A dynamic and quantitative risk assessment method with uncertainties for offshore managed pressure drilling phases

Abstract

Drilling into offshore oil and gas fields often meets many challenges and uncertainties, such as a narrow window of drilling fluid density and shallow gas zones. Managed pressure drilling (MPD) techniques are increasingly used as alternatives to conventional drilling operations to manage such extreme conditions and reduce drilling costs and risks. Many safety and operational issues related to MPD process need to be investigated more thoroughly. Well kick is considered a typical hazardous event that may occur at different drilling phases, and such an event is prone to develop into a blowout. During offshore drilling phases, the risk of accidents may change with time, and such a dynamic characteristic should be recorded in risk assessment. This study presents a method for the application of dynamic Bayesian networks (DBNs) in conducting accident scenario analysis and dynamic quantitative risk assessment for MPD safety. This method can model the influence of uncertain risk factors, which have been ignored in existing research, by introducing an additional probability parameter. The effects of degradation are also taken into account. DBN inference is adopted to perform quantitative risk analysis and dynamic risk evolution. Then, the vulnerable root causes are identified by sensitivity analysis for accident prevention and mitigation measures. Well kick for four drilling cases is analyzed as a case study to demonstrate the feasibility of the proposed method. Three-step analysis partially validates the correctness and rationality of the proposed DBN model.