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dc.contributor.authorEl-Amin, Mohamed F.
dc.contributor.authorAlwated, Budoor
dc.contributor.authorHoteit, Hussein
dc.date.accessioned2023-03-13T09:20:54Z
dc.date.available2023-03-13T09:20:54Z
dc.date.issued2023-01-06
dc.identifier.citationEl-Amin, M.F.; Alwated, B.; Hoteit, H.A. Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media. Energies 2023, 16, 678. https://doi.org/10.3390/en16020678en_US
dc.identifier.doihttps://doi.org/10.3390/en16020678en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/619
dc.description.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 means 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.en_US
dc.publisherMDPIen_US
dc.subjectNanoparticlesen_US
dc.subjectEnhanced Oil Recoveryen_US
dc.subjectMachine Learningen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectGradient Boosting Regressionen_US
dc.subjectRandom Foresten_US
dc.subjectDecision Treeen_US
dc.titleMachine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Mediaen_US
dc.source.journalEnergiesen_US
dc.source.volume16en_US
dc.source.issue2en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labEnergy Laben_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentNSMTUen_US
dc.contributor.pgstudent1en_US
dc.contributor.firstauthorEl-Amin, Mohamed F.


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