1016/0165-0114(92)90223-Q
  12. Misra, K.B. and Weber, G.G. (1990), "Use of fuzzy set theory for level-I studies in probabilistic risk assessment", Fuzzy Set. Syst., 37(2), 139-160. https://doi.org/10.1016/0165-0114(90)90038-8
  13. OGP Risk Assessment Data Directory (2010a), Ignition probabilities. Report 434-06.
  14. OGP Risk Assessment Data Directory (2010b), Process release frequencies. Report 434-01.
  15. Onisawa, T. (1988), "A representation of human reliability using fuzzy concepts", Inform. Sci., 45, 153-173. https://doi.org/10.1016/0020-0255(88)90038-2
  16. Onisawa, T. (1988), "An approach to human reliability in man-machine systems using error possibility", Fuzzy Set. Syst., 27, 87-103. https://doi.org/10.1016/0165-0114(88)90140-6
  17. Onisawa, T. (1990), "An application of fuzzy concepts to modelling of reliability analysis", Fuzzy Set. Syst., 37, 266-286.
  18. Onisawa, T. (1993), "On fuzzy set operations in system reliability analysis with natural language", Japanese J. Fuzzy Theory Syst., 5(1) 1-14. https://doi.org/10.3156/jfuzzy.5.1_1
  19. Onisawa, T. (1993), Use of natural language in the system reliability analysis, Fuzzy Logic: State of the Art, Kluwer Academic Publishers, Dordrecht, The Netherlands, 517-529.
  20. Onisawa, T. (1996), "Subjective analysis of system reliability and its analyzer", Fuzzy Set. Syst., 83, 249-269. https://doi.org/10.1016/0165-0114(95)00381-9
  21. Ramzali, N., Lavasani, M.R.M. and Ghodousi, J. (2015), "Safety barriers analysis of offshore drilling system by employing fuzzy event tree analysis", Saf. Sci., 78, 49-59. https://doi.org/10.1016/j.ssci.2015.04.004
  22. Ram Prasad, M.V.S. (2010), Risk assessment and integrity management of pipeline system in an offshore gas field, M.S thesis, IIT Madras.
  23. Ross, J.T. (2004), Fuzzy Logic with Engineering Application, John Wiley & Sons, Ltd, West Sussex, England.
  24. Shan, X., Liu, K. and Sun, P.L. (2017), Risk analysis on leakage failure of natural gas pipelines by fuzzy Bayesian network with a bow-tie model, Scientific Programming, Article ID 3639524, 11 pages.
  25. Silvianita, K.M.F. and Kurian, V.J. (2013), "Decision making for safety assessment of mobile mooring system", J.Teknologi (Sciences & Engineering), 61(3), 41-52.
  26. Singer, D. (1990), "A fuzzy set approach to fault tree and reliability analysis", Fuzzy Set. Syst., 34, 145-155. https://doi.org/10.1016/0165-0114(90)90154-X
  27. Sivanandam, S.N., Sumathi, S. and Deepa, S.N. (2007), Introduction to fuzzy logic using Matlab, Springer.
  28. Suresh, P. V., Babar, A.K. and Raj, V.V. (1996), "Uncertainty in fault tree analysis: A fuzzy approach", Fuzzy Set. Syst., 83, 135-141. https://doi.org/10.1016/0165-0114(95)00386-X
  29. Vinnem, J.E. (2014), Offshore Risk Assessment vol. 1. Principles, Modelling and Applications of QRA Studies. Springer.
  30. Wang, D., Liu, E. and Huang, L. (2017), "Event tree analysis of Xinda oil pipeline leakage based on fuzzy set theory", Open Civil Eng. J., 11, 101-108. https://doi.org/10.2174/1874149501711010101
  31. Yuhua, D. and Datao, Y. (2005), "Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis", J. Loss Prevent. Proc., 18, 83-88. https://doi.org/10.1016/j.jlp.2004.12.003
  32. Zadeh, L.A. (1965), "Fuzzy sets", Inform.Control, 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  33. Zadeh, L.A. (1996), "Fuzzy logic computing with words", IEEE T. Fuzzy Syst., 4(2), 103-111. https://doi.org/10.1109/91.493904