Estimating Permeability in Carbonate Reservoirs Using Artificial Neural Networks and K-Nearest Neighbor Algorithm

Document Type : Research Paper

Authors

1 Petrophysics Department, Pars Petro Zagros Co., Tehran, Iran\Earth Science, Islamic Azad University, Science and Research Campus, Tehran, Iran

2 Petrophysics Department, Pars Petro Zagros Co., Tehran, Iran\Geology Department, Shahid Chamran University, Ahvaz, Iran

Abstract

Permeability is one of the most important petrophysical properties of hydrocarbon reservoirs. Estimating permeability is a challenge that petroleum engineers face, particularly in carbonate reservoirs, especially karst reservoirs, where data on core samples may be lacking. In this study, empirical relationships, regression analysis, artificial neural networks, and the nearest-neighbor algorithm, were employed to estimate permeability in depth intervals where core data were unavailable. The results obtained from these methods were compared with each other and with core measurements. Electrical facies provide intelligent models with the capability to estimate permeability with more details using conventional petrophysical logs. Furthermore, considering that electrical facies analysis is conducted for all wells in the field, the use of optimized intelligent models allows for the estimation of permeability in all wells, leading to more accurate results. Based on the results, artificial neural networks and the nearest-neighbor algorithm performed better compared to the other methods, with correlation coefficients of 2% and 5% higher, respectively, than the other approaches. To optimize the obtained results, permeability estimation using these two methods was incorporated into the framework of electrical facies modeling. Subsequently, the results of facies analysis were compared with the results of layered modeling. Ultimatly, among the two methods used, the nearest-neighbor algorithm, on average, provides a more suitable permeability estimation for the Fahliyan formation with a correlation coefficient of 66% compared to the artificial neural network method with a correlation coefficient of 57%. The proposed method in this study can be applied in heterogeneous carbonate reservoirs with well-defined heterogeneity in porosity distribution.

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References

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Journal of Petroleum Research
Volume 34, 1403-3 - Serial Number 136
September and October 2024
Pages 163-176

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