AI-Driven Robotics for Pipeline Integrity: Autonomous Subsea Applications in the North Sea

Abstract Maintaining subsea pipeline integrity is critical for safe and sustainable offshore operations. Traditional inspection methods are costly, time intensive, and constrained by environmental factors, leading to delays in identification and remediation of integrity risks. For example, pipeline Anode inspection currently requires around six months to complete due to manual analysis of the survey data. The paper presents an AI-driven autonomous robotic system designed for high-precision subsea pipeline inspections in the North Sea. It demonstrates efficient detection of pipeline integrity risks through machine learning, reducing processing time and operational expenditure. The system combines computer vision, sonar imaging, and deep learning to autonomously identify pipeline defects, including anchors-marks, corrosion, marine growth, and leaks. The robot leverages a trained YOLO-model for object detection, optimizing inspection based on seabed conditions. Data fusion techniques integrate acoustic, optical, and sensor data to enhance anomaly detection accuracy. The collected inspection data has been streamlined using a cloud-based repository, enabling automated data ingestion, contextualization, and visualization. Machine learning models trained on historical datasets detect deviations with high precision, reducing the time required for defect identification. Field trials with the North Sea operators and their technology centre have validated the system's reliability and effectiveness [1]. Field applications in the North Sea have demonstrated significant improvements in early-stage defect detection, surpassing traditional remotely operated vehicle (ROV) based methods. The AI-driven autonomous system has increased inspection efficiency by reducing manual inspection/Classification of 5000 images from a couple of months to within an hour. The integration of cloud-based analytics has facilitated predictive maintenance strategies, reduced unplanned shutdowns and optimized long-term asset integrity. Automated anomaly detection has minimized manual processing efforts, leading to faster decision-making and intervention. These findings underscore the transformative potential of AI-driven robotics in offshore integrity management and reinforce their suitability for large-scale industry adoption. This paper presents the first implementation of cloud computing and AI-driven autonomous robotics for subsea pipeline inspections in the North Sea. The integration of machine learning with operational decision-making marks a significant advancement in offshore integrity management. By automating data ingestion, contextualization, and anomaly detection, this approach establishes a new benchmark for efficiency, accuracy, and sustainability in subsea inspection. The findings provide valuable insights for industry stakeholders transitioning from conventional inspection methods to AI-powered autonomous solutions.