AI-Enhanced Multi-Method Pipeline Monitoring

Leak detection systems are a crucial part in pipeline safety. Unfortunately, general knowledge about these systems is still limited in the pipeline and measurement industry. Recent advances in leak detection technologies have now enabled operators to operate their pipelines with confidence that their assets are not only secure, but also in compliance with local and industry regulations and standards, such as those developed by the American Petroleum Industry (API).

Computational Pipeline Monitoring methods such as Extended Real Time Transient Modeling (E-RTTM) aim to deliver a reliable form of real-time leak detection technology that can be implemented on new and existing pipelines irrespective of the product that they transport. These advanced leak detection systems rely on sophisticated algorithms that model the pipeline operation throughout all operational conditions such as: Steady State, Transient, Shut-in, and Standstill conditions, ensuring 24/7 coverage and provide information about leak anomalies, leak size, and leak location.

Extended Real Time Transient Modeling works in two stages. In the first stage, properties of the fluid, properties of the pipeline, elevation profiles, and equations for conservation of energy, conservation of mass, and conservation of momentum are all considered and used to determine the expected pipeline flow in leak free scenario. These calculated values are then compared to the flow, pressure and temperature measurements at inlet and outlet locations of the pipeline in real time to detect anomalies. The second stage uses Leak Signature Analysis to filter through false alarms enabling pipeline operators to respond with confidence in only a matter of minutes. Now with the implementation of Artificial Intelligence Optimization, leak detection systems have become more accurate, reliable, and free of false alarms.

AI Optimized E-RTTM systems lead to a reduction in product loss, a reduction in cleanup cost, and lower environmental damage in the event of a leak or a rupture.

Key learning objectives:

• Learn about the importance of leak detection
• Learn about leak detection methodology
• Learn about the use of AI in leak detection