IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i6p1479-d1360030.html
   My bibliography  Save this article

Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved Pelican Optimization Algorithm

Author

Listed:
  • Lei Qiao

    (Hebei Instrument & Meter Engineering Technology Research Center, Hebei Petroleum University of Technology, Chengde 067000, China)

  • Nansi He

    (Department of Computer and Information Engineering, Hebei Petroleum University of Technology, Chengde 067000, China)

  • You Cui

    (Hebei Instrument & Meter Engineering Technology Research Center, Hebei Petroleum University of Technology, Chengde 067000, China)

  • Jichang Zhu

    (Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Kun Xiao

    (State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China)

Abstract

To accurately predict reservoir porosity, a method based on bi-directional long short-term memory with attention mechanism (BiLSTM-AM) optimized by the improved pelican optimization algorithm (IPOA) is proposed. Firstly, the nonlinear inertia weight factor, Cauchy mutation, and sparrow warning mechanism are introduced to improve the pelican optimization algorithm (POA). Secondly, the superiority of IPOA is verified by using the CEC–2022 benchmark test functions. In addition, the Wilcoxon test is applied to evaluate the experimental results, which proves the superiority of IPOA against other popular algorithms. Finally, BiLSTM-AM is optimized by IPOA, and IPOA-BiLSTM-AM is used for porosity prediction in the Midlands basin. The results show that IPOA-BiLSTM-AM has the smallest prediction error for the verification set samples (RMSE and MAE were 0.5736 and 0.4313, respectively), which verifies its excellent performance.

Suggested Citation

  • Lei Qiao & Nansi He & You Cui & Jichang Zhu & Kun Xiao, 2024. "Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved Pelican Optimization Algorithm," Energies, MDPI, vol. 17(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1479-:d:1360030
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/6/1479/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/6/1479/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Farzad Kiani & Sajjad Nematzadeh & Fateme Aysin Anka & Mine Afacan Findikli, 2023. "Chaotic Sand Cat Swarm Optimization," Mathematics, MDPI, vol. 11(10), pages 1-47, 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. Fan Cao & Zhili Tang & Caicheng Zhu & Xin Zhao, 2023. "An Efficient Hybrid Multi-Objective Optimization Method Coupling Global Evolutionary and Local Gradient Searches for Solving Aerodynamic Optimization Problems," Mathematics, MDPI, vol. 11(18), pages 1-31, September.

    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:17:y:2024:i:6:p:1479-:d:1360030. 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.