IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4456-d842317.html
   My bibliography  Save this article

WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

Author

Listed:
  • Youzhuang Sun

    (College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China)

  • Junhua Zhang

    (College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China)

  • Zhengjun Yu

    (Sinopec Shengli Oilfield, Dongying 257000, China)

  • Zhen Liu

    (College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China)

  • Pengbo Yin

    (College of Earth Science and Technology, China University of Petroleum, Qingdao 266555, China)

Abstract

Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the performance of the model, RMSE, R 2 , MAE and VAF evaluation indexes are introduced to evaluate. This paper also introduces the non-optimized Elman neural network and BP neural network to compare with this optimization model. The research shows that the WOA algorithm optimizes the super parameters of the Elman neural network, so that the performance of the WOA–Elman model is better than the Elman neural network model and the BP neural network model.

Suggested Citation

  • Youzhuang Sun & Junhua Zhang & Zhengjun Yu & Zhen Liu & Pengbo Yin, 2022. "WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve," Energies, MDPI, vol. 15(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4456-:d:842317
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4456/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/12/4456/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mulashani, Alvin K. & Shen, Chuanbo & Nkurlu, Baraka M. & Mkono, Christopher N. & Kawamala, Martin, 2022. "Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data," Energy, Elsevier, vol. 239(PA).
    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. Mkono, Christopher N. & Chuanbo, Shen & Mulashani, Alvin K. & Mwakipunda, Grant Charles, 2023. "Deep learning integrated approach for hydrocarbon source rock evaluation and geochemical indicators prediction in the Jurassic - Paleogene of the Mandawa basin, SE Tanzania," Energy, Elsevier, vol. 284(C).
    2. Wang, Jun & Cao, Junxing & Fu, Jingcheng & Xu, Hanqing, 2022. "Missing well logs prediction using deep learning integrated neural network with the self-attention mechanism," Energy, Elsevier, vol. 261(PB).

    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:12:p:4456-:d:842317. 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.