Parameter Identification of Empirical Models for Head Estimation of Electrical Submersible Pumps used for Dual-phase Petroleum Fluids

The performance of electrical submersible pumps (ESPs) in petroleum fluids decreases significantly when operating under dual-phase (gas–liquid) flow conditions compared to single-phase operations. Two-phase flow conditions also creates difficulties in head prediction, which is a vital for petroleum production system design and operation. This chapter solves the problem by evaluating and validating empirical head prediction models and identifying their new parameters for two-phase flow situations. The study examines multiple existing head-prediction models and optimize their parameters to enhance their ability to model gas–liquid mixture behavior. The research combines empirical modeling with statistical parameter identification through the application of Ordinary Least Squares (OLS) fitting to extensive experimental data and data filtering methods to reduce measurement noise and achieve robust parameter estimation. The research demonstrates thorough systematic model calibration and validation so that the adjusted models better predict head than uncalibrated models while providing reliable head predictions across various gas volume fractions without requiring complex analytical methods. The practical value of this research becomes evident through a user-friendly graphical user interface (GUI) that uses the calibrated models to let engineers easily calculate ESP head under different two-phase flow conditions. The research findings have been transformed into a decision-support tool by the (GUI) which demonstrates the methodological strength of this chapter while providing a practical solution that can be enhanced for various field applications.