Enterprise AI for Predicting Asset Failures
A large hydrocarbon producer experienced a major unplanned shutdown due to asset failures and began to reevaluate its current asset maintenance strategy. Before engaging Baker Hughes and C3 AI, the company relied on rules-based control systems to detect operational anomalies. However, the control systems generated a high volume of alarms daily and operators struggled to distinguish false alarms from material anomalies. The control systems also could not accurately predict asset failures and operated in siloes. The company needed a better solution that could improve the quality of alarms, accurately predict failures before they occurred, and provide an integrated end-to-end view of assets.
To address these issues and increase the reliability of the critical systems, the company selected C3 AI® Reliability. Within 2 weeks, a Baker Hughes and C3 AI experts ingested and unified over 2 years of data for more than 2,500 assets from 27 locations. The team configured an anomaly detection pipeline to predict asset failures and provide root cause analyses for 2 critical upstream systems: gas compressors and water injection pumps. With C3 AI Reliability, the oil & gas company can generate over $10 millions of additional annual revenue and savings from increased uptime and reduced maintenance costs across the 2 systems: gas compressors and water injection pumps.
- Configure ML algorithms to detect anomalies at the system and subsystem level
- Display prioritized alerts and recommended actions supported by a robust evidence package
Enterprise AI for Oil & Gas
The C3 AI Platform provides the necessary and comprehensive services to build enterprise-scale AI applications up to 25x faster than alternative approaches. The C3 AI Platform integrates all relevant data sources to rapidly generate predictive insights across the oil and gas value chain. When deployed at enterprise-scale, C3 AI applications can deliver up to $100 million and more in annual economic value to oil and gas organizations.