Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions (original) (raw)

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.

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References (66)

  1. Gudmestad OT, Karunakaran D. Challenges Faced by the Marine Contractors Working in Western and Southern Barents Sea. OTC Arctic Technology Conference. 3-5 December, Houston, Texas, USA2012.
  2. Løset S, Shkhinek K, Gudmestad OT, Strass P, Michalenko E, Frederking R, et al. Comparison of the physical environment of some Arctic seas. Cold Regions Science and Technology. 1999;29:201-14.
  3. Homlong E, Kayrbekova D, Panesar SS, Markeset T. Assessing Maintenance Time, Cost and Uncertainty for Offshore Production Facilities in Arctic Environment. In: Frick J, Laugen BT, editors. Advances in Production Management Systems -Value Networks: Innovation, Technologies, and Management: Springer; 2012.
  4. Naseri M, Barabady J. Offshore drilling activities in Barents Sea: Challenges and considerations. Proceedings of the 22nd International Conference on Port and Ocean Engineering under Arctic Conditions (POAC). June 9-13, Espoo, Finland2013.
  5. Jardine A, Anderson P, Mann D. Application of the Weibull proportional hazards model to aircraft and marine engine failure data. Quality and reliability engineering international. 1987;3:77-82.
  6. Doyen L, Gaudoin O. Classes of imperfect repair models based on reduction of failure intensity or virtual age. Reliability Engineering & System Safety. 2004;84:45-56.
  7. Kumar D, Klefsjö B. Proportional hazards model: a review. Reliability Engineering & System Safety. 1994;44:177-88.
  8. Dale CJ. Application of the proportional hazards model in the reliability field. Reliability Engineering. 1985;10:1-14.
  9. Ansell JI, Philipps MJ. Practical aspects of modelling of repairable systems data using proportional hazards models. Reliability Engineering & System Safety. 1997;58:165-71.
  10. Martorell S, Sanchez A, Serradell V. Age-dependent reliability model considering effects of maintenance and working conditions. Reliability Engineering & System Safety. 1999;64:19-31.
  11. Vesely WE, Wolford AJ. Risk evaluations of aging phenomena: The linear aging reliability model and its extensions. Nuclear Engineering and Design. 1988;108:179-85.
  12. Martón I, Sánchez AI, Martorell S. Ageing PSA incorporating effectiveness of maintenance and testing. Reliability Engineering & System Safety. 2015;139:131-40.
  13. Artiba A, Riane F, Ghodrati B, Kumar U. Reliability and operating environment-based spare parts estimation approach: a case study in Kiruna Mine, Sweden. Journal of Quality in Maintenance Engineering. 2005;11:169-84.
  14. Gao X, Barabady J, Markeset T. An approach for prediction of petroleum production facility performance considering Arctic influence factors. Reliability Engineering & System Safety. 2010;95:837-46.
  15. Tian L, Zucker D, Wei LJ. On the Cox Model With Time-Varying Regression Coefficients. Journal of the American Statistical Association. 2005;100:172-83.
  16. Peng L, Huang Y. Survival Analysis with Temporal Covariate Effects. Biometrika. 2007;49:719-33.
  17. Rocchetta R, Li YF, Zio E. Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions. Reliability Engineering & System Safety. 2015;136:47-61.
  18. Alvehag K, Soder L. A stochastic weather dependent reliability model for distribution systems. Proceedings of the 10th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS08). May 25-29, Rincón, Puerto Rico: IEEE; 2008. p. 1-8.
  19. Alvehag K, Soder L. A reliability model for distribution systems incorporating seasonal variations in severe weather. IEEE Transactions on Power Delivery. 2011;26:910-9.
  20. Barabadi A, Barabady J, Markeset T. Maintainability analysis considering time- dependent and time-independent covariates. Reliability Engineering & System Safety. 2011;96:210-7.
  21. Barabadi A, Barabady J, Markeset T. Application of reliability models with covariates in spare part prediction and optimization -A case study. Reliability Engineering & System Safety. 2014;123:1-7.
  22. Kayrbekova D, Barabadi A, Markeset T. Maintenance cost evaluation of a system to be used in Arctic conditions: a case study. Journal of Quality in Maintenance Engineering. 2011;17:320-36.
  23. Barabadi A, Gudmestad OT, Barabady J. RAMS data collection under Arctic conditions. Reliability Engineering & System Safety. 2015;135:92-9.
  24. Baraldi P, Compare M, Despujols A, Zio E. Modelling the effects of maintenance on the degradation of a water-feeding turbo-pump of a nuclear power plant. Journal of Risk and Reliability. 2011;225:169-83.
  25. Baraldi P, Zio E, Compare M, Rossetti G, Despujols A. A novel approach to model the degradation of components in electrical production plants. In: Bris R, Soares CG, Martorell S, editors. Proceedings of 18th European Safety and Reliability Conference. Prague CRC Press; 2009.
  26. Naseri M, Barabady J. Expert-Based Reliability Modelling and Analysis of Arctic Oil and Gas Production Plants: Accounting for the Effects of Harsh Weather Conditions. Submitted to a journal for publication. 2015.
  27. Department of Defense. Military Handbook MIL-HDBK-217F -Reliability Predition of Electronic Equipment. Washington D.C.: Department of Defense; 1991.
  28. Kijima M. Some results for repairable systems with general repair. Journal of Applied probability. 1989;26:89-102.
  29. Baraldi P, Balestrero A, Compare M, Benetrix L, Despujols A, Zio E. A modeling framework for maintenance optimization of electrical components based on fuzzy logic and effective age. Quality and Reliability Engineering International. 2013;29:385-405.
  30. XiaoFei L, Min L. Hazard rate function in dynamic environment. Reliability Engineering & System Safety. 2014;130:50-60.
  31. Naseri M, Barabady J. An expert-based approach to production performance analysis of oil and gas facilities considering time-independent Arctic operating conditions. International Journal of System Assurance Engineering and Management. 2016:1-15.
  32. Murthy DNP, Xie M, Jiang R. Weibull Models. New Jersey: John Wiley & Sons; 2004.
  33. Rausand M, Høyland A. System reliability theory: models, statistical methods, and applications: John Wiley & Sons; 2004.
  34. Mannan S. Lees' Process Safety Essentials: Hazard Identification, Assessment and Control. Oxford: Butterworth-Heinemann; 2014.
  35. Zio E. The Monte Carlo Simulation Method for System Reliability and Risk Analysis. London: Springer; 2013.
  36. Elsayed EA. Reliability Engineering. 2nd ed. Hoboken: John Wiely & Sons; 2012.
  37. Bagdonavicius V, Nikulin M. Accelerated life models: modeling and statistical analysis: CRC press; 2001.
  38. Zio E. An introduction to the basics of reliability and risk analysis: World scientific; 2007.
  39. Cox DR. Regression Models and Life-Tables. Journal of the Royal Statistical Society. 1972;34:187-220.
  40. Dubi A. Monte Carlo Applications in Systems Engineering. Chichester:: Wiley; 2000.
  41. Dubi A. Analytic approach & Monte Carlo methods for realistic system analysis. Mathematics and Computers in Simulation. 1998;47:243-69.
  42. Siu N. Risk assessment for dynamic systems: An overview. Reliability Engineering & System Safety. 1994;43:43-73.
  43. OREDA Participants. Offshore Reliability Data Handbook 5th ed. Trondhim: OREDA Participants; 2009.
  44. Obanijesu EO, Akindeju MK, Vishnu P, Tade MO. Modelling the Natural Gas Pipeline Internal Corrosion Rate Resulting from Hydrate Formation. In: Pistikopoulos EN, Georgiadis MC, Kokossis AC, editors. Computer Aided Chemical Engineering: Elsevier; 2011. p. 1160-4.
  45. Dutta PK. Behaviour of materials at cold regions temperatures -Part 1: Program rationale and test plan. New Hampshire: US Army Engineer Research and Development Centre; 1988.
  46. Rudin A, Choi P. Chapter 4 -Mechanical Properties of Polymer Solids and Liquids. In: Rudin A, Choi P, editors. The Elements of Polymer Science & Engineering (Third Edition). Boston: Academic Press; 2013. p. 149-229.
  47. Stachowiak GW, Batchelor AW. 2 -Physical Properties of Lubricants. In: Stachowiak GW, Batchelor AW, editors. Engineering Tribology (Third Edition). Burlington: Butterworth-Heinemann; 2006. p. 11-50.
  48. ISO. ISO 19906: Petroleum and Natural Gas Industries -Arctic Offshore Structures. Geneva: ISO; 2010.
  49. Pilcher JJ, Nadler E, Busch C. Effects of hot and cold temperature exposure on performance: a meta-analytic review. Ergonomics. 2002;45:682-98.
  50. Šaltytė Benth J, Benth FE. A critical view on temperature modelling for application in weather derivatives markets. Energy Economics. 2012;34:592-602.
  51. Wakaura M, Ogata Y. A time series analysis on the seasonality of air temperature anomalies. Meteorological Applications. 2007;14:425-34.
  52. Benth Jš, Benth FE, Jalinskas P. A Spatial-temporal Model for Temperature with Seasonal Variance. Journal of Applied Statistics. 2007;34:823-41.
  53. Alexandridis AK, Zapranis AD. Weather derivatives -Modeling and Pricing Weather- Related Risk. New York: Springer; 2013.
  54. Taib CMIC, Benth FE. Pricing of temperature index insurance. Review of Development Finance. 2012;2:22-31.
  55. Osczevski R, Bluestein M. The new wind chill equivalent temperature chart. Bulletin of the American Meteorological Society. 2005;86:1453-8.
  56. Bluestein M, Quayle R. Wind Chill. In: Holton JR, Curry JA, Pyle JA, editors. Encyclopedia of Atmospheric Sciences. Oxford: Academic Press; 2003. p. 2597-602.
  57. Benth FE, Šaltytė Benth J. Dynamic pricing of wind futures. Energy Economics. 2009;31:16-24.
  58. Benth JŠ, Benth FE. Analysis and modelling of wind speed in New York. Journal of Applied Statistics. 2010;37:893-909.
  59. Caporin M, Preś J. Modelling and forecasting wind speed intensity for weather risk management. Computational Statistics & Data Analysis. 2012;56:3459-76.
  60. Department of Defense. Military Standard MIL-STD-721C -Definitions of terms for reliability and maintainability. Washington, DC: Department of Defense; 1981.
  61. Jardine AK, Tsang AH. Maintenance, replacement, and reliability: theory and applications. Boca Raton: CRC press; 2013.
  62. Zio E, Compare M. Evaluating maintenance policies by quantitative modeling and analysis. Reliability Engineering & System Safety. 2013;109:53-65.
  63. Riane F, Roux O, Basile O, Dehombreux P. Simulation Based Approaches for Maintenance Strategies Optimization. In: Ben-Daya M, Duffuaa SO, Raouf A, Knezevic J, Ait- Kadi D, editors. Handbook of Maintenance Management and Engineering. London: Elsevier; 2009. p. 133-53.
  64. Pierskalla WP, Voelker JA. A survey of maintenance models: the control and surveillance of deteriorating systems. Naval Research Logistics Quarterly. 1976;23:353-88.
  65. Zio E, Aven T. Industrial disasters: Extreme events, extremely rare. Some reflections on the treatment of uncertainties in the assessment of the associated risks. Process Safety and Environmental Protection. 2013;91:31-45.
  66. Aven T, Zio E, Baraldi P, Flage R. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. West Sussex: John Wiley & Sons; 2014.