Chairs: Doug Norton; Terra15; Joel Le Calvez, SLB

The primary challenge involving the integrity monitoring of pipelines and wellbores is providing a high confidence level of the asset’s integrity assessment based on the interpretation of the monitoring system data.  The copious data from DFOS technologies coupled with the recent advancements in machine learning and AI are providing more reliable and hence more valuable information regarding the status of these assets and any potential impact to the environment and personnel.  This session explores case histories, lessons learned, and the solutions to asset integrity and flow assurance issues in the upstream and midstream markets.  Presentations will highlight the value of conclusions made from the DFOS data and their impact on installation practices, event detection challenges and tradeoffs, and new features and capabilities.

Pipeline Monitoring - Using Machine Learning        
Safil Sunny, Tranzmeo             

Fiber Optic-Assisted Well Intervention        
Ivan Lim Chen Ning, Chevron             

Pipeline Third Party Intrusion Detection with DAS and Machine Learning            
Mathieu Champion, FEBUS