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Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

Author

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  • Baraka Mathew Nkurlu

    (Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education, China University of Geosciences, Wuhan 430074, China
    Department of Petroleum Geology, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Chuanbo Shen

    (Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education, China University of Geosciences, Wuhan 430074, China
    Department of Petroleum Geology, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Solomon Asante-Okyere

    (Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education, China University of Geosciences, Wuhan 430074, China
    Department of Petroleum Geology, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Alvin K. Mulashani

    (Department of Petroleum Geology, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China
    Department of Geoscience and Mining Technology, College of Engineering and Technology, Mbeya University of Science and Technology, Mbeya 00225, Tanzania)

  • Jacqueline Chungu

    (Department of Petroleum Geology, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Liang Wang

    (Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education, China University of Geosciences, Wuhan 430074, China
    Department of Petroleum Geology, Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China)

Abstract

Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

Suggested Citation

  • Baraka Mathew Nkurlu & Chuanbo Shen & Solomon Asante-Okyere & Alvin K. Mulashani & Jacqueline Chungu & Liang Wang, 2020. "Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data," Energies, MDPI, vol. 13(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:551-:d:312377
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    References listed on IDEAS

    as
    1. Solomon Asante-Okyere & Chuanbo Shen & Yao Yevenyo Ziggah & Mercy Moses Rulegeya & Xiangfeng Zhu, 2018. "Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability," Energies, MDPI, vol. 11(12), pages 1-13, November.
    2. Shaghaghi, Saba & Bonakdari, Hossein & Gholami, Azadeh & Ebtehaj, Isa & Zeinolabedini, Maryam, 2017. "Comparative analysis of GMDH neural network based on genetic algorithm and particle swarm optimization in stable channel design," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 271-286.
    3. J. -A. Müller & A. G. Ivachnenko & F. Lemke, 1998. "GMDH algorithms for complex systems modelling," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 4(4), pages 275-316, January.
    4. Jungwon Yu & June Ho Park & Sungshin Kim, 2018. "A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting," Energies, MDPI, vol. 11(11), pages 1-20, October.
    5. Lambert, Romain S.C. & Lemke, Frank & Kucherenko, Sergei S. & Song, Shufang & Shah, Nilay, 2016. "Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 42-54.
    6. Chuanbo Shen & Solomon Asante-Okyere & Yao Yevenyo Ziggah & Liang Wang & Xiangfeng Zhu, 2019. "Group Method of Data Handling (GMDH) Lithology Identification Based on Wavelet Analysis and Dimensionality Reduction as Well Log Data Pre-Processing Techniques," Energies, MDPI, vol. 12(8), pages 1-16, April.
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    Cited by:

    1. Reza Rezaee, 2022. "Editorial on Special Issues of Development of Unconventional Reservoirs," Energies, MDPI, vol. 15(7), pages 1-9, April.
    2. 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).
    3. Rana Muhammad Adnan & Salim Heddam & Zaher Mundher Yaseen & Shamsuddin Shahid & Ozgur Kisi & Binquan Li, 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches," Sustainability, MDPI, vol. 13(1), pages 1-21, December.

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