AI-Driven Analytics for Electric Submersible Pump Efficiency

This case study presents an AI-driven analytics solution designed to enhance the efficiency of Electric Submersible Pump (ESP) monitoring across 11 wells. The solution addresses challenges such as fragmented data and energy inefficiencies by integrating real-time power analysis, machine learning, and sensor networks. It enables predictive maintenance, reduces false alarms, and improves operational clarity. The Automated Event Detection (AED) system is highlighted, which comprises a Data Quality engine, reference engine, machine learning-based event detectors, and an event identification layer. This system monitors equipment in real time, validates streaming data, and establishes baseline operating conditions. It identifies patterns linked to ESP anomalies and provides immediate visualization of power quality. The results indicate that the system can detect electrical stress, energy leaks, and optimize operational practices, ultimately extending equipment life and minimizing downtime. The integration with Prognostic Health Management solutions further enhances maintenance strategies and operational efficiency.