Enhancing Upstream Operations: Hybrid Approach for ESP Failure Prediction in ADNOC’s Assets for Operational Excellence and Digital Progress

Abstract This paper proposes a transformative approach in Electric Submersible Pump (ESP) management, aiming to predict failures and prevent ESP downtimes ahead of their occurrence. The goal is to build a reliable tool that assists in the decision-making process for preventive ESP replacements. This initiative not only enhances planning through accurate rig allocation and inventory optimization but also minimizes deferred production by establishing a zero-failure intervention strategy. The predictive model operates on an assembly that considers a Real-Time data acquisition system, core predictive model algorithms, and automated engineering workflows. This ensemble is designed to process both real-time and historical data, complemented with surface parameters to accurately forecast ESP failures while providing probabilities of occurrence. By integrating machine learning techniques with traditional physics and statistical analysis, the model provides critical insights into ESP health, thus enabling proactive maintenance and operational planning. Applied across diverse ESP setups, this proactive strategy not only sustains production but also enhances operational flexibility. The ESP predictive model provides the information that enables the establishment of a cohesive predictive maintenance solution that effectively unifies the principles of data science with operational technology. The implementation has notably improved the reliability of ESP operations by providing petroleum engineers with the tools to maintain continuous production. The evidence gathered during the pilot phase reveals the system’s proficiency in anticipating ESP failures, leading to a significant reduction in unplanned shutdowns and maintenance costs. The success of this project emphasizes the strategic value of predictive maintenance in realizing operational efficiency, enhancing profitability, and fostering a digitally empowered corporate culture within ADNOC’s upstream sector. This paper introduces a groundbreaking approach to predictive maintenance in the oilfield, enhancing established methodologies with a comprehensive, data-driven strategy that bonds machine learning, physics, statistics, and real-time operational data. This novel method not only advances ESP management but also sets a new standard in failure prevention and operational optimization while improving decision-making by providing probabilities of occurrence.