Deep Learning-Based Performance Prediction of Electric Submersible Pumps Under Viscous and Gas–Liquid Flow Conditions

Electric Submersible Pumps (ESPs) play a pivotal role in the petroleum industry, but their performance is significantly affected by factors such as oil viscosity, gas–liquid ratios, and solid content. Traditional performance prediction methods, including polynomial fitting and mechanistic modeling, often lack adaptability and efficiency, requiring extensive empirical testing. This study leverages experimental data from the viscous and gas–liquid flow tests reported in the literature to benchmark various prediction methods. This research provides a comparative analysis of traditional curve-fitting methods, mechanistic modeling, and seven machine learning approaches. A key innovation of this study is an in-depth sensitivity analysis of different machine learning methods, especially focused on neural network parameters, such as activation functions and training configurations, to assess their impact on prediction accuracy and identify optimal network designs. Furthermore, a pump testing methodology is introduced to significantly reduce testing costs while maintaining a high prediction accuracy. The findings demonstrate the advantages of machine learning over traditional methods, including an enhanced prediction accuracy, practical guidelines for efficient parameter tuning, and the ability to address incomplete pump curve data. These contributions not only highlight the value of integrating machine learning into ESP modeling and operational workflows but also pave the way for future advancements in universal modeling frameworks for diverse ESP applications.