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Submitted
Sep 30, 2020
Accepted
Mar 18, 2021
Published
Mar 27, 2021
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Abstract

Gas and oil pipelines have decreased function and damaged due to corrosion. This research aims to analyze and predict the life of gas and oil pipelines within a certain time span. The method used is a reliability study using a normal distribution. The analysis results show it is predicted that the pipe reliability probability in 2030 will decrease and the probability of failure will increase. The probability of reliability is 0.843572786617270 and the probability of failure is 0.156427213382730 in 2030. With the long distance pipeline, maximum depth as shown in the attachment the average thick remain is 0.2200 inches, the average corrotion rate is 0.0317 mm/year, with prediction thick remain from 2000 to 2030 in inches.

Keywords

Normal Distributon Reliability Probability of Failure

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
1.
Suhendi Syafei N, Hidayat D, Rohadi N, Joebaedi K, Supriyana E. Reliability Study of Oil and Gas Pipelines Using the Normal Distribution Method. EKSAKTA [Internet]. 2021Mar.27 [cited 2024Apr.19];22(1):73-81. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/246

References

  1. Mahmoodian, M., & Li, C. Q. (2017). Failure assessment and safe life prediction of corroded oil and gas pipelines. Journal of Petroleum Science and Engineering, 151, 434-438.
  2. Pandey, M. D. (1998). Probabilistic models for condition assessment of oil and gas pipelines. Ndt & E International, 31(5), 349-358.
  3. Cheraghi, N., & Taheri, F. (2007). A damage index for structural health monitoring based on the empirical mode decomposition. Journal of Mechanics of Materials and Structures, 2(1), 43-61.
  4. Zhang, L., Wu, X., Qin, Y., Skibniewski, M. J., & Liu, W. (2016). Towards a fuzzy Bayesian network based approach for safety risk analysis of tunnel‐induced pipeline damage. Risk Analysis, 36(2), 278-301.
  5. Montiel, H., Vı́lchez, J. A., Casal, J., & Arnaldos, J. (1998). Mathematical modelling of accidental gas releases. Journal of Hazardous Materials, 59(2-3), 211-233.
  6. Yoo, D. G., Kang, D., Jun, H., & Kim, J. H. (2014). Rehabilitation priority determination of water pipes based on hydraulic importance. Water, 6(12), 3864-3887.
  7. Ferrante, M., & Brunone, B. (2003). Pipe system diagnosis and leak detection by unsteady-state tests. 2. Wavelet analysis. Advances in Water Resources, 26(1), 107-116.
  8. Amirat, A., Mohamed-Chateauneuf, A., & Chaoui, K. (2006). Reliability assessment of underground pipelines under the combined effect of active corrosion and residual stress. International Journal of Pressure Vessels and Piping, 83(2), 107-117.
  9. Nahal, M., Khelif, R., Bourenane, R., & Salah, S. (2015). Pipelines reliability analysis under corrosion effect and residual stress. Arabian Journal for Science and Engineering, 40(11), 3273-3283.
  10. Zaidun, Yasin.2010. Thesis: Comparative Analysis of Risk Based Assessment Methods With Time Based Assessment Methods at Gas Processing Stations. Jakarta. UI.
  11. Varela, F., Yongjun Tan, M., & Forsyth, M. (2015). An overview of major methods for inspecting and monitoring external corrosion of on-shore transportation pipelines. Corrosion Engineering, Science and Technology, 50(3), 226-235.
  12. Race, J. M., Dawson, S. J., Stanley, L. M., & Kariyawasam, S. (2007). Development of a predictive model for pipeline external corrosion rates. In Pipeline Pigging and Integrity Management Conference. Newcastle University.
  13. Popang, Oridian. 2011.Thesis Risk Assessment of Unburried Subsea Pipeline on Trawl Gear with Hooking Condition. Final Project, Department of Ocean Engineering-FTK, Sepuluh Nopember Institute of Technology, Surabaya
  14. Elsayed, A.E. 1996. Reliability Engineering. Massachusets: Addison Wesley Longman, Inc.
  15. Kumar, U.D., Crocker, J., Chitra, T., Saranga, H., 2006. Reliability and Six Sigma, Springer, New York.
  16. Rifda Ilahy Rosihan-Hari Agung Yuniarto, Reliability System Analysis with Reliability Block Diagram Approach, volume 9, no.1, 22 December 2019, Pages 1-85, JOURNAL OF TECHNOSAINS (JURNAL TEKNOSAINS).
  17. Distefano, S., & Xing, L. (2006, January). A new approach to modeling the system reliability: dynamic reliability block diagrams. In RAMS'06. Annual Reliability and Maintainability Symposium, 2006. (pp. 189-195). IEEE.
  18. Wang, W., Loman, J. M., Arno, R. G., Vassiliou, P., Furlong, E. R., & Ogden, D. (2004). Reliability block diagram simulation techniques applied to the IEEE std. 493 standard network. IEEE Transactions on Industry Applications, 40(3), 887-895.
  19. Xu, H., Xing, L., & Robidoux, R. (2009). Drbd: Dynamic reliability block diagrams for system reliability modelling. International Journal of Computers and Applications, 31(2), 132-141.
  20. Robidoux, R., Xu, H., Xing, L., & Zhou, M. (2009). Automated modeling of dynamic reliability block diagrams using colored Petri nets. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(2), 337-351.
  21. Distefano, S., & Puliafito, A. (2007, January). Dynamic reliability block diagrams vs dynamic fault trees. In 2007 Annual Reliability and Maintainability Symposium (pp. 71-76). IEEE.
  22. Roczen, B., Arno, R. G., & Hale, P. S. (2004, May). Reliability block diagram methodology applied to gold book standard network. In Conference, 2004 IEEE Industrial and Commercial Power Systems Technical (pp. 116-126). IEEE.
  23. Gough, W. S., Riley, J., & Koren, J. M. (1990, January). A new approach to the analysis of reliability block diagrams. In Annual Proceedings on Reliability and Maintainability Symposium (pp. 456-464). IEEE.