
Offshore drilling accident analysis based on Dynamic Bayesian Network
Author(s) -
Wang Xing-zhong,
Xinghua Kou,
Jinfeng Huang,
Wanli Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2029/1/012143
Subject(s) - offshore drilling , submarine pipeline , drilling , bayesian network , petroleum engineering , engineering , marine engineering , accident (philosophy) , risk analysis (engineering) , computer science , geotechnical engineering , mechanical engineering , business , artificial intelligence , philosophy , epistemology
Offshore drilling operations has the characteristics of far offshore, harsh environment, high risk and great technical difficulty. It is difficult to deal with offshore drilling accidents, and it is easy to cause serious social and economic problems. Based on the risk records of drilling engineering, this paper analyzes the forms and causes of offshore drilling accidents, and establishes a dynamic Bayesian network analysis model for offshore drilling accidents. With the dynamic Bayesian network, the prediction probability of drilling risk is calculated. According to the blowout accident in offshore drilling, the analysis results show that the risk of blowout accident is is as high as 50.6%. The research results show that the method and the model presented in the paper is contributed to analysis and prevention of offshore drilling accidents.