IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i22p8389-d968318.html
   My bibliography  Save this article

Cuttings Bed Height Prediction in Microhole Horizontal Wells with Artificial Intelligence Models

Author

Listed:
  • Yaotu Han

    (State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China
    Tianjin Branch of CNOOC Ltd., Tianjin 300450, China)

  • Xiaocheng Zhang

    (State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China
    Tianjin Branch of CNOOC Ltd., Tianjin 300450, China)

  • Zhengming Xu

    (School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China)

  • Xianzhi Song

    (College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China)

  • Weijie Zhao

    (State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China
    CNOOC Research Institute Ltd., Beijing 100028, China)

  • Qilong Zhang

    (State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China
    Tianjin Branch of CNOOC Ltd., Tianjin 300450, China)

Abstract

Inadequate drill cuttings removal can cause costly problems such as excessive drag, lower rate of penetration, and even mechanical pipe sticking. Cuttings bed height is usually used to evaluate hole-cleaning efficiency in horizontal wells. In this study, artificial intelligence models, including artificial neural network (ANN), support vector regression (SVR), recurrent neural network (RNN), and long short-term memory (LSTM), were employed to predict cuttings bed height in the well-bore. A total of 136 different tests were conducted, and cuttings bed height under different conditions were measured in our previous study. By training four different artificial intelligence models with the experiment data, it was found that the ANN model performed best among other artificial intelligence models. The ANN model outperformed the dimensionless cuttings bed height model proposed in our previous study. Due to the amount of data points, the memory ability of RNN and LSTM models has not been entirely played compared with the ANN model.

Suggested Citation

  • Yaotu Han & Xiaocheng Zhang & Zhengming Xu & Xianzhi Song & Weijie Zhao & Qilong Zhang, 2022. "Cuttings Bed Height Prediction in Microhole Horizontal Wells with Artificial Intelligence Models," Energies, MDPI, vol. 15(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8389-:d:968318
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/22/8389/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/22/8389/
    Download Restriction: no
    ---><---

    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:15:y:2022:i:22:p:8389-:d:968318. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.