IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5665-d632015.html
   My bibliography  Save this article

A Two-Parameter Model for Water-Lubricated Pipeline Transportation of Unconventional Crudes

Author

Listed:
  • Sayeed Rushd

    (Department of Chemical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al Ahsa 31982, Saudi Arabia)

  • Ezz Ahmed

    (Department of Chemical and Materials Engineering, Faculty of Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Shahriar Mahmud

    (Craft & Hawkins Department of Petroleum Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA)

  • SK Safdar Hossain

    (Department of Chemical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al Ahsa 31982, Saudi Arabia)

Abstract

Water-lubricated flow technology is an environmentally friendly and economically beneficial means of transporting unconventional viscous crudes. The current research was initiated to investigate an engineering model suitable to estimate the frictional pressure losses in water-lubricated pipelines as a function of design/operating parameters such as flow rates, water content, pipe size, and liquid properties. The available models were reviewed and critically assessed for this purpose. As the reliability of the existing models was not found to be satisfactory, a new two-parameter model was developed based on a phenomenological analysis of the dataset available in the open literature. The experimental conditions for these data included pipe sizes and oil viscosities in the ranges of 25–260 mm and 1220–26,500 mPa·s, respectively. A similar range of water equivalent Reynolds numbers corresponding to the investigated flow conditions was 10 3 –10 6 . The predictions of the new model agreed well with the experimental results. The respective values of the coefficient of determination (R 2 ) and the root mean square error (RMSE) were 0.90 and 0.46. The current model is more refined, easy-to-use, and adaptable compared to other existing models.

Suggested Citation

  • Sayeed Rushd & Ezz Ahmed & Shahriar Mahmud & SK Safdar Hossain, 2021. "A Two-Parameter Model for Water-Lubricated Pipeline Transportation of Unconventional Crudes," Energies, MDPI, vol. 14(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5665-:d:632015
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5665/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/18/5665/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tayeb Brahimi, 2019. "Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia," Energies, MDPI, vol. 12(24), pages 1-16, December.
    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. Yitian Xing & Fue-Sang Lien & William Melek & Eugene Yee, 2022. "A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model," Energies, MDPI, vol. 15(15), pages 1-35, July.
    2. Khadijah Barashid & Amr Munshi & Ahmad Alhindi, 2023. "Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia," Energies, MDPI, vol. 16(2), pages 1-22, January.
    3. Mohamed Mohana & Abdelaziz Salah Saidi & Salem Alelyani & Mohammed J. Alshayeb & Suhail Basha & Ali Eisa Anqi, 2021. "Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning," Energies, MDPI, vol. 14(20), pages 1-18, October.
    4. Ghadah Alkhayat & Syed Hamid Hasan & Rashid Mehmood, 2023. "A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate," Sustainability, MDPI, vol. 15(24), pages 1-39, December.
    5. Noman, Abdullah Al & Tasneem, Zinat & Sahed, Md. Fahad & Muyeen, S.M. & Das, Sajal K. & Alam, Firoz, 2022. "Towards next generation Savonius wind turbine: Artificial intelligence in blade design trends and framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
    7. Shereen K. Sibie & Mohamed F. El-Amin & Shuyu Sun, 2021. "Modeling of Water Generation from Air Using Anhydrous Salts," Energies, MDPI, vol. 14(13), pages 1-21, June.
    8. Luis Baquero & Herena Torio & Paul Leask, 2022. "Machine Learning Algorithms for Vertical Wind Speed Data Extrapolation: Comparison and Performance Using Mesoscale and Measured Site Data," Energies, MDPI, vol. 15(15), pages 1-20, July.

    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:14:y:2021:i:18:p:5665-:d:632015. 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.