New Approach for Electrical Submersible Pump Equipment Design: AI-Powered Precision and Engineering Efficiency Tool

Abstract Designing Electrical Submersible Pump (ESP) systems requires combining many types of information, including reservoir inflow, fluid properties, pump performance, and mechanical limits of the well. Traditional design methods often use separate tools and step-by-step calculations, which makes it difficult to evaluate the whole system at once and may lead to inconsistencies. This paper presents a new approach that integrates physics-based engineering models with artificial intelligence (AI) to create a more complete and consistent ESP design process. The engineering part of the approach includes tools for checking equipment clearance, modelling intermittent well behavior, evaluating tubing strength load, performing nodal analysis, and predicting wax and salt deposition. These calculations help assess whether the selected ESP configuration fits the well geometry, works safely under expected loads, and can operate reliably under changing flow and temperature conditions. AI techniques are added to support tasks that are difficult or time-consuming to perform manually. They automatically interpret pump performance curves from images, analyze historical data to identify operational patterns, and estimate well production using a hybrid virtual flow meter that combines physical models with machine learning. A failure prediction model and Pump Health Index (PHI) provide an early indication of abnormal operation based on telemetry trends and design parameters. The proposed approach shows how engineering models and AI methods can work together to reduce uncertainty, improve design consistency, and provide additional diagnostic insights. The study demonstrates that integrating data-driven analysis into the ESP design workflow helps engineers better understand system behavior and make more informed technical decisions.