Simulation of Core Flooding with Predicted Oil and Water Relative Permeabilities Using Bagging, Boosting, and Stacking Machine Learning Techniques

Oil field development and management require oil reservoir simulations, whose parameters include relative permeability curves. However, empirical measurement of relative permeabilities can be arduous and time-consuming, and the machine learning models that can predict them are often difficult to use. This study presents the simulation of a core flooding experiment using predicted oil and water relative permeabilities and the simple supervised machine learning models used to predict them. A model was developed for predicting each relative permeability. These models were based on a data set containing over 1000 data points and bagging, boosting, and stacking techniques (random forest, adaptive boosting, and linear regression algorithms). Model evaluation showed a high coefficient of determination and a small mean squared error, demonstrating model accuracy. Furthermore, the evaluation metrics of <i>k</i>-fold cross-validation were close to those of the models, indicating they could generalize and had minimal overfitting. The experimental and simulated oil recovery factors were 60.05 and 59.45%, respectively, with a history match quality index of 95%. These findings validated the machine learning models' predictions as viable alternatives that researchers can use when lacking empirically measured values.