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Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

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  • Mulashani, Alvin K.
  • Shen, Chuanbo
  • Nkurlu, Baraka M.
  • Mkono, Christopher N.
  • Kawamala, Martin

Abstract

Permeability is the key variable for reservoir characterization used for estimating the flow patterns and volume of hydrocarbons. Modern computer advancement has highlighted the use of machine learning approaches such as group method of data handling (GMDH) in predicting permeability. However, the widely employed GMDH has intrinsic problems in its application. Therefore, the objective of this study is to present an enhanced GMDH based modified Levenberg-Marquardt (LM) as an improved alternative to conventional GMDH in predicting permeability from well logs. The study used natural gamma-ray, standard resolution formation density, limited effective porosity, shale volume of rock, and thermal neutron porosity well logs as input variables. Results show that an enhanced method has a reasonable reduction in processing time with high accuracy. Compared to conventional GMDH and backpropagation neural networks (BPNN), the GMDH-LM used 30% less computation time and performed excellently during training with the least error values of 0.092 and 0.018 for RMSE and MAE. Likewise, good results were observed during testing, obtaining the least error values of 0.679 and 0.056 for RMSE and MAE respectively. The modified generalization performance of GMDH-LM makes it an improved form of GMDH and can be adopted as an improved alternative in predicting permeability.

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  • 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).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221021630
    DOI: 10.1016/j.energy.2021.121915
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Abelly, Elieneza Nicodemus & Yang, Feng & Ngata, Mbega Ramadhani & Mwakipunda, Grant Charles & Shanghvi, Eric Richard, 2024. "A field study of pore-network systems on the tight shale gas formation through adsorption-desorption technique and mercury intrusion capillary porosimeter: Percolation theory and simulations," Energy, Elsevier, vol. 302(C).
    3. 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.
    4. 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).

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