Hybrid Multi-phase Choke Model for Predicting Pressure Drop and Choke Size for Production Optimization

Abstract

This study addresses the problem of pressure drop and choke size prediction a in high water -cut oil wells by developing a New Multi-Phase Choke Formula (NMPCF) (a hybrid multi-phase choke model), to improve prediction accuracy and operational performance. A robust predictive model was developed that integrates mechanistic, empirical, and machine learning approaches for estimating pressure drop (ΔP) and choke size (Cₛ). Historical well test data were obtained from two fields in Niger Delta with choke sizes ranging from 0.1875-0.4375 inches, flow rates of 270-1673 bbl/day, and water cuts up to 40.26%. Data preprocessing and model implementation were carried out using Python, leveraging libraries such as NumPy, SciPy, and scikit-learn. Random Forest regression was trained on an 80/20 train-test split to learn nonlinear relationships and provide correction terms to the mechanistic model. Results show that the coefficient of determination (R 2 ) is greater than 0.85. The dataset of 1000 samples exhibited a mean water cut of 0.52 and mean choke size of 63.87 inches enhancing model generalization. Pressure drop increased nonlinearly with water cut (Wc), particularly beyond Wc = 0.5, due to increased mixture density and viscosity. The model maintained stable predictions across average flow rates of 420.71 bbl/day. The NMPCF model provides improved accuracy for predicting pressure drop and choke size, making it suitable for optimizing production in high water-cut oil wells.