Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media
Subjectnanoparticles; enhanced oil recovery; machine learning; artificial neural networks; gradient boosting regression; random forest; decision tree
MetadataShow full item record
AbstractReservoir simulation is a time-consuming procedure that requires a deep understanding of complex fluid flow processes as well as the numerical solution of nonlinear partial differential equations. Machine learning algorithms have made significant progress in modeling flow problems in reservoir engineering. This study employs machine learning methods such as random forest, decision trees, gradient boosting regression, and artificial neural networks to forecast nanoparticle transport with the two-phase flow in porous media. Due to the shortage of data on nanoparticle transport in porous media, this work creates artificial datasets using a mathematical model. It predicts nanoparticle transport behavior using machine learning techniques, including gradient boosting regression, decision trees, random forests, and artificial neural networks. Utilizing the scikit-learn toolkit, strategies for data preprocessing, correlation, and feature importance are addressed. Furthermore, the GridSearchCV algorithm is used to optimize hyperparameter tuning. The mean absolute error, R-squared correlation, mean squared error, and root mean square error are used to assess the models. The ANN model has the best performance in forecasting the transport of nanoparticles in porous media, according to the results.