Enterprise AI for Improving Electric Submersible Pump Performance

Challenge

An integrated hydrocarbon producer wanted to improve the reliability of Electric Submersible Pumps (ESP) in its upstream operations to reduce unplanned downtime and deferred production. Prior to engaging Baker Hughes and C3 AI, the oil & gas company relied on threshold-based systems to monitor its fleet of 2,100 ESPs. Existing systems, however, generated a high volume of false alarms and could not provide the failure cause analysis required to resolve alerts quickly. Due to the constraints of conventional monitoring systems, approximately 20% of ESPs failed on an annual basis.

Approach

To anticipate and prevent ESP failures, the oil & gas company selected C3 AI® Reliability. Within 14 weeks, the Baker Hughes and C3 AI team unified over 5 years of data from 9 disparate data sources, integrated the C3 AI Reliability Failure Mode Library to provide failure cause analysis, and configured over 14,400 machine learning models to predict system failures for 400 ESPs across 2 different fields. With C3 AI Reliability, the oil & gas company can achieve up to $13M of additional recurring revenue by reducing downtime and costs associated with ESP failures.

Project Objectives

• Reduce the number of false alarms generated by threshold-based systems
• Apply machine learning to predict ESP failures 20-40 days in advance of failure

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.