Tree-Based Ensembles for Predicting the Bottomhole Pressure of Oil and Gas Well Flows
In this paper, we develop a predictive model for the multi-phase wellbore flows based on ensembles of decision trees like Random Forest or XGBoost. The tree-based ensembles are trained on the time series of different physical parameters generated using the numerical simulator of the full-scale transient wellbore flows. Once the training is completed, the ensemble is used to predict one of the key parameters of the wellbore flow, namely, the bottomhole pressure. According to our recent experiments with complex wellbore configurations and flows, the normalized root mean squared error (NRMSE) of prediction below 5% can be achieved and beaten by ensembles of decision trees in comparison to artificial neural networks. Moreover, the obtained solution is more scalable and demonstrate good noise-tolerance properties. The error analysis shows that the prediction becomes particularly challenging in the case of highly transient slug flows. Some hints for overcoming these challenges and research prospects are provided.