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

Baraka Mathew Nkurlu, Chuanbo Shen, Solomon Asante-Okyere, Alvin K. Mulashani, Jacqueline Chungu and Liang Wang
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Baraka Mathew Nkurlu: 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
Solomon Asante-Okyere: Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education, 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
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

Energies, 2020, vol. 13, issue 3, 1-18

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.

Keywords: permeability; group method of data handling; artificial neural network; well logs; sensitivity analysis (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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