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

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 engin...

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Bibliographic Details
Published in:Energies (Basel) 2023-01, Vol.16 (2), p.678
Main Authors: El-Amin, Mohamed, Alwated, Budoor, Hoteit, Hussein
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
artificial neural networks
Classification
Datasets
Decision trees
Differential equations
Enhanced oil recovery
Errors
Flow velocity
Fluid dynamics
Fluid flow
gradient boosting regression
Hydrocarbons
Learning algorithms
Machine learning
Mathematical models
Multiphase flow
Nanoparticles
Neural networks
Nonlinear differential equations
Partial differential equations
Permeability
Polymers
Porous media
random forest
Research methodology
Reservoir engineering
Reservoirs
Retention
Surfactants
Transport phenomena
Two phase flow
Viscosity
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16020678