A novel methodology to analyze accident path in deepwater drilling operation considering uncertain information

Highlights: • A network for blowout risk assessment of deepwater drilling is constructed. • Risk entropy is used to measure both technical failures and human errors. • Bayesian theory is applied to capture the dynamics of random factors. • Dijkstra algorithm is adopted to calculate the most probable failure mode of the system An initial failure in a vulnerable part of deepwater drilling system may escalate into major accidents such as blowout, fire, or explosion. Such accidents have characteristics of complexity, dynamics, and uncertainty, which traditional risk assessment methods fail to capture. This paper presents an integrated methodology for evaluating deepwater drilling risk by combining directed acyclic graph (DAG) and risk entropy. The methodology follows four basic steps: identifying risk factors, defining failure scenarios, determining failure probabilities and entropy values, and evaluating the most probable path of failure events. A network topology is established to develop the possible accident scenarios and paths. Risk entropy is then applied to handle both technical failures and human errors. Bayesian theory is used to describe the dynamics of random factors. The shortest path that represents the most probable failure path from an initial event to a blowout accident is further calculated using Dijkstra algorithm. The proposed approach is then applied in a case study about a managed pressure drilling (MPD) system. The result shows that changes of uncertainties of risk factors result in the variation of the shortest path both in probability values and event sequences. Hence the targeted measures can be implemented according to the assessment result.