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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
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    References listed on IDEAS

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    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).
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