Floating Storage and Offloading (FSO) Mooring System Identification Using Integrated Side Scan Sonar And Multibeam Echosounder

Abstract

The Floating Storage and Offloading (FSO) plays an important role as a key component in supporting the infrastructure for oil and gas production activities in maritime. Regular inspections of FSOs' mooring systems, which provide the main support, are necessary to ensure their operating stability and safety. This study aims to identify the buoy mooring systems through an integrated using two geophysical acoustic methods, Side Scan Sonar (SSS) and Multibeam Echosounder (MBES). SSS provides high resolution backscatter images of the seabed by recording the reflected (backscattered) images of objects through sonar transducers on both sides. Meanwhile, MBES captures bathymetry, backscatter intensity, and water column data by measuring acoustic travel time (∆T) and swath angle (ψ) from each emitted pulse, which are then used to calculate depth and cross-track position. Data processing was completed using a standard workflow, starting with raw data import. SSS processing focused on slant distance correction, clean water column, additional filtering, mosaicking, and seabed texture interpretation and object detection, while MBES processing included motion and SVP correction, patch test correction, tidal adjustment and noise filtering, bathymetry extraction, and grid generation. Both data types were then visualized and analyzed for seabed classification and feature mapping. The results showed six mooring systems consisting of anchors, sinkers, and chains, with two sunken buoys identified in systems 2 and 3. In addition, three pipelines and several debris objects were identified. Seabed morphology analysis found five types of classification, while sediment classification showed sand dominance, with four types of sediment identified. MBES data recorded depth variations ranging from 5.16 to 22.37 mLWS. Depth data obtained from Multibeam Echosounder measurements is considered accurate, as the 95% confidence value of 0.12 is lower than the maximum allowable TVU of 0.2887 based on IHO S-44 2022 Special Order criteria. The integration of SSS and MBES has been shown to improve the accuracy of identification and mapping of seabed features and conditions, thereby supporting more effective mooring system maintenance and improving operational safety through early detection of potential geological hazards.