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

Solomon Asante-Okyere, Chuanbo Shen, Yao Yevenyo Ziggah, Mercy Moses Rulegeya and Xiangfeng Zhu
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Solomon Asante-Okyere: Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education, 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
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

Energies, 2018, vol. 11, issue 12, 1-13

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.

Keywords: Gaussian process regression; porosity; permeability; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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