In-Line Service for Internal Inspection of Unpiggable Buried Oil Pipeleines Using Long Range Ultrasound Guided Waves in Fifty Metre Segments

The PIGWaves project targets at developing an in-service pipe inspection tool capable of inspecting both piggable and currently unpiggable oil pipelines of steel construction and internal diameter 150-350mm that provides 100% volume inspection for features such as hard spots, stress corrosion cracking, corrosion and erosion. Long Range Ultrasound Guided Waves (LRUG) will be employed by the robot in order to produce a map of the circumferential and axial pipe corrosion and cracks. Moreover, total volume inspection of the pipeline will be achieved far more rapidly, accurately and cheaply than state-of-the-art magnetic and ultrasonic pigs. After project ends the system will be scaled up to inspect pipelines of diameters 500-1000mm.

The PIGWaves system will comprise of a robot that will be capable of working in pipelines that carry liquids, and particularly oil. The PIGWaves robot will float down the pipeline in a non-inspecting mode. Every 50m it will expand a flexible probe collar to lock itself to the circumference of the pipe. The collar will employ Long Range Ultrasound Guided waves technology (LRUG) to inspect a pipe segment of 50m in both directions. It will then retract the collar and move another 50m before inspecting the next 50m. The robot will communicate with base station at entry point to send NDT data and locate the position of the robot. The robotic system will be designed to float freely past dents, sharp bends, debris, valves and changing pipe diameters. Moreover, in the presence of no flow at the pipe, the robot will actively swim past these obstructions.

The aim is to perform total volume inspection far more rapidly and accurately than current methods of ultrasonic NDT inspection. In the field of pipeline inspection LRUG presents the benefit that the probes would only need to be adjusted every 50m, the typical attainable propagation range of LRUG in pipelines, thus making the adaptation more feasible.

This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 315232