An Applied Machine Learning Approach to Subsea Asset Inspection

Abstract

Abstract Subsea infrastructure inspection campaigns are conducted on a fixed time schedule to ensure the integrity of offshore assets while guaranteeing compliance with government and other stakeholders’ legislation. These thorough assessments of subsea assets are achieved by acquiring and processing survey results alongside the relevant engineering data and environmental conditions. The current approach is not ideal. The infrastructure operators would prefer to have more frequent, on-demand field visits and base the assessments on an up-to-date high resolution, precise asset model. Unfortunately, this has historically not been technically feasible. The underwater inspection vehicles moved relatively slow, the positioning accuracy was limited, and the underwater sensors’ accuracy and resolution was not comparable with those used in air. This is not the case anymore. We now operate inspection vehicles traveling with speeds over six knots! We can control them remotely from the office-based command center. Next to the acoustic technology, the vehicles are additionally equipped with optic sensors, dynamically collecting point clouds, images and videos of unprecedented resolution. Gigabytes of data hits the vessel every few minutes. And that's where it used to end… Processing and, so called, "Digital Twin" modelling from this data has traditionally been taking weeks for every day of inspection. In many cases it was simply not technically possible since the data files were prohibitively big. Data required subsampling, more data processors had to go offshore, asset operators had to wait longer. Given the increasing capacity of satellite links, limitless potential of commoditized cloud computing and sophisticated Deep Learning algorithms, we are now able to move subsea asset management to the new era. Through one of their latest research & development programmes - Roames, Fugro has been able to revolutionize the traditional workflows using the state-of-the-art digital technology. The resultant product is a bespoke, web-based service that enables asset (initially pipelines) inspection data to be uploaded to the secure cloud environment, processed using Machine Learning, verified by experts on shore and visualized in an intuitive 4D web viewer. All delivered to the geographically spread stakeholders, operators and decision makers in near real time. This approach greatly reduces the cost of infrastructure management practices, lowers the risk exposure and contributes to extended life of the asset. The Roames, Machine Learning based methodology was validated using a vast archive of survey data prior to testing the workflow on a live project. Detailed method statement and field results are presented in this paper.