Robotic digital X-ray scanning system for deep water flexible riser inspection
The boundaries of oil and gas exploration continue to push production facilities into deeper water creating many new challenges for operators to provide adequate integrity assurance of their assets. Operating in deep water hostile environments, flexible risers are used to bring oil from oil wells deep underwater to the surface, where it can be processed by Floating Production, Storage and Off Loading (FPSO) installations. Today, there are over 270 vessels worldwide as oil FPSOs with generally 5-30 riser slots.
A flexible riser consists of a number of layers of steel and polymer that have a complex structure and some layers are shielded by others, making non-destructive testing (NDT) particularly difficult. Further challenging condition arise from corrosive environments, higher pressures and temperatures. The inspection techniques currently available in the market consist of only irregular diver or Remotely Operated Vehicle (ROV) inspections and are able to inspect only the near side layers for wire disruptions, with the far side layers remaining uninspected.
The RobotX project will investigate the feasibility of a robotic digital X-ray scanning system to solve the needs and challenge of deep water flexible risers’ inspection.
The RobotX system will perform a see-through quick scan as it crawls and process the data using innovative image processing methods and categorise them using machine learning.
If defects are detected the robotic system will be able to turn around the riser and perform a more thorough scan. The defect will be correctly identified, using image taken at several angles. The robot and digital radiography equipment would have to withstand harsh environmental conditions i.e. high pressure (100bar).
Several enabling technologies will be explored and feasibility assessed for the deep-water riser inspection applications.
The project will develop:
A Robot crawler capable of moving along the subsea riser and revolve about the axis thereby allowing a complete series of continuous radiographic images;
Image Processing methods to improve image focusing and resolution on real life radiographic images (presence of bio-fouling, repair clamps etc.). New innovative feature analysis methods will be investigated to model observations and reduce effect of noise under extreme adverse conditions;
Innovative machine learning methods, e.g. deep learning, support vector machine etc., to carry out automated defect detection, classification and autonomous decision making for further inspection and intervention.
The innovations of RobotX will allow the detection, and location, of the defects, and classify them according to an existing historical database, automatically deciding on bespoke scans for assessing the severity and needs for future intervention. This autonomous and adaptive scanning methodology will allow cost-effective, fast and comprehensive robotic inspections of flexible risers especially at higher depths (>1000m), where the only technology is based on extremely costly ROV deployed from surface ships.
The project results will feed also into other applications like the inspection of wind or aircraft turbine blades.
- London SouthBank University
- Brunel University London