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http://dx.doi.org/10.3744/SNAK.2018.55.2.93

Probabilistic Risk Analysis of Dropped Objects for Corroded Subsea Pipelines  

Kumar, Ankush (Department of Naval Architecture and Ocean Engineering, Pusan National University)
Seo, Jung Kwan (The Korea Ship and Offshore Research Institute, Pusan National University)
Publication Information
Journal of the Society of Naval Architects of Korea / v.55, no.2, 2018 , pp. 93-102 More about this Journal
Abstract
Quantitative Risk Assessment (QRA) has been used in shipping and offshore industries for many years, supporting the decision-making process to guarantee safe running at different stages of design, fabrication and throughout service life. The assessments of a risk perspective are informed by the frequency of events (probability) and the associated consequences. As the number of offshore platforms increases, so does the length of subsea pipelines, thus there is a need to extend this approach and enable the subsea industry to place more emphasis on uncertainties. On-board operations can lead to objects being dropped on subsea pipelines, which can cause leaks and other pipeline damage. This study explains how to conduct hit frequency analyses of subsea pipelines, using historical data, and how to obtain a finite number of scenarios for the consequences analysis. An example study using probabilistic methods is used.
Keywords
Risk analysis; Frequency analysis; Dropped objects; Corroded subsea pipeline; Probabilistic approach;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Ma, L., Li, Y., Liang, L., Li, M. & Cheng, L., 2013. A novel method of quantitative risk assessment based on grid difference of pipeline sections. Safety Science, 59, pp. 219-226.   DOI
2 Milazzo, M.F. & Aven, T., 2012. An extended risk assessment approach for chemical plants applied to a related to pipe ruptures. Reliability Engineering & System Safety, 99, pp.183-192.   DOI
3 Mohd, M.H., Kim, D.K., Kim, D.W. & Paik, J.K., 2014a. A time-variant corrosion wastage model for subsea gas pipelines, Ships and Offshore Structures, 9(2), pp. 161-176.   DOI
4 Mohd, M.H., Kim, D.K., Lee, B.J., Kim, D.K., Seo, J.K. & Paik, J.K., 2014b. On the burst strength capacity of an aging subsea gas pipeline. Journal Offshore Mechanics and Arctic Engineering, 136(4), pp.1-7.
5 Niedzwecki, J.M. & Bai, Y., 2014. Modeling deepwater seabed steady-state thermal fields around buried pipeline including trenching and backfill effects. Computers and Geotechnics, 61, pp.221-229.   DOI
6 OGP, 2010. No. 432: Risk Assessments data directory. International Association of Oil and Gas Producers (OGP): UK.
7 PETRONAS. 2011. Pipeline inspection report. Kuala Lumpur: Petroliam Nasional Berhad (PETRONAS).
8 Ross, S.M., 2009. Introduction to probability and statistics for engineers and scientists. Academic Press: MA, USA.
9 Seo, J.K., Cui, Y., Mohd. M.H., Ha, Y.C., Kim, B.J. & Paik, JK., 2015. A risk-based inspection planning method for corroded subsea pipelines. Ocean Engineering, 109(15), pp.539-552.   DOI
10 Seo, J.K., Lee, S.E. & Park, J.S., 2017. A method for determining fire accidental loads and its application to thermal response analysis for optimal design of offshore thin-walled structures. Fire Safety Journal, 92, pp.107-121.   DOI
11 Aynbinder, A., 1997. New method determines effect of concrete coating on pipe-collapse pressure. Oil and Gas Journal, 95(43), pp.57-63.
12 Azevedo, C.R.F., 2007. Failure analysis of a crude oil pipeline. Engineering Failure Analysis, 14(6), pp.978-994.   DOI
13 Bai, Y. & Bai, Q., 2005. Subsea pipelines and risers. Elsevier: MA, USA.
14 Bai, Y. & Bai, Q., 2014. Subsea pipeline integrity and risk management. Gulf Professional Publishing: MA, USA.
15 Brito, A.J. & Almeida, A.T., 2009. Multi-attribute risk assessment for risk ranking of natural gas pipelines. Reliability Engineering & System Safety, 94(2), 187-198.   DOI
16 Crawley, F.K., Lines, I.G. & Mather, J., 2003. Oil and gas pipeline failure modelling. Process Safety and Environmental Protection, 81(1), pp.3-11.   DOI
17 Cui, Y., Kim, D.W., Seo, J.K., Ha, Y.C., Kim, B.J. & Paik, J.K., 2015. Serviceability assessment of corroded subsea crude oil pipelines. Journal of the Society of Naval Architects of Korea, 52(2), pp.153-160.   DOI
18 Det Norske Veritas (DNV), 2010. DNV-RP-F107: Risk assessment of pipeline protection. Det Norske Veritas: oslo, Norway.
19 Han, Z.Y. & Weng, W.G., 2011. Comparison study on qualitative and quantitative risk assessment methods for urban natural gas pipeline network. Journal of Hazardous Materials, 189(1), pp.509-518.   DOI
20 Ilman, M.N., 2014. Analysis of internal corrosion in subsea oil pipeline. Case Studies in Engineering Failure Analysis, 2(1), pp.1-8.   DOI
21 International Organization for Standardization (ISO), 2015. ISO 13702:2015: Petroleum and natural gas industries. International Organization for Standardization: Geneva, Switzerland.
22 Koornneef, J., Spruijt, M., Molag, M., Ramirez, A., Turkenburg, W. & Faaij, A., 2010. Quantitative risk assessment of $CO_2$ transport by pipelines-a review of uncertainties and their impacts. Journal of hazardous materials, 177(1), pp.12-27.   DOI
23 Lee, G.H., Seo, J.K. & Paik, J.K. 2017. Condition assessment of damaged elbow in subsea pipelines. Ships and Offshore Structures, 12(1), pp.135-151.   DOI
24 Woo, S., Lee, K. & Choung, J., 2017. Design of subsea manifold protective structure against dropped object impacts. Journal of Ocean Engineering and Technology 31(3), pp.233-240.   DOI
25 Shanbi, P. & Zhaoxiong, Z., 2015. An experimental study on the internal corrosion of a subsea multiphase pipeline. Petroleum, 1(1), pp.75-81.   DOI
26 Teixeira, A.P., Soares, C.G., Netto, T.A. & Estefen, S.F., 2008. Reliability of pipelines with corrosion defects. International Journal of Pressure Vessels and Piping, 85(4), pp.228-237.   DOI
27 Vinnem, J.E., 2013. Offshore risk assessment: principles, modelling and applications of QRA studies. Springer Science & Business Media: The Netherlands.
28 Ye, K.Q., 1998. Orthogonal column Latin hypercubes and their application in computer experiments. Journal of the American Statistical Association, 93(444), pp. 1430-1439.   DOI