Reliability analysis of subsea wellhead system subject to fatigue and degradation during service life
•A systematic reliability analysis method for subsea wellhead systems is proposed.•Multiple risk factors and component degradation are taken into consideration in system reliability modeling.•A multistate transition model of wellhead components is established to analyze system degradation.•The propo...
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| Published in | Reliability engineering & system safety Vol. 239; p. 109494 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.11.2023
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| Subjects | |
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| Abstract | •A systematic reliability analysis method for subsea wellhead systems is proposed.•Multiple risk factors and component degradation are taken into consideration in system reliability modeling.•A multistate transition model of wellhead components is established to analyze system degradation.•The proposed method is validated using a hybrid structural reliability analysis model.
Subsea wellhead systems play a critical role in ensuring the safe exploitation of offshore oil and gas during offshore drilling and production operation stages. However, due to the complexity of the marine environment and operating condition, this kind of structure is prone to the failure caused by cumulative effects of the fatigue and degradation of components. This paper presents a Markov- Monte Carlo-DBN-based approach to comprehensively predict the reliability of subsea wellhead systems and diagnose the underlying root cause during its service life. Multi-factor impacts on system performance are considered for modeling. A multistate transition model of wellhead components is proposed to analyze system degradation. Reliability evaluation of the critical component is performed and verified by employing the Monte Carlo simulation-based method which is also used to address the issue of insufficient data for subsea wellhead fatigue prediction. By embedding multi-factor effects and multistate transition into DBN, the system state can be effectively updated. An example of a subsea wellhead system demonstrates the application of this approach, through which the system reliability during its service life is predicted, and the most important factors that affect the system's reliability and deserve special attention are identified. |
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| AbstractList | •A systematic reliability analysis method for subsea wellhead systems is proposed.•Multiple risk factors and component degradation are taken into consideration in system reliability modeling.•A multistate transition model of wellhead components is established to analyze system degradation.•The proposed method is validated using a hybrid structural reliability analysis model.
Subsea wellhead systems play a critical role in ensuring the safe exploitation of offshore oil and gas during offshore drilling and production operation stages. However, due to the complexity of the marine environment and operating condition, this kind of structure is prone to the failure caused by cumulative effects of the fatigue and degradation of components. This paper presents a Markov- Monte Carlo-DBN-based approach to comprehensively predict the reliability of subsea wellhead systems and diagnose the underlying root cause during its service life. Multi-factor impacts on system performance are considered for modeling. A multistate transition model of wellhead components is proposed to analyze system degradation. Reliability evaluation of the critical component is performed and verified by employing the Monte Carlo simulation-based method which is also used to address the issue of insufficient data for subsea wellhead fatigue prediction. By embedding multi-factor effects and multistate transition into DBN, the system state can be effectively updated. An example of a subsea wellhead system demonstrates the application of this approach, through which the system reliability during its service life is predicted, and the most important factors that affect the system's reliability and deserve special attention are identified. |
| ArticleNumber | 109494 |
| Author | Liu, Yiliu Li, Bin Wu, Shengnan Li, Zhong Zheng, Wenpei Zhang, Qiao Li, Zhandong Zhang, Laibin |
| Author_xml | – sequence: 1 givenname: Shengnan surname: Wu fullname: Wu, Shengnan email: wushengnan@cup.edu.cn organization: College of Safety and Ocean Engineering, China University of Petroleum(Beijing), Beijing, China – sequence: 2 givenname: Qiao surname: Zhang fullname: Zhang, Qiao organization: College of Safety and Ocean Engineering, China University of Petroleum(Beijing), Beijing, China – sequence: 3 givenname: Bin surname: Li fullname: Li, Bin organization: College of Safety and Ocean Engineering, China University of Petroleum(Beijing), Beijing, China – sequence: 4 givenname: Laibin surname: Zhang fullname: Zhang, Laibin organization: College of Safety and Ocean Engineering, China University of Petroleum(Beijing), Beijing, China – sequence: 5 givenname: Wenpei surname: Zheng fullname: Zheng, Wenpei organization: College of Safety and Ocean Engineering, China University of Petroleum(Beijing), Beijing, China – sequence: 6 givenname: Zhong surname: Li fullname: Li, Zhong organization: Beijing Research Center, CNOOC (China) Co., Ltd., Beijing, China – sequence: 7 givenname: Zhandong surname: Li fullname: Li, Zhandong organization: Sanya Institute of Offshore Oil and Gas Research, Northeastern Petroleum University, Sanya, China – sequence: 8 givenname: Yiliu orcidid: 0000-0002-0612-2231 surname: Liu fullname: Liu, Yiliu organization: Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway |
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| Keywords | HPH BOP MP SW LPH CPT Markov- Monte Carlo -Dynamic Bayesian networks PGB BN Multistate transition model Degradation DBN RoV Subsea wellhead system Reliability analysis MCS |
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| SubjectTerms | Degradation Markov- Monte Carlo -Dynamic Bayesian networks Multistate transition model Reliability analysis Subsea wellhead system |
| Title | Reliability analysis of subsea wellhead system subject to fatigue and degradation during service life |
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