Case Study: a leading oil and gas company improves pipeline efficiency with Progress DataRPM

Gas Pipeline Improves Station Efficiency and Drives Revenue with DataRPM

A leading oil and gas company was spending millions annually on corrective maintenance for its pipeline and faced an estimated $250 million in lost revenue due to undelivered gas. The challenge involved managing 22 injection stations under disparate conditions and identifying the complex interdependencies between them to improve overall pipeline efficiency. The company turned to Progress Software and its DataRPM Cognitive Anomaly Detection and Prediction (CADP) solution to build a more accurate predictive maintenance model.

Progress Software implemented its DataRPM CADP solution, which used self-learning algorithms to analyze over 15 variables from each station. The solution automatically created a highly accurate model with over 97% accuracy in less than four days, a task that could normally take months. This provided the company with actionable insights and real-time monitoring, enabling them to save millions by reducing unplanned downtime and gaining unparalleled visibility into their pipeline's performance.