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 (B-o) 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 B-o. 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 B-o. The most reliable ET model could predict B-o 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 B-o models.

Automated summary

This study focuses on developing reliable and accurate compositional oil formation volume factor (B-o) models using advanced machine learning models. The results show that tree-based models, especially the ET model, outperform other models and can be reliably applied for estimating B-o. Furthermore, machine learning models provide more accurate predictions compared to equations of state.