Providing a comprehensive approach to oil well blowout risk assessment.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network for risk assessment of oil well blowout (Fig. 5 of the referenced paper). Probabilities were given within the referenced paper. The vertices are:
(F, S);
BOP stack failure (F, S);
Valve failure (F, S);
BOP control system failure (F, S);
Line failure (F, S);
Choke manifold failure (F, S);
Annular preventer (F, S);
Ram preventer (F, S);
Kill valve fail (F, S);
Choke valve fail (F, S);
Choke line fail (F, S);
Kill line fail (F, S);
Upper annular preventer fails (F, S);
Lower annular preventer fails (F, S);
Upper pipe ram fail (F, S);
Middle pipe ram fail (F, S);
Lower pipe ram failure (F, S);
Blind shear ram failure (F, S);
Power system failure (F, S);
4Way valve failure (F, S);
Remote panel valve failure (F, S);
Signal line failure (F, S);
Accumulator line failure (F, S);
Air-driven pump failure (F, S);
Electric pump failure (F, S);
Choke valve failure (F, S);
Hydraulic choke valve failure (F, S);
Gate valve failure (F, S);
Choke remote panel failure (F, S);
Hydraulic choke valve failure (F, S);
Satiarvand, M., Orak, N., Varshosaz, K., Hassan, E. M., & Cheraghi, M. (2023). Providing a comprehensive approach to oil well blowout risk assessment. Plos One, 18(12), e0296086.