IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p678-d1027447.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/678/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/2/678/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Darsh T. Wasan & Alex D. Nikolov, 2003. "Spreading of nanofluids on solids," Nature, Nature, vol. 423(6936), pages 156-159, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tawfik, Mohamed M., 2017. "Experimental studies of nanofluid thermal conductivity enhancement and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1239-1253.
    2. Xiao Wang & Senbo Xiao & Zhiliang Zhang & Jianying He, 2017. "Effect of Nanoparticles on Spontaneous Imbibition of Water into Ultraconfined Reservoir Capillary by Molecular Dynamics Simulation," Energies, MDPI, vol. 10(4), pages 1-14, April.
    3. Jianzhong Wang & Suo Tian & Xiaoze Liu & Xiangtao Wang & Yue Huang & Yingchao Fu & Qingfa Xu, 2022. "Molecular Dynamics Simulation of the Oil–Water Interface Behavior of Modified Graphene Oxide and Its Effect on Interfacial Phenomena," Energies, MDPI, vol. 15(12), pages 1-12, June.
    4. Khalil, Munawar & Jan, Badrul Mohamed & Tong, Chong Wen & Berawi, Mohammed Ali, 2017. "Advanced nanomaterials in oil and gas industry: Design, application and challenges," Applied Energy, Elsevier, vol. 191(C), pages 287-310.
    5. Devendiran, Dhinesh Kumar & Amirtham, Valan Arasu, 2016. "A review on preparation, characterization, properties and applications of nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 21-40.
    6. Murshed, S.M. Sohel & Nieto de Castro, C.A. & Lourenço, M.J.V. & Lopes, M.L.M. & Santos, F.J.V., 2011. "A review of boiling and convective heat transfer with nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(5), pages 2342-2354, June.
    7. Bahador Najafiazar & Dag Wessel-Berg & Per Eirik Bergmo & Christian Rone Simon & Juan Yang & Ole Torsæter & Torleif Holt, 2019. "Polymer Gels Made with Functionalized Organo-Silica Nanomaterials for Conformance Control," Energies, MDPI, vol. 12(19), pages 1-28, September.
    8. Alberto Bila & Ole Torsæter, 2020. "Enhancing Oil Recovery with Hydrophilic Polymer-Coated Silica Nanoparticles," Energies, MDPI, vol. 13(21), pages 1-15, November.
    9. Chaturvedi, Krishna Raghav & Trivedi, Japan & Sharma, Tushar, 2020. "Single-step silica nanofluid for improved carbon dioxide flow and reduced formation damage in porous media for carbon utilization," Energy, Elsevier, vol. 197(C).
    10. Yunus Tansu Aksoy & Yanshen Zhu & Pinar Eneren & Erin Koos & Maria Rosaria Vetrano, 2020. "The Impact of Nanofluids on Droplet/Spray Cooling of a Heated Surface: A Critical Review," Energies, MDPI, vol. 14(1), pages 1-33, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:678-:d:1027447. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.