Quantitative risk assessment & leak detection criteria for a subsea oil export pipeline

Fang-yuan Zhang , Yong Bai , Mohd Fauzi Badaruddin , Suhartodjo Tuty

Journal of Marine Science and Application ›› 2009, Vol. 8 ›› Issue (2) : 168 -174.

PDF
Journal of Marine Science and Application ›› 2009, Vol. 8 ›› Issue (2) : 168 -174. DOI: 10.1007/s11804-009-8116-y
Article

Quantitative risk assessment & leak detection criteria for a subsea oil export pipeline

Author information +
History +
PDF

Abstract

A quantitative risk assessment (QRA) based on leak detection criteria (LDC) for the design of a proposed subsea oil export pipeline is presented in this paper. The objective of this QRA/LDC study was to determine if current leak detection methodologies were sufficient, based on QRA results, while excluding the use of statistical leak detection; if not, an appropriate LDC for the leak detection system would need to be established. The famous UK PARLOC database was used for the calculation of pipeline failure rates, and the software POSVCM from MMS was used for oil spill simulations. QRA results revealed that the installation of a statistically based leak detection system (LDS) can significantly reduce time to leak detection, thereby mitigating the consequences of leakage. A sound LDC has been defined based on QRA study results and comments from various LDS vendors to assist the emergency response team (ERT) to quickly identify and locate leakage and employ the most effective measures to contain damage.

Keywords

QRA (quantitative risk assessment) / risk / LDC (leak detection criteria) / PARLOC database / pipeline

Cite this article

Download citation ▾
Fang-yuan Zhang, Yong Bai, Mohd Fauzi Badaruddin, Suhartodjo Tuty. Quantitative risk assessment & leak detection criteria for a subsea oil export pipeline. Journal of Marine Science and Application, 2009, 8(2): 168-174 DOI:10.1007/s11804-009-8116-y

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Bai Y., Bai Q. Subsea pipelines & risers[M]. 2005, Holland: Elsevier Science Ltd, 751-784

[2]

UK PARLOC Database 2001[DB]. Mott MacDonald Ltd, HSE, UKOOA, and IP, 2003 rev. 5.

[3]

REED M, DITLEVSEN M K, HETLAND B, et al. User manual-MMS-POSVCM, Project NO. 661247[R].

[4]

Design and engineering practice. DEP 31.40.60.11[J]. Pipeline Leak Detection, 2002(9).

[5]

API 1130-2002: “Computational Pipeline Monitoring for Liquid Pipelines”, 2nd Ed [R]. 2009) 8: 175–181

AI Summary AI Mindmap
PDF

145

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/