Dynamic Risk Analysis of Deepwater Gas Hydrate Drilling with a Riserless Drilling System Based on Uncertain Dynamic Bayesian Network Model
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 1
Abstract
The riserless drilling system (RDS) is a dual gradient drilling system. Offshore natural gas hydrate (NGH) drilling projects are often subject to various risks due to uncertainty factors (marine environment and complex operating conditions). When encountering the NGH layer during drilling, significant risks can be generated by the decomposition and secondary generation of the NGH. In order to predict risk accidents, environmental factors and equipment factors are analyzed in this study. First, a bow-tie (BT) model is established, which is then transformed into a Bayesian network (BN) using a mapping algorithm. Secondly, uncertainty modeling is carried out in BN. The leaky noisy-OR node is added to BN and the uncertain influence of the logical relationship is considered. Then, considering the dynamic uncertainty of equipment factors, the established BN is transformed into an uncertain dynamic BN (UDBN) through the transition probability matrix. In addition, fuzzy theory and expert judgment are used to quantify the prior probability of equipment failures. Considering the effect of NGH, the risk of RDS is analyzed by the developed model, and the dynamic risk probabilities under three working conditions are obtained. The sensitivity analysis of the equipment was also carried out. The final result shows that the drilling risk probability in an NGH layer is higher than that under normal working conditions. Due to the influence of NGH, the probability of risks also becomes uncertain. After adding uncertain factors, the probability of drilling risk occurrence is reduced. The correctness of the established UDBNs model is verified by the Petri nets method.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (51704254), the Key Research and Development Project in Key Technical Field of Sichuan Province (2019ZDZX0030), the International Science and Technology Innovation Cooperation Program of Sichuan Province (2021YFH0115), the International Science and Technology Cooperation Project of Chengdu (2019-GH02-00039-HZ), and the Technology Innovation Research and Development Project of Chengdu (2019-YF05-01872-SN).
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8 • Issue 1 • March 2022
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© 2021 American Society of Civil Engineers.
History
Received: Aug 31, 2021
Accepted: Oct 6, 2021
Published online: Dec 8, 2021
Published in print: Mar 1, 2022
Discussion open until: May 8, 2022
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