Application of artificial neural network to predict slug liquid holdup

This work demonstrates the artificial neural network (ANN) ability for predicting slug liquid holdup (H-LS), using 2525 measured points from 20 experimental studies. Six variables, including superficial gas velocity (V-SG), superficial liquid velocity, liquid viscosity, pipe diameter, pipe inclination (empty set), and surface tension, are selected as inputs to the ANN. The optimum ANN structure obtained is 6-11-1, with tangent sigmoid as an activation function. The developed ANN performs best and outperforms 12 existing H-LS models compared with present data and independent data. A sensitivity analysis shows that empty set has the lowest impact, whereas V-SG is the most significant variable on ANN-H-LS. To demonstrate the impact of ANN-H-LS on pressure drop in pipes, a mathematical model is derived and combined with the slug mechanistic models of Zhang et al. (2003) and Abdul-Majeed and Al-Mashat (2000). Based on Tulsa University Fluid Flow Project field measured data (1712 well cases), the new mathematical model's incorporation results in better predictions than using the individual H-LS correlations of these two models. The statistical results indicate that the slug model of Zhang et al. (2003) with ANN-H-LS and modified Barnea map gives the best performance compared to the existing pressure models.

Automated summary

This work demonstrates the ability of artificial neural networks (ANNs) in predicting slug liquid holdup, with a developed ANN model outperforming existing models. The ANN model combined with slug mechanistic models provides better predictions for pressure drop in pipes.