Dynamic Bayesian networks based approach for risk analysis of subsea wellhead fatigue failure during service life. (August 2019)

• Record Type: Journal Article
• Authors: Chang, Yuanjiang; Wu, Xiangfei; Zhang, Changshuai; Chen, Guoming; Liu, Xiuquan; Li, Jiayi; Cai, Baoping; Xu, Liangbin
• Is Part Of: Reliability engineering & system safety. Volume 188(2019)
• Page Start: 454
• Page End: 462
• Publication Date: 2019-08
• Subjects: Dynamic Bayesian network -- Subsea wellhead -- Service life fatigue failure -- Probabilistic prediction; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2019.03.040
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A DBN-based methodology for dynamic risk analysis of SW fatigue failure during entire life is proposed. Uncertainty in fatigue damage of SW is analyzed. The most probable factors contributing to fatigue failure of SW are identified. Active measures to mitigate the fatigue failure risk during SW's service life are developed. Abstract: Subsea wellhead is a critical component of the drilling and production system in offshore oil and gas industry. Excited by cyclical fatigue loadings due to environmental forces, the wellhead is prone to fatigue failure, which could lead to the loss of well integrity and even catastrophic accidents. Although fatigue failure probability of the wellhead carries an elevated uncertainties, it will definitely increase with the accumulation of fatigue in wellhead. This paper presents a fatigue failure risk analysis approach based on dynamic Bayesian Networks, aiming to predict the fatigue failure probability of the wellhead during service life. The proposed model can use the previously accumulated fatigue of the wellhead to probabilistically predict the present failure risk under present dynamic conditions. The practical application of the developed model is demonstrated through a case study. Adopting the predictive, diagnostic analysis techniques in the Bayesian inference, the dynamic fatigue failure probability of the wellhead at any time slices was achieved, and the most influential factors were figured out. Finally, the corresponding safety control measures are proposed to effectively mitigate the fatigue failure risk of subsea wellhead during service life.