Increasing attention has been focused on environmental protection from hydrocarbon release in the sea due to recent infamous events. In order to limit the environmental impact of an oil spill, early detection may be employed. Standards and guidelines are established for developing effective sensor networks in the subsea templates for both monitoring purposes and data collection. However, sensors provide heterogeneous information about the subsea templates they are monitoring. According to recent understanding of risk, continuous knowledge improvement in relation to a specific system is an important feature that should be considered for better managing potential variation of the risk level. The information provided by sensor networks may be used in this perspective. Sensors may be functionally placed in fault tree analysis to update data about deviation frequency. They may provide an information basis for dynamic risk management in a quasi-real time.
Therefore, this work focuses on risk management using information provided from subsea sensor networks. The study adopts a top-down approach inspired by System Engineering to analyse the communication patterns and information exchanges among the different stakeholders. A real reference case from the oil and gas industry located in a particularly environmentally sensitive area in the Norwegian continent shelf is considered for testing the suggested approach. The case study refers to subsea monitoring of oil leakages from the wellhead templates, namely the wellhead and X-tree. Different sensor configurations are considered in order to identify the one that best provides the higher amount of reliable information. The insights from the case study highlight how sensor data analysis may improve risk management but also consistently support operational decision making.