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Pipeline Predictive Analytics Through On-Line Remote Corrosion Monitoring

Picture for Pipeline Predictive Analytics Through On-Line Remote Corrosion Monitoring
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Pipelines are vast and complex networks delivering fossil fuel from remote locations to gas processing facilities refineries petrochemical manufacturers and refined products all the way to end users. Pipeline operators rely on Pipeline Integrity Management (PIM) systems to conduct safe and reliable hydrocarbon transportation operations cope with local regulations maximize transportation capacity and identify integrity threats.Internal and external corrosion are leading causes of incidents in pipelines that can lead to spills explosions and increased downtime. ASME describe the threats above as time-dependent; however they are commonly assessed with methods such as in-line inspection direct assessment and hydrostatic pressure tests whose measurement interval can range from months to years providing isolated snapshots throughout the pipeline lifetime. Moreover executing these techniques requires extensive planning and execution pipelines ready to accommodate in-line inspection tools and in some instances stop hydrocarbon transportation activities.Coping with increased demand pushes operators to boost their pipeline’s utilization rate to serve their customers and communities safely and reliably. In consequence PIM systems will require more data to constantly monitor dynamic changes along the infrastructure (either high consequence areas or not) and leverage predictive analytics. Increasing remote corrosion monitoring locations along several pipeline segments provide continuous input to feed PIM systems with on-line data that is seamlessly integrated into the operator’s control systems and data historian minimizing human intervention.This paper will explore remote corrosion monitoring technologies and how increasing real-time insights to risk maintenance and performance can increase reliability and decrease downtime through predictive analytics.

Product Number: 51319-12899-SG
Author: Ivan Stubelj
Publication Date: 2019
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