Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/67196
Title: Reliability analysis of subsea blowout preventers with the use of bayesian networks
Authors: Ong, Zheng Jie
Keywords: DRNTU::Engineering
Issue Date: 2016
Abstract: Secondary intervention systems equipped on a blowout preventer could play a crucial role in safeguarding lives on sea. Setting out with an idea to improve operational reliability, the AMF/Deadman system is being analysed in this report. The main idea behind this system is to close the blind shear ram to prevent outflow of hydrocarbon. Analysis using Bayesian network is employed to calculate the chances of its successful operation. Sequence of operation was first translated from a flow chart into a Bayesian network. Influencing factors such as mechanical, hydraulic, electrical & hardware were next brought in. This enables the entire Bayesian network to be established. Lastly, this network is then analysed through qualitative analysis and sensitivity analysis. Result from qualitative analysis shows that the AMF/Deadman system has an 80.08% chance of closing the blind shear ram in an event of a blowout. Therefore, it is generally deemed capable of securing a leaking wellhead. Additionally, sensitivity analysis indicated that electrical factors are the most impactful on operational success, followed by mechanical, while hydraulic & hardware shares the third place. Hence, more attention should be catered for the maintenance of electrical systems and tougher regulations must be set against them.
URI: http://hdl.handle.net/10356/67196
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP Report.pdf
  Restricted Access
5.95 MBAdobe PDFView/Open
Main BN.pdf
  Restricted Access
1.56 MBAdobe PDFView/Open

Page view(s) 50

93
checked on Sep 30, 2020

Download(s) 50

11
checked on Sep 30, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.