Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media

Mohamed F. El-Amin, Budoor Alwated and Hussein A. Hoteit
Additional contact information
Mohamed F. El-Amin: College of Engineering, Effat University, Jeddah 21478, Saudi Arabia
Budoor Alwated: College of Engineering, Effat University, Jeddah 21478, Saudi Arabia
Hussein A. Hoteit: Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia

Energies, 2023, vol. 16, issue 2, 1-27

Abstract: Reservoir 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.

Keywords: nanoparticles; enhanced oil recovery; machine learning; artificial neural networks; gradient boosting regression; random forest; decision tree (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/2/678/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/2/678/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:2:p:678-:d:1027447

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().