Reliability analysis of subsea blowout preventers with the use of bayesian networks

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 ra...

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Main Author: Ong, Zheng Jie
Other Authors: Dimitrios Konovessis
Format: Final Year Project
Language:English
Published: 2016
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Online Access:http://hdl.handle.net/10356/67196
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-671962023-03-04T18:38:38Z Reliability analysis of subsea blowout preventers with the use of bayesian networks Ong, Zheng Jie Dimitrios Konovessis School of Mechanical and Aerospace Engineering DRNTU::Engineering 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. Bachelor of Engineering (Mechanical Engineering) 2016-05-12T08:26:43Z 2016-05-12T08:26:43Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67196 en Nanyang Technological University 168 p. application/pdf application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Ong, Zheng Jie
Reliability analysis of subsea blowout preventers with the use of bayesian networks
description 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.
author2 Dimitrios Konovessis
author_facet Dimitrios Konovessis
Ong, Zheng Jie
format Final Year Project
author Ong, Zheng Jie
author_sort Ong, Zheng Jie
title Reliability analysis of subsea blowout preventers with the use of bayesian networks
title_short Reliability analysis of subsea blowout preventers with the use of bayesian networks
title_full Reliability analysis of subsea blowout preventers with the use of bayesian networks
title_fullStr Reliability analysis of subsea blowout preventers with the use of bayesian networks
title_full_unstemmed Reliability analysis of subsea blowout preventers with the use of bayesian networks
title_sort reliability analysis of subsea blowout preventers with the use of bayesian networks
publishDate 2016
url http://hdl.handle.net/10356/67196
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