Context-Based and Image-Based Subsea Pipeline Degradation Monitoring

Abstract

This research examines the factors contributing to the exterior material degradation of subsea oil and gas pipelines monitored with autonomous underwater systems (AUS). The AUS have a role of gathering image data that is further analyzed with artificial intelligence data analysis methods. Corrosion and potential ruptures on pipeline surfaces are complex processes involving several competing elements, such as the geographical properties, composition of soil, atmosphere, and marine life, whose effects can result in substantial environmental damage and financial loss. Despite extensive research, corrosion monitoring and prediction remain a persistent challenge in the industry. There is a lack of knowledge map that can enable image analysis using an AUS to recognize ongoing degradation processes and potentially prevent substantial damage. The main contribution of this research is the knowledge map for increased context and risk awareness to improve the reliability of image-based monitoring and inspection by autonomous underwater systems in detecting hazards and early signs of material degradation on subsea pipeline surfaces.

Common pitfalls in using AI in high-risk domains

“…Based on the literature review and the authors personal experience in the application of AI and machine learning a set of common pitfalls using AI in high risk domains were identified, and a checklist constructed to reduce the risk of falling victim of them. Examples from successful use of AI in the oil and gas industry was used to investigate the relevance of a risk based approach to using AI [17]. Finally, a paper co-authored by one of the authors [18] (Randeberg) was used to test the relevance of the checklist for practical applications.…”