Asset Integrity Inspection with AI for Topside Offshore Structures & Marine Terminals

Abstract

Abstract In this paper, we detail how damage mechanisms commonly found on marine terminal structures and process equipment can be automatically detected using a machine learning (ML) pipeline. Defects in its varied forms, such as corrosion, steel, wood and concrete damages must be closely monitored in offshore oil and gas platforms and also in marine terminals; the cost of not monitoring and detecting defects can range from unplanned shutdowns, plant damage, to human fatalities. On-site inspection of defects is labor intensive and require frequent travel to offshore and onshore facilities. This process has two shortcomings: firstly, it is costly involving man hours spent on the facility and secondly only a subset of the total defects is monitored during each inspection due to difficulty in obtaining full coverage; specially in this scenario a high-risk defect could be missed completely. We use marine terminals as an example of how defects can be detected using machine learning techniques, however the pipeline for generating the ML models could be extended to other types of defects and structures. Significantly, the output from our ML pipeline can be used as an input to the Risk Based Inspection (RBI) system assisting asset operators in their inspection and maintenance planning. Furthermore, digitized inspections and ML can provide around 95% coverage of the terminal while avoiding problems faced by a human inspector such as producing consistent results across inspections and sites. Our system captured terrestrial spherical imagery and range data (LIDAR), subsea imagery, sonar, and ultrasonic thickness (UT) gauge data using different sensors and equipment on a marine terminal. Equipment used in our data-capture included an unmanned aerial vehicle (UAV), a terrestrial scanner, a remotely operated vehicle for underwater imaging (ROV) and a remotely operated surface vehicle (ROSV). For the purposes of this work, we used the images captured by the terrestrial scanner. We built an automated defect detection pipeline using state of the art machine learning and artificial intelligence (AI) tools taking the cube face images as inputs. The defect detection pipeline was trained with examples of defects labeled by asset integrity engineers to ensure quality of results. We built the pipeline to detect damage mechanisms for steel and wooden structures, however our system could be scaled to incorporate other types of defects and structures. Our AI pipeline was able to achieve a high level of accuracy identifying wood damage with a mean-Average Precision (m-AP) of 0.80 and steel damage with a m-AP of 0.84, where a m-AP of 1.0 indicates perfect detection of damage.