Ocean Systems Engineering

  • 제12권3호
  • /
  • Pages.267-284
  • /
  • 2022
  • /
  • 2093-6702(pISSN)
  • /
  • 2093-677X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Vessel traffic geometric probability approaches with AIS data in active shipping lane for subsea pipeline quantitative risk assessment against third-party impact

  • 투고 : 2022.07.07
  • 심사 : 2022.09.13
  • 발행 : 2022.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

A subsea pipeline designed across active shipping lane prones to failure against external interferences such as anchorage activities, hence risk assessment is essential. It requires quantifying the geometric probability derived from ship traffic distribution based on Automatic Identification System (AIS) data. The actual probability density function from historical vessel traffic data is ideal, as for rapid assessment, conceptual study, when the AIS data is scarce or when the local vessels traffic are not utilised with AIS. Recommended practices suggest the probability distribution is assumed as a single peak Gaussian. This study compares several fitted Gaussian distributions and Monte Carlo simulation based on actual ship traffic data in main ship direction in an active shipping lane across a subsea pipeline. The results shows that a Gaussian distribution with five peaks is required to represent the ship traffic data, providing an error of 0.23%, while a single peak Gaussian distribution and the Monte Carlo simulation with one hundred million realisation provide an error of 1.32% and 0.79% respectively. Thus, it can be concluded that the multi-peak Gaussian distribution can represent the actual ship traffic distribution in the main direction, but it is less representative for ship traffic distribution in other direction. The geometric probability is utilised in a quantitative risk assessment (QRA) for subsea pipeline against vessel anchor dropping and dragging and vessel sinking.

키워드

과제정보

This work acknowledges the support from Research, Community Service and Innovation Program, Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Indonesia.

참고문헌

  1. ABS (2013), "Rules for Building and Classing Steel Vessels Part 3 Chapter 5", American Bureau of Shipping.
  2. Aulia, R., Tan, H. and Sriramula, S. (2021), "Dynamic reliability model for subsea pipeline risk assessment due to third-party interference", J. Pipeline Sci. Eng., 1(3), 277-289. https://doi.org/10.1016/j.jpse.2021.09.006.
  3. Bartolini, L., Marchionni, L., Parrella, A. and Vitali, L. (2018), "Advanced FE modelling approach for pipeline hooking interaction of dragged anchors", Proceedings of the ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering, Madrid, Spain, June.
  4. Brown, R.J. (1984), "Offshore Pipeline Repair Methods and Costs in Diapir Area, Alaska", Zug Houston The Hague Singapore.
  5. Chung, Y.N., Man, C.L., Juan, C.H. and Hsin, C.K. (2019), "Risk assessment and traffic behaviour evaluation of inbound ships in Keelung Harbour based on AIS data", J. Mar. Sci. Technol., 27. https://doi.org/10.6119/JMST.201908_27(4).0002.
  6. De Stefani, V. and Carr, P. (2011), "A model to estimate the failure rates of offshore pipelines", Proceedings of the 2010 8th International Pipeline Conference, Alberta, Canada, September.
  7. DNV (2010), 'DNV RP F-111 Interference Between Trawl Gear and Pipelines'. Det Norske Veritas.
  8. DNVGL (2017), 'DNVGL-RP-F107 Risk Assessment of Pipeline Protection'. Det Norske Veritas.
  9. Hu, Z., Liu, Y., Li, Z. and Liu, Y. (2021), "Impact frequencies of subsea pipelines by anchoring and trawling based on AIS data", Proceedings of the 31st International Ocean and Polar Engineering Conference, Rhodes, Greece, June.
  10. Huang, J.C., Nieh, C.Y. and Kuo, H.C. (2019), "Risk assessment of ships maneuvering in an approaching channel based on AIS data", Ocean Eng., 173, 399-414. https://doi.org/10.1016/j.oceaneng.2018.12.058.
  11. Idris, H. and Ramli, M.F. (2018), "Southeast Asian region maritime connectivity and the potential development of the Northern Sea route for commercial shipping", JATI - J. Southeast Asian Studies, 23(2), 25-46. https://doi.org/10.22452/jati.vol23no2.2.
  12. Macdonald, K.A., Cosham, A., Alexander, C.R. and Hopkins, P. (2007), "Assessing mechanical damage in offshore pipelines - Two case studies", Eng. Fail. Anal., 14(8), 1667-1679. https://doi.org/10.1016/j.engfailanal.2006.11.074.
  13. Marcjan, K., Dzikowski, R. and Bilewski, M. (2017), "Criteria of accidental damage by ships anchors of subsea gas pipeline in the Gdansk bay area", TransNav, Int. J. Marine Navigation and Safety od Sea Transportation, 11(3), 441-446. https://doi.org/10.12716/1001.11.03.07.
  14. Mujeeb-Ahmed, M.P., Seo, J.K. and Paik, J.K. (2018), "Probabilistic approach for collision risk analysis of powered vessel with offshore platforms", Ocean Eng., 151, 206-221. https://doi.org/10.1016/j.oceaneng.2018.01.008.
  15. Mulyadi, Y., Kobayashi, E., Wakabayashi, N., Pitana, T. and Wahyudi. (2014a), "Development of ship sinking frequency model over subsea pipeline for Madura Strait using AIS data", WMU J. Maritime Affairs, 13(1), 43-59. https://doi.org/10.1007/s13437-013-0049-2.
  16. Mulyadi, Y., Kobayashi, E., Wakabayashi, N., Pitana, T., Wahyudi and Prasetyo, E. (2014b), "Estimation method for dragged anchor accident frequency on subsea pipelines in busy port areas", J. Japan Soc. Naval Archit. Ocean Engineers, 20, 173-183. https://doi.org/10.2534/jjasnaoe.20.173.
  17. Ponte, G.P.D. (2021), Risk Management in The Oil and Gas Industry. Gulf Professional Publishing.
  18. Spouge, J. (1999), A Guide to Quantitative Risk Assessment for Offshore Installations. CMPT Aberdeen, UK.
  19. Svanberg, M., Santen, V., Horteborn, A., Holm, H. and Finnsgard, C. (2019), "AIS in maritime research", Marine Policy, 106, 103520. https://doi.org/10.1016/j.marpol.2019.103520.
  20. Tawekal, R.L., Allo, R.P.R. and Taufik, A. (2017), "Damage analysis of subsea pipeline due to anchor drag", Int. J. Appl. Eng. Res., 12(5).
  21. Tawekal, R.L. and De Velas, J. (2019), "Subsea pipeline protection design subjected to dropped anchor using concrete mattress", GEOMATE J., 17(60), 251-258. https://doi.org/10.21660/2019.60.84652.
  22. Trelleborg (2003), Marine Fendering Systems: Ship Features & Tables, Trelleborg.
  23. U.S. Energy Information Admisnistration (2013), "The South China Sea is an important world energy trade route", U.S. Energy Information Admisnistration. https://www.eia.gov/todayinenergy/detail.php?id=10671.
  24. Vitali, L., Candiracci, F., Crea, C., Bruschi, R. and Rott, W. (2012), "Nord stream project - Pipeline safety against ship traffic related threats: Quantitative risk assessment approach", Proceedings of the 22nd International Offshore and Polar Engineering Conference, Rhodes, Greece, June.
  25. Wang, L., Li, Y., Wan, Z., Yang, Z., Wang, T., Guan, K. and Fu, L. (2020), "Use of AIS data for performance evaluation of ship traffic with speed control", Ocean Eng. 204, 107259. https://doi.org/10.1016/j.oceaneng.2020.107259.
  26. Woo, J., Kim, D. and Na, W.B. (2015), "Damage assessment of a tunnel-type structure to protect submarine power cables during anchor collisions", Mar. Struct., 44, 19-42. https://doi.org/10.1016/j.marstruc.2015.07.005.
  27. Yoo, Y. and Kim, T.G. (2019), "An improved ship collision risk evaluation method for Korea maritime safety audit considering traffic flow characteristics", J. Mar. Sci. Eng., 7(12), 448. https://doi.org/10.3390/jmse7120448.
  28. Zhen, R., Riveiro, M. and Jin, Y. (2017), "A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance", Ocean Eng. 145, 492-501. https://doi.org/10.1016/j.oceaneng.2017.09.015.