Machine-learning approach improves deepwater facility uptime

Deepwater oil and gas facilities encounter up to an estimated 5% annual production loss, estimated at billions of dollars, because of unplanned downtime. This paper describes an automated work flow that uses sensor data and machine-learning (ML) algorithms to predict and identify root causes of impending and unplanned shutdown events and provide actionable insights. A systematic application of such a method could prevent unfavorable operational situations in real time using equipment and process sensor data. © 2020 Society of Petroleum Engineers. All rights reserved.