Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

This communication
primarily concentrates
on developing reliable
and accurate compositional oil formation volume factor (Bo) models using several advanced and powerful machine
learning (ML) models, namely, extra trees (ETs), random forest (RF),
decision trees (DTs), generalized regression neural networks, and
cascade-forward back-propagation network, alongside radial basis function
and multilayer perceptron neural networks. Along with these models,
seven equations of state (EoSs) were employed to estimate Bo. The performance of the developed ML models
and employed EoSs was assessed through various statistical and graphical
evaluations. Overall, the ML models could provide much more accurate
predictions in comparison to EoSs. However, the results indicated
that tree-based models, specifically ET models, could outperform the
other models and can be reliably applied for estimating Bo. The most reliable ET model could predict Bo with a total average error of 1.17%. Lastly, the outlier
detection approach verified the dataset’s consistency detecting
only 17 (out of 1224) data points as outliers for the proposed Bo models.