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This paper discusses the implementation of machine learning-based predictive models for autonomous surveillance of electric submersible pumps (ESPs) to enhance preventative maintenance and reduce downtime. By utilizing AI for anomaly detection, the system allows operators to focus on optimization rather than constant monitoring, resulting in significant cost savings and improved operational efficiency. A pilot project demonstrated the effectiveness of this approach, predicting ESP failures up to 12 days in

Author

nguyen nguyen

Language

EN