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Group Method of Data Handling (GMDH) Lithology Identification Based on Wavelet Analysis and Dimensionality Reduction as Well Log Data Pre-Processing Techniques

Author

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  • 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)

  • Yao Yevenyo Ziggah

    (Department of Geomatic Engineering, Faculty of Mineral Resource Technology, University of Mines and Technology, Tarkwa 00233, Ghana)

  • 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)

  • Xiangfeng Zhu

    (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

Although the group method of data handling (GMDH) is a self-organizing metaheuristic neural network capable of developing a classification function using influential input variables, the results can be improved by using some pre-processing steps. In this paper, we propose a joint principal component analysis (PCA) and GMDH (PCA-GMDH) classifier method. We investigated well log data pre-processing techniques composed of dimensionality reduction (DR) and wavelet analysis (WA), using the southern basin of the South Yellow Sea as a case study, with the aim of improving the lithology classification accuracy of the GMDH. Our results showed that the dimensionality reduction method, which is composed of PCA and linear discriminant analysis (LDA), minimized the complexity of the classifier by reducing the number of well log suites to the relevant components and factors. On the other hand, the WA decomposed the well log signals into time-frequency wavelets for the GMDH algorithm. Of all the pre-processing methods, only the PCA was able to significantly increase the classification accuracy rate of the GMDH. Finally, the proposed joint PCA-GMDH classifier not only increased the accuracy but also was able to distinguish between all the classes of lithofacies present in the southern basin of the South Yellow Sea.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1509-:d:224770
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    References listed on IDEAS

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    1. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    2. 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.
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    Cited by:

    1. Zhixue Sun & Baosheng Jiang & Xiangling Li & Jikang Li & Kang Xiao, 2020. "A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning," Energies, MDPI, vol. 13(15), pages 1-15, July.
    2. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).
    3. Sebastian Waszkiewicz & Paulina I. Krakowska-Madejska, 2021. "Vitrinite Equivalent Reflectance Estimation from Improved Maturity Indicator and Well Logs Based on Statistical Methods," Energies, MDPI, vol. 14(19), pages 1-16, September.
    4. Timur Merembayev & Darkhan Kurmangaliyev & Bakhbergen Bekbauov & Yerlan Amanbek, 2021. "A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan," Energies, MDPI, vol. 14(7), pages 1-16, March.
    5. Edyta Puskarczyk, 2020. "Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits," Energies, MDPI, vol. 13(7), pages 1-18, March.
    6. 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.
    7. Cenk Temizel & Uchenna Odi & Karthik Balaji & Hakki Aydin & Javier E. Santos, 2022. "Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches," Energies, MDPI, vol. 15(20), pages 1-15, October.
    8. 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).
    9. 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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