Document Type : Original Research Article
Authors
1
Department of Petroleum and Geoenergy Engineering, Amirkabir University of Technology, Tehran, Iran
2
Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran
Abstract
Electric Submersible Pumps (ESPs) are essential artificial lift systems that enable sustained hydrocarbon production across diverse reservoir conditions. However, ESPs operate in some of the most severe downhole environments in the oil and gas industry, characterized by extreme temperatures, high pressures, corrosive fluids, and abrasive particulates, resulting in frequent failures and costly workovers. Traditional maintenance strategies, including reactive and preventive approaches, have proven inadequate for addressing the operational, economic, and environmental challenges posed by ESP failures. Predictive maintenance, enabled by advances in IoT sensor technologies, edge and cloud computing, digital twins, and artificial intelligence, represents a significant advancement in condition-based monitoring and reliability management of ESP systems. By continuously monitoring system health, detecting anomalies early, and accurately forecasting failures, predictive maintenance significantly reduces downtime, lowers operational expenditure, enhances energy efficiency, and supports environmental stewardship. This paper presents a comprehensive descriptive analysis of predictive maintenance for ESP systems. It begins by examining the role of ESPs in hydrocarbon production, the limitations of traditional maintenance, and the economic drivers for a reliability-centered strategy. It then explores the technical foundations of predictive maintenance, including data acquisition, analytical models, and key performance indicators. Operational challenges, benefits, and global adoption trends are analyzed through real-world data, and a detailed case study highlights successful implementation in offshore operations. Future directions such as explainable AI, blockchain for maintenance traceability, edge computing advancements, and holistic adoption pathways are also discussed. By integrating technical depth with practical insight, this paper positions predictive maintenance as a cornerstone of modern upstream digital transformation, enabling safer, more reliable, and more sustainable ESP operations. The study combines a structured review of predictive maintenance technologies with a validated offshore case study to present an integrated, reliability-centered framework for ESP asset management.
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