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Hydrogen leakage is a crucial risk for the hydrogen generation unit, which would lead to the potential fire and explosion accidents. Hydrogen leakage risk analysis is the essential alternative to ensure the safety of the hydrogen generation process. This paper presents a dynamic risk analysis methodology regarding the hydrogen leakage in the hydrogen generation unit by using the dynamic Bayesian network, which is employed to address the potential uncertainty and dynamic nature underlying the leakage risk of the hydrogen generation unit. A case study of hydrogen generation unit is carried out to demonstrate the applicability and advantage of the proposed methodology. Results indicate that the leakage probability of hydrogen generation unit can be significantly decreased within one year through equipment repair. Furthermore, the failure and repair rates of overflow alarm and pressure sensor are the most contributory factors to the hydrogen generation unit leakage. Finally, some active mitigative suggestions are presented to further reduce the leakage risk of the hydrogen generation unit. • A DBN-based methodology for dynamic risk prediction of hydrogen generation unit leakage was proposed. • The dynamic risk of hydrogen generation unitleakage and accident consequences were achieved. • The most contributory factors leading to leakage failure of hydrogen generation unit were identified. • Active measures to mitigate the leakage failure risk in the process of hydrogen generation were presented. [ABSTRACT FROM AUTHOR]

The marine natural gas hydrate is considered to have huge resource potential in the South China Sea, and a sixty‐day production test has been successfully conducted in 2017. As the crucial and most vulnerable connection of floating platform and subsea wellhead, the risers are of paramount importance in safe production test of the hydrate. Due to harsh environment and the possible drive off and drift off events induced by failure of dynamic positioning system of the platform, the unplanned emergency disconnect of risers is an integral preventive measure to avoid accidents and secure the well. Once the disconnect occurs, the huge potential energy stored in the riser column will release and consequently cause violent axial recoil, which will cause serious consequences if the recoil control fails. This paper presented a quantitative risk assessment approach on the basis of dynamic Bayesian networks to dynamically predict the risk of riser recoil control failure during production test of hydrate. By using the proposed method, the dynamic failure risk of the riser recoil control was figured out, and the most contributory factors were identified. The practical application of the proposed methodology was demonstrated through a case study. Results indicated that the probability of riser recoil control failure can be significantly decreased within one year through equipment repair, and the failure and repair rates of shutoff valves and main air isolation valves are the most contributory factors. Some corresponding safety control measures were proposed to further mitigate the risk of riser recoil control failure during the production test of hydrate. [ABSTRACT FROM AUTHOR]

• 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. 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. [ABSTRACT FROM AUTHOR]

Drilling risers are crucial connections of subsea wellhead and floating drilling vessel. Riser emergency disconnect (RED) is the most effective protective measure to secure risers and wellhead in case of emergency. There are many different stages for RED depending on riser-connected operation. A novel approach based on Dynamic Bayesian Network and GO (DBN-GO) model is proposed to dynamically analyze the variation of RED risk over time in different stages. The GO model was built by translating the operation flow chart of RED and then mapped into BN. A DBN model was established considering the dynamic reliability of equipment. The cognitive reliability and error analysis method (CREAM) and improved analytic hierarchy process (IAHP) were used to determine and modify the probabilities of human factors in the DBN-GO model. The practical application of the developed model was demonstrated through a case study. The results showed that DBN-GO approach could be used to predict the dynamic success probability of RED under different stages during a drilling duration. Additionally, the key steps corresponding to each stage were identified, and some preventive measures to mitigate the failure risk of RED were proposed. [ABSTRACT FROM AUTHOR]