Dynamic assessment of safety barriers preventing escalation in offshore Oil&Gas

Abstract

Oil&Gas activities in the arctic and subarctic regions are characterized by several challenges related to the harsh but sensitive environment in which they are carried out. The weather may deteriorate facility components at a higher rate, and delay operations, emergency and evacuation procedures. Moreover, these regions host unique ecosystems, and their preservation is a worldwide priority. For this reason, a comprehensive and systematic approach for risk analysis is necessary to prevent major accidents and comply with Arctic pollution control. A novel approach for dynamic risk assessment and management, based on Bayesian Networks and safety barrier assessment, is suggested. The method is applied to the Goliat Oil&Gas platform located in the Barents Sea and risk data on the Norwegian petroleum activities are used as evidence to simulate continuous update of risk assessment throughout the years. The case study shows the benefits and limitations of such approach. Accurate modelling of potential accident scenarios is possible through BNs, but time-consuming. The approach allows for drill-down capabilities, which enhance support of operations and definition of risk mitigating measures. However, the data used for dynamic risk assessment has a pivotal role, as data quality and quantity sensibly affect the outcome. Fortunately, the Oil&Gas industry is committed to improving collection of field data for the assessment of safety barrier performance. This approach represents a strategy to process deviations and resilient reactions, regularly iterating dynamic risk assessment to support risk management of critical systems, such as the Oil&Gas production in the arctic and sub-arctic regions.