Enterprise AI for Preventing Downtime of End Flash Gas Compressors

Challenge

A major player in the global liquified natural gas (LNG) industry configured the BHC3TM Reliability application to help operators accurately predict compressor failures, reduce false alarms, and better leverage the high volume of control systems data.

End flash gas compressor systems, which pressurize the vapor produced during the LNG refrigeration process, are managed by direct control systems (DCS). The existing control systems lack the ability to accurately predict compressor failures and do not provide the root cause analysis required for troubleshooting. The resulting unplanned shutdowns cause significant delays and increase maintenance costs. To address these issues and increase the reliability of its compressors, the LNG company selected BHC3 Reliability.

Approach

The team ingested 1.4 billion data points from telemetry data, event failure history and asset hierarchy extracted from piping and instrumentation diagrams to create a unified and federated data image.

The team then leveraged this data image and a prebuilt anomaly detection machine learning pipeline as the foundation to determine the optimal model for predicting end flash gas compressor failures. After further testing, the final model detected 89% of unplanned shutdowns with a five-day proactive notice and reduced the number of false alarms by 55%. Lastly, the developers configured BHC3 Reliability dashboards to visualize the predictive insights providing a managerial view to maintain overall asset health of end flash gas compressors systems and subsystems.

Project Objectives

  • Create a unified, federated data model integrating disparate data sources (e.g., asset hierarchy, sensor tags, failure events, P&ID)
  • Deliver a user-friendly application that uses machine learning to identify high-risk end flash gas compressor units and predict unplanned shutdowns in advance
  • Reduce the number of false alarms created by rules-based systems (DCS)

About the Company

  • $2B+ in annual revenue
  • 1,000+ employees
  • 20+ million tons per annum of LNG production capacity

Project Highlights

  • 8 weeks from kick-off to production-ready application
  • 1.4 billion data points ingested from 4 disparate data sources and 3 years of data
  • Created unified object model to represent asset hierarchy, telemetry data, and shutdown and events logs
  • 1,000+ machine learning features tested
  • 50+ model configurations built and tested
  • 5 BHC3 Reliability application user interfaces configured

Results

89%
compressor failures accurately predicted
55%
reduction in false alarms raised each month
5
days of proactive notice

Solution Architecture

BHC3 Reliability

Enterprise AI for Oil & Gas

The BHC3 AI Suite provides the necessary and comprehensive services to build enterprise-scale AI applications up to 18x faster than alternative approaches. The BHC3 AI Suite integrates all relevant data sources to rapidly generate predictive insights across the oil and gas value chain. When deployed at enterprise-scale, BHC3 applications can deliver up to $100 million and more in annual economic value to oil and gas organizations.

BHC3 provides wide-ranging Enterprise AI applications for oil and gas companies, including optimizing artificial lift systems operations, selection of drilling targets, and production operations. These pre-configured applications provide insights to automate the well lifecycle design process, allow real-time monitoring of process reliability, and lower costs of maintenance interventions.

Proven results in weeks, not years

timeline
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