Abstract
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 k-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.