Pipeline 3D-Positioning at River Crossing: Long-Range Magnetic Via Unmanned Aerial System (UAS)

Pipeline networks traverse modern societies’ national territories and can be located in areas difficult to access, making maintenance logistically challenging and potentially dangerous for field personnel. River crossings, where pipelines are buried under the bed of a water stream, fall under such a definition. Recurrent safety monitoring operations entail the deployment of divers with accompanying operational risks and constraints. In addition, most of the tools available are inefficient due to the presence of water. In particular, traditional Ground Penetrating Radars (GPR) are not applicable. Other radio-frequency equipment will provide low-density datasets which rely significantly on human interpretation, introducing measurement biases impacting accuracy and reliability. Skipper NDT has enhanced its proprietary autonomous technology to serve clients looking to position and secure their pipeline networks under river crossings. Magnetic mapping using a UAS vector allows, firstly, quick data acquisition with less than 30 minutes of flight time per 100-m of river inspection, and, secondly, an automated survey without putting field personnel at a safety risk. Datasets that are developed using such a system present a high spatial density, up to 20 points per meter (6 points per foot), which enables the creation of high-precision digital twins of the buried structure. Data processing is also automated through proprietary and patented algorithms. In addition, Skipper NDT deliverables are ESRI® compatible and can integrate thirdparty GIS datasets, such as bathymetric or photogrammetric measurements. Thus, a 3D model of the river crossing features using QGIS software was made possible, to further enhance decision-making capabilities of pipeline integrity departments. The performance of the Skipper NDT technology was tested under real field conditions with the collaboration of the incumbent French Gas operator GRTgaz managing over 32’500 km of pipeline network. This paper is based on 3 case studies from 160 to 220 m (522 to 722 ft) river crossings with a maximum depth of 12 m (39 ft). The data show a strong correlation with existing information while enhancing data quality and reliability.