Wireless Sensor Networks for Detection and Localization of Subsea Oil Leakages

Abstract

This work studies the impact of Wireless Sensor Networks (WSNs) for oil spill detection and localization in Subsea Production Systems. The case study is the Goliat FPSO, with a realistic assumption about the presence of a WSN built upon the existing passive acoustic sensors installed on each subsea template to monitor the manifold. The sensors take local binary decisions regarding the presence/absence of a spill by performing an energy test. A Fusion Center (FC) collects such local decisions and provides a more reliable global binary decision. The Counting Rule (CR) and a modified Chair-Varshney Rule (MCVR) are compared. An objective function based on the Receiver Operating Characteristic (ROC) is used for threshold design. The FC, in case of a spill detection, provides an estimated position of the leak source. Four localization algorithms are explored: Maximum A-Posteriori (MAP) estimation, Minimum Mean Square Error (MMSE) estimation, and two heuristic centroid-based algorithms. Detection and localization performances are assessed in comparison to the (position) Clairvoyant Chair-Varshney Rule (CVR) and to the Cramer-Rao Lower Bound (CRLB), respectively. The considered framework requires the prior knowledge of the involved subsea production system in terms of components that in case of failure would cause a leakage and their corresponding failure rates.