Enterprise AI for Predicting Gas Compressor Downtime

Challenge

A major hydrocarbon producer wanted to minimize the environmental impact of its upstream oil production and installed mobile gas compressors to utilize associated petroleum gas (APG), a production byproduct. However, within a few months of installing MGCs, operators became overwhelmed with daily compressor failures and over 1,500 alarms per month from each compressor, many of which were false alarms. The existing control systems lacked the ability to accurately predict compressor failures in advance and could not support the root cause analysis required for troubleshooting. The company needed a better solution that could accurately predict failures, identify root causes, and improve the quality of alarms.

Approach

To address these issues and increase the reliability of its compressors, the oil & gas company selected BHC3^™ Reliability. Within 10 weeks, the team ingested over 55 million rows of data from 4 disparate data sources, configured an anomaly detection pipeline to predict compressor failures and configured the BHC3 Reliability dashboard to visualize the predictive insights. With BHC3 Reliability, the company can achieve more than $40M in annual savings from increased productivity, increased gas throughput, and reduced maintenance costs.

Project Objectives

• Reduce the number of false alarms created by rules-based systems
• Reduce non-productive time caused by unplanned downtime and increase throughput

Solution Architecture