Enhancing Pipeline Integrity Management with Machine Learning and Integrated Monitoring Technologies

Abstract

Abstract As the oil and gas pipeline industry shifts toward digitalization, machine learning and artificial intelligence (AI) play an increasingly important role in asset integrity management, including operation monitoring, leak and intrusion detection, corrosion protection, and flow assurance, among others. This paper introduces an integrated approach using fiber optics, inspection reports, and fluid flow simulations and demonstrates how machine learning and AI can help operators by producing unified insights. Fiber-optic distributed acoustic sensing (DAS) technologies are routinely used to monitor pipeline activities; critical events such as product leaks, digging near the pipeline, and pigging are captured by quantitatively analyzing unique signatures on the fiber-optic generated space-time image. This can be treated as a pattern recognition or machine learning problem. YOLO, a state-of-the-art fast object detection algorithm, was used to demonstrate accurate tracking of pipeline inspection gauges (PIGs), among other activities, using a small quantity of training data. In addition, using AI, routine inspection reports and flow simulation results were automatically calibrated, cross-validated, and then contextualized with the fiber-optic DAS generated events. The event detection and classification algorithm used in this work achieves high location accuracy, superior to current industry-standard methods. As a result, this method significantly improves the tracking of PIGs. More importantly, these detections are automatically calibrated with inspection reports for cross-validation. Traditionally, fiber-optic systems are an independent and isolated sensor technology, which require field teams to perform manual activities approximately every 2 km along the entire pipeline for georeferencing. This is inefficient and does not provide the location accuracy needed to link the fiber-optic system to other sources of data, such as inspection reports or flow simulation results. This lack of integration has been a longstanding challenge that prevented operators from easily isolating important signals or repeated trends for each weld, valve, meter, or road crossing, for example. With our machine learning - assisted integrated management system, various sources of data can be consolidated and analyzed to provide valuable information that was previously unavailable. This paper presents the novel use of fast machine learning models to accurately detect and track pipeline activities. In addition, data analytics aids in the calibration and cross-validation of different monitoring technologies under a single integrated pipeline integrity management platform, providing operators with unique insights.