Abstract
This paper presents a simple machine learning based framework for diagnosing the inline inspection data (ILI) of subsea pipelines. ILI data are obtained by intelligent pigging devices operating along subsea pipelines. The wall thickness (WT) and standoff distance (SO) are collected by the sensors installed on the pigging, which are normally in the format of 2D arrays. There are many uncertainties for the ILI data collected from the offshore survey. An attempt was made to apply the machine learning method to diagnose the uncertainties. A convolutional neural network (CNN) is used, the ILI data are discretized and processed in 64x64 grid size. Fabricated training datasets were made for training the machine learning model since the ground truth information (actual corroded wall thickness) is hardly known in this case. The trained model was successfully. It is demonstrated that certain corrosion patterns have been recognized by the trained model. Comparisons were performed between the new method and traditional methods with case studies on real ILI data. The validity of the methodology was discussed.
Highlights
According to the Norwegian petroleum directorate (2021)[1], the total length of the Norwegian pipeline network is over 15, 000 kilometers
It is seen that the ML filter gives an remaining wall thickness (RWT) close to 14.5 mm, with less variation over the length of the section
The simulated data shows that some corrosion patterns have been successfully recognized
Summary
According to the Norwegian petroleum directorate (2021)[1], the total length of the Norwegian pipeline network is over 15, 000 kilometers (oil and gas). The inspection tool travels along the pipeline, collecting the remaining wall thickness data (WT) and the standing-off data (SO). For ultrasonics testing inspection tool (UT), the measuring principle is based on perpendicular incidence of ultrasound into the pipe wall. The ultrasonic pulse is reflected from the back wall and travels several times to and from, until the signal energy is dissipated. The time t between entry echo and first rear wall echo or between two rear wall echoes is measured. The wall thickness can be determined by the time and sound velocity. The distance between probe and pipe wall is measured (stand-off), see Figure 1 for an illustration of the inspection principle.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
