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

Author

Listed:
  • Mohamed F. El-Amin

    (College of Engineering, Effat University, Jeddah 21478, Saudi Arabia
    Mathematics Department, Faculty of Science, Aswan University, Aswan 81528, Egypt)

  • 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)

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.

Suggested Citation

  • Mohamed F. El-Amin & Budoor Alwated & Hussein A. Hoteit, 2023. "Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media," Energies, MDPI, vol. 16(2), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:678-:d:1027447
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    References listed on IDEAS

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    1. Darsh T. Wasan & Alex D. Nikolov, 2003. "Spreading of nanofluids on solids," Nature, Nature, vol. 423(6936), pages 156-159, May.
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