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Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

Author

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  • 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)

  • 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)

  • Yao Yevenyo Ziggah

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

  • Mercy Moses Rulegeya

    (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

In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basin of the South Yellow Sea using petrophysical well log data. Generally, the performance of GPR is deeply reliant on the type covariance function utilized. Therefore, to obtain the optimal GPR model, five different kernel functions were tested. The resulting optimal GPR model consisted of the exponential covariance function, which produced the highest correlation coefficient (R) of 0.85 and the least root mean square error (RMSE) of 0.037 and 6.47 for porosity and permeability, respectively. Comparison was further made with benchmark methods involving a back propagation neural network (BPNN), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The statistical findings revealed that the proposed GPR is a powerful technique and can be used as a supplement to the widely used artificial neural network methods. In terms of computational speed, the GPR technique was computationally faster than the BPNN, GRNN, and RBFNN methods in estimating reservoir porosity and permeability.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3261-:d:184993
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    Citations

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    Cited by:

    1. Rana Muhammad Adnan Ikram & Xinyi Cao & Kulwinder Singh Parmar & Ozgur Kisi & Shamsuddin Shahid & Mohammad Zounemat-Kermani, 2023. "Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods," Mathematics, MDPI, vol. 11(14), pages 1-24, July.
    2. Muhammad Naeim Mohd Aris & Hanita Daud & Khairul Arifin Mohd Noh & Sarat Chandra Dass, 2021. "Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging," Mathematics, MDPI, vol. 9(9), pages 1-24, April.
    3. 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.
    4. 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.
    5. 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.
    6. 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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