Fast evaluation of well placements in heterogeneous reservoir models using machine learning

A handful of training observations are obtained from numerical reservoir simulations. The Extreme Gradient Boosting method is then used to build an intelligent model for making predictions given any set of observations. The proposed approach is demonstrated using five synthetic case studies: i) a homogeneous reservoir waterflood, ii) a channelized reservoir waterflood, iii) a 20-model ensemble waterflood, iv) a CO2 flood in a heterogeneous reservoir, v) a CO2 flood in a heterogeneous reservoir with complex topography. Results show a significant correlation between proxy predictions and reservoir simulation results.