I am an ESP Application Engineering expert with over a decade of hands-on expertise in Electric Submersible Pump  systems, combining advanced engineering practice with cutting-edge research in Artificial Intelligence , Machine Learning , and Natural Language Processing  for artificial lift optimization. My experience spans across ESP application engineering, field operations, CFD research, and the digital transformation of ESP lifecycle management.
 ESP Engineering & Field Expertise

• Over 12+ years of experience in ESP system design, installation, and optimization, with deep proficiency in software platforms such as SubPump, Perform, DesignPro, and Prosper,Eclipse etc
• Solid understanding of IPR-VLP-ESP system matching and operating envelope control.
• Experience applying prosper/mbal/GAP concepts for performance tuning and history based optimization.
• Strong foundation in automation and control systems(PLC/SCADA awarness,RTU ,field instrumentation).
• Development of python based engineering and risk assessment tools for esp wells  and AI data analystics also.
• Specialized in DHE & SEE product integration, including project scoping, planning, and lifecycle forecasting for high-efficiency ESP installations.
• Proven track record in cost-saving initiatives and was honored with the Best Cost Saver Award by the President, and recognized under I-Lead New Technology initiatives.
• Skilled in CFD modeling and simulation, particularly focusing on two-phase flow behavior across pump stages to understand flow instabilities and thermal interactions.

 R&D and Manufacturing Insight

• In-depth exposure to ESP equipment R&D, procurement, bidding, and assembly operations.
• Designed and validated ESP systems using 3D modeling (e.g., Alkhorayef models) and Vibration Analysis, incorporating Quality Assurance Testing and Failure Risk Evaluation algorithms.
• Strong foundation in NMR preparation, PMP/MTR data sheets, and ESP selection-to-completion workflows.
• Engineering decisions driven by data,trends and physics (not assumptions).

 AI & Predictive Analytics in ESP

• Conducted original PhD research in AI-integrated ESP performance diagnostics, focusing on machine learning-based anomaly detection, sensor data fusion, and natural language model interpretation for real-time field insights.
• Built a custom NLP-powered ChatBot to assist field operators with ESP troubleshooting, part replacement guidance, and VSD alarm interpretation.
• Developed AI/ML models for predictive maintenance, reducing ESP downtime, improving run life, and enhancing operational efficiency by >25%.
• Leveraged sensor telemetry, motor torque patterns, and wellhead data to train supervised ML models for fault classification (e.g., gas lock, downthrust, insulation failures).
• Keeping pace with evolving AI advancements, with active focus on digital twins for ESPs, reinforcement learning for optimization, and LLM-driven field support systems.

Key Skills

• ESP Design & Commissioning
• AI/ML for Artificial Lift
• PLC/SCADA
• Predictive Maintenance Modeling
• CFD Simulation & Flow Analysis
• ESP Completion & NMR Documentation
• Field Automation & Chatbot Development
• Data Analytics for Failure Diagnostics
• 3D modeling