Session: 01-03 Advanced Analytics for Condition Monitoring and Asset Reliability

Paper Number: 173658

173658 - Digital Solutions for Sucker-Rod Pumping Systems: A Comprehensive Review With Industry Integration and Deviated-Well Perspective 

Sucker-rod pumping (SRP) systems continue to dominate artificial lift operations, particularly for mature and marginal wells, due to their mechanical robustness and operational simplicity. However, the evolving landscape towards deviated and horizontal wells presents unique technical challenges, including complex rod-string interactions, increased frictional losses, and distinctive multiphase fluid dynamics. Concurrently, digital transformation driven by Industry 4.0 technologies, such as advanced sensing, real-time data analytics, and machine learning algorithms, has substantially enhanced the predictive diagnostics and operational efficiencies of SRP systems.
This review systematically examines recent developments in digital SRP solutions, categorizing advancements into physics-based modeling, instrumentation, signal-based diagnostics, control strategies, and digital twin technologies. It provides an in-depth evaluation of technology readiness levels (TRLs) and industry integration status, highlighting gaps between theoretical
innovation and practical field deployment. Special attention is directed toward deviated and horizontal well configurations, emphasizing existing limitations in current predictive models and control strategies.
Cross-cutting themes, including synthetic and augmented data techniques, uncertainty quantification, federated learning, and interpretability, are extensively analyzed to underline their importance in achieving robust, reliable, and operationally transparent models. Finally, targeted recommendations are articulated for future research, advocating for the development of deviation-aware digital twins, advanced data augmentation, uncertainty-aware and interpretable modeling approaches, reinforcement learning frameworks for adaptive control, and robust deployment pipelines. This review underscores the critical need for multidisciplinary collaboration to fully realize the benefits of digital innovations in complex SRP environments.

Presenting Author: Malek Rekik ChampionX

Presenting Author Biography: Malek Rekik received an associate degree in Physics and Technology from 'IPEIS - Institut Préparatoire aux Études d'Ingénieur de Sfax' in 2017 in Sfax, Tunisia. He also received a multidisciplinary engineering degree from École Polytechnique de Tunisie in 2020 in Tunis, Tunisia. He received his Ph.D. in Mechanical Engineering from the University of Houston, Houston, TX, USA, in 2024. He is currently working as a Senior Data Scientist at ChampionX, focusing on applying physics-informed machine learning techniques to oil and gas assets control, health monitoring, and anomaly detection. His research focuses on linear and nonlinear system identification, nonlinear control theory, uncertainty quantification and machine learning.