Abstract
The availability and throughput of offshore oil and gas plants operating in the Arctic are adversely influenced by the harsh environmental conditions. One of the major challenges in quantifying such effects is lack of adequate life data. The data collected in normal-climate regions cannot effectively reflect the negative effects of harsh Arctic operating conditions on the reliability, availability, and maintainability performance of the facilities. Expert opinions, however, can modify such data. In an analogy with proportional hazard models, this paper develops an expert-based availability model to analyse the performance of the plants operating in the Arctic, while accounting for the uncertainties associated with expert judgements. The presented model takes into account waiting downtimes and those related to extended active repair times, as well as the impacts of operating conditions on components’ reliability. The model is illustrated by analysing the availability and throughput of the power generation unit of an offshore platform operating in the western Barents Sea.
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Abbreviations
- CDF:
-
Cumulative distribution function
- DM:
-
Decision maker
- FTTF:
-
First time to failure
- GEN:
-
Generator
- GT:
-
Gas turbine
- MC:
-
Monte Carlo
- MTTF:
-
Mean time to failure
- MTTR:
-
Mean time to repair
- O&G:
-
Oil and gas
- ORDA:
-
Offshore reliability data
- PDF:
-
Probability density function
- PHM:
-
Proportional hazard model
- RAM:
-
Reliability, availability, and maintainability
- TR:
-
Train
- TTF:
-
Time to failure
- TTR:
-
Time to repair
- E :
-
Degree of increase in MTTR of a component operating in an Arctic location. In other words, component MTTR increases by a factor of (1 + E). In E i , subscript i refers to component i
- E′:
-
Time-independent factor by which component active repair rate is decreased due to the effects of Arctic operating conditions on maintenance crew performance
- \(F_{i,E}^{DM} (\varepsilon )\) :
-
Decision maker’s CDFs of random variables E (i.e., the degree of increase in a component’s MTTR) corresponding to component i
- \(F_{{i,\Delta }}^{DM} (\delta )\) :
-
Decision maker’s CDF of random variable Δ (i.e., the degree of reduction in a component’s MTTF) corresponding to component i
- \(F_{TDT}^{(A)} (t)\) :
-
The CDF of total downtimes, including active repair times and waiting downtimes, corresponding to a component, whose repair is performed under Arctic operating environment
- \(F_{TTF}^{(B)} (t)\) :
-
Failure probability function of a component operating in the base area. In \(F_{TTF}^{(A)} (t)\), superscript A refers to the Arctic
- \(F_{TTR}^{(A)} (t)\) :
-
CDF of active TTRs of a component in the Arctic offshore
- \(F_{WDT} (t)\) :
-
CDF of waiting downtimes
- \(F_{\varPhi }^{DM} (\psi )\) :
-
Decision maker’s CDF of unknown random variable Φ
- \(F_{\varPhi }^{j} (\psi )\) :
-
Expert j’s CDF of unknown random variable Φ
- m :
-
Mean of the natural logarithm of WDTs
- m :
-
Vector of the means of normal distributions fitted to experts’ data
- m′:
-
Mean of the lognormal distribution of WDTs, \(F_{WDT} (t)\)
- m j :
-
Mean of the normal distribution fitted to the data given by expert j
- m DM :
-
Mean of DM’s distribution obtained by Bayesian aggregation of experts’ distributions
- MTTF B :
-
Mean time to failure of a component operating in the base area. In MTTF A , subscript A refers to the Arctic
- MTTR B :
-
MTTR of a component operating in the base area. In MTTR A , subscript A refers to the Arctic
- N C :
-
Total number of system components
- N e :
-
Total number of experts
- N 1 s :
-
Number of required samples drawn from DM’s CDFs \(F_{{i,\Delta }}^{DM} (\delta )\) and \(F_{i,E}^{DM} (\varepsilon )\) to effectively represent uncertainties in system availability and throughput results
- N 2 s :
-
Number of required samples drawn from waiting downtime and active repair distributions to form the distribution of total downtime
- PGS s :
-
Power generation scenario s
- TDT :
-
Total downtime corresponding to each corrective maintenance task, which includes both waiting downtime and active repair time
- TTR :
-
Active time to repair
- w j :
-
Expert j’s weighting factor
- WDT :
-
Waiting downtime corresponding to each corrective maintenance task
- y j :
-
Experience of expert j in years
- β B :
-
Shape parameter of a Weibull failure probability function of a component operating in the base area. In β A , subscript A refers to the Arctic
- Δ :
-
Degree of reduction in MTTF of a component in an Arctic location. In other words, component MTTF reduces by a factor of (1 − Δ). In Δ i , subscript i refers to component i
- Δ′:
-
Time-independent factor by which component failure rate increases due to the effects of operating environment
- ζ 1 :
-
A random number drawn from uniform distribution over (0, 1)
- ζ 2 :
-
A random number drawn from uniform distribution over (0, 1)
- η B :
-
Scale parameter of a Weibull failure probability function of a component operating in the base area. In η A , subscript A refers to the Arctic
- λ B (t):
-
Weibull failure rate of a component operating in the base area. In λ A (t), subscript A refers to the Arctic
- μ B :
-
Active repair rate of a component operating in the base area. μ B refers to the active TTRs and excludes other waiting downtimes. In μ A , subscript A refers to the Arctic
- ρ jk :
-
Correlation coefficient of the data given by experts j and k
- σ :
-
Standard deviation of the natural logarithm of WDTs
- σ′:
-
Standard deviation of the lognormal distribution of WDTs, F WDT (t)
- σ j :
-
Standard deviation of the normal distribution fitted to the data given by expert j
- σ DM :
-
Standard deviation of DM’s distribution obtained by Bayesian aggregation of experts’ distributions
- Σ :
-
Covariance matrix representing the correlation among experts
- \(\{ \varDelta_{ji,5\% } ,\varDelta_{ji,50\% } ,\varDelta_{ji,95\% } \}\) :
-
The 5th, 50th, and 95th quantiles of the degree of reduction in MTTF of component i, given by expert j
- \(\{ E_{ji,5\% } ,E_{ji,50\% } ,E_{ji,95\% } \}\) :
-
The 5th, 50th, and 95th quantiles of the degree of increase in MTTR of component i, given by expert j
References
Ansell JI, Philipps MJ (1997) Practical aspects of modelling of repairable systems data using proportional hazards models. Reliab Eng Syst Saf 58:165–171. doi:10.1016/S0951-8320(97)00026-4
Artiba A, Riane F, Ghodrati B, Kumar U (2005) Reliability and operating environment-based spare parts estimation approach: a case study in Kiruna Mine. Swed J Qual Maint Eng 11:169–184
Barabadi A, Markeset T (2011) Reliability and maintainability performance under Arctic conditions. Int J Syst Assur Eng Manag 2:205–217. doi:10.1007/s13198-011-0071-8
Barabadi A, Barabady J, Markeset T (2011a) Maintainability analysis considering time-dependent and time-independent covariates. Reliab Eng Syst Saf 96:210–217. doi:10.1016/j.ress.2010.08.007
Barabadi A, Barabady J, Markeset T (2011b) A methodology for throughput capacity analysis of a production facility considering environment condition. Reliab Eng Syst Saf 96:1637–1646. doi:10.1016/j.ress.2011.09.001
Barabadi A, Gudmestad OT, Barabady J (2015) RAMS data collection under Arctic conditions. Reliab Eng Syst Saf 135:92–99. doi:10.1016/j.ress.2014.11.008
Bedford T, Cooke R (2001) Probabilistic risk analysis: foundations and methods. Cambridge University Press, Cambridge
Clemen RT, Winkler RL (1999) Combining probability distributions from experts in risk analysis. Risk Anal 19:187–203
Clemen RT, Winkler RL (2007) Aggregating probability distributions. In: Edwards W, Miles RF Jr, Von Winterfeldt D (eds) Advances in decision analysis: from foundations to applications. Cambridge University Press, Cambridge, pp 154–176
Cooke RM (1991) Experts in uncertainty: opinion and subjective probability in science. Oxford University Press, Oxford
Dale CJ (1985) Application of the proportional hazards model in the reliability field. Reliab Eng 10:1–14. doi:10.1016/0143-8174(85)90038-1
Dubi A (2000) Monte Carlo applications in systems engineering. Wiley, Chichester
French S (1985) Group consensus probability distributions: a critical survey. In: Bernardo JM, Groot MHD, Lindley DV, Smith AFM (eds) Bayesian statistics. Elsevier, North Holland, pp 183–201
Gao X, Barabady J, Markeset T (2010) An approach for prediction of petroleum production facility performance considering Arctic influence factors. Reliab Eng Syst Saf 95:837–846. doi:10.1016/j.ress.2010.03.011
Genest C, McConway KJ (1990) Allocating the weights in the linear opinion pool. J Forecast 9:53–73. doi:10.1002/for.3980090106
Genest C, Zidek JV (1986) Combining probability distributions: a critique and an annotated bibliography. Stat Sci 1:114–135
Gudmestad OT, Karunakaran D (2012) Challenges faced by the marine contractors working in western and southern Barents Sea. Paper presented at the OTC Arctic technology conference, Houston, Texas, USA, 3–5 December
ISO (2001) ISO 12494: atmospheric icing of structures. ISO, Geneva
ISO (2010) ISO 19906: petroleum and natural gas industries—Arctic offshore structures. ISO, Geneva
Jardine A, Anderson P, Mann D (1987) Application of the Weibull proportional hazards model to aircraft and marine engine failure data. Qual Reliab Eng Int 3:77–82
Kumar D, Klefsjö B (1994) Proportional hazards model: a review. Reliab Eng Syst Saf 44:177–188. doi:10.1016/0951-8320(94)90010-8
Labeau PE, Zio E (2002) Procedures of Monte Carlo transport simulation for applications in system engineering. Reliab Eng Syst Saf 77:217–228. doi:10.1016/S0951-8320(02)00055-8
Løset S, Shkhinek K, Gudmestad OT, Strass P, Michalenko E, Frederking R, Kärnä T (1999) Comparison of the physical environment of some Arctic seas. Cold Reg Sci Technol 29:201–214. doi:10.1016/S0165-232X(99)00031-2
Mannan S (2014) Lees’ process safety essentials: hazard identification, assessment and control. Butterworth-Heinemann, Oxford. doi:10.1016/B978-1-85617-776-4.00004-X
Meyer MA, Booker JM (1991) Eliciting and analyzing expert judgement—a practical guide. Academic Press, London. doi:10.1137/1.9780898718485
Morris PA (1977) Combining expert judgments: a Bayesian approach. Manag Sci 23:679–693
Mosleh A, Apostolakis G (1986) The assessment of probability distributions from expert opinions with an application to seismic fragility curves. Risk Anal 6:447–461
Mosleh A, Bier VM, Apostolakis G (1987) Methods for the elicitation and use of expert opinion in risk assessment: phase 1, a critical evaluation and directions for future research (NUREG/CR-4962). U.S. Nuclear Regulatory Commission, Washington DC
Murthy DNP, Xie M, Jiang R (2004) Weibull models. Wiley, New Jersey
Naseri M, Barabady J (2013) Offshore drilling activities in Barents Sea: challenges and considerations. Paper presented at the proceedings of the 22nd international conference on port and ocean engineering under Arctic conditions (POAC), Espoo, Finland, 9–13 June
Naseri M, Barabady J (2015) Reliability analysis of Arctic oil and gas production plants: accounting for the effects of harsh weather conditions using expert data. J Offshorr Mech Arct Eng (under review)
OREDA Participants (2009) Offshore reliability data handbook, 5th edn. OREDA Participants, Trondhim
Pilcher JJ, Nadler E, Busch C (2002) Effects of hot and cold temperature exposure on performance: a meta-analytic review. Ergonomics 45:682–698
Podofillini L, Dang VN (2013) A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction. Reliab Eng Syst Saf 117:52–64. doi:10.1016/j.ress.2013.03.015
Pulkkinen U (1993) Methods for combination of expert judgements. Reliab Eng Syst Saf 40:111–118. doi:10.1016/0951-8320(93)90101-4
Rausand M, Høyland A (2004) System reliability theory: models, statistical methods, and applications, vol 396. Wiley, Hoboken
Rufo MJ, Pérez CJ, Martín J (2012) A Bayesian approach to aggregate experts’ initial information. Electron J Stat 6:2362–2382
Stapelberg RF (2009) Handbook of reliability, availability, maintainability and safety in engineering design. Springer, New York
Winkler RL (1981) Combining probability distributions from dependent information sources. Manag Sci 27:479–488
Zio E (2013) The Monte Carlo simulation method for system reliability and risk analysis. Springer, London
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The authors would like to thank the anonymous experts for their participation in this study.
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Naseri, M., Barabady, J. An expert-based approach to production performance analysis of oil and gas facilities considering time-independent Arctic operating conditions. Int J Syst Assur Eng Manag 7, 99–113 (2016). https://doi.org/10.1007/s13198-015-0380-4
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DOI: https://doi.org/10.1007/s13198-015-0380-4