| Summary: | Oil and gas development in the Arctic is becoming a major focus in the industry today. However, compared to other regions, there is less experience regarding drilling operations in the Arctic environment. This fact raises concerns about high-risk scenarios, which might take place because of the harsh environmental conditions and the effects they have on various phases of operations, equipment and human performance. The operating conditions depend on the location in the Arctic, but sea ice, spray and atmospheric icing, low temperatures, seasonal darkness, winds, and polar lows, are considered as important Arctic environmental factors. In any offshore drilling setting, well control operations are among the most crucial activities taking place, from risk perspective. A failure to control the wellbore can lead to devastating scenarios such as oil spills, explosions and major fatalities. There are mainly two safety barriers in place to prevent the loss of well control: primary and secondary well control barriers. The former refers to maintaining the wellbore pressure greater than formation pore pressure and less than formation fracture pressure, using the mud column pressure. The latter refers to mechanically securing the wellbore utilizing the blowout preventer (BOP) stack. This study aims to develop a risk model for a well control operation, based on which the effects of the operating conditions in the Arctic offshore can be assessed. This aim is achieved through a stepwise procedure. By identifying the causes and consequences of failures in different phases of a well control operation, the risk model is built. Furthermore, the potential impacts of Arctic operating environment are investigated. Finally, how such impacts can be quantified and applied to the model is discussed. The analyses performed in this study indicate that Arctic operating conditions can negatively affect human performance and reliability performances of well control procedures. The potential impacts are accounted for in the developed risk model through an expert-based approach, based on linear aggregation of expert opinions, through which the decision-maker's distribution is estimated using a Monte Carlo simulation method. A sensitivity analysis of well control safety barriers is performed, using Birnbaum's importance measure, to prioritize such barriers from reliability performance perspective. Moreover, a Monte Carlo simulation technique is used for the propagation of parameter uncertainties, to evaluate the resulting probabilities of near miss and blowout.
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