TY - JOUR

T1 - Estimation of carbonates permeability using pore network parameters extracted from thin section images and comparison with experimental data

AU - Rabbani, Arash

AU - Assadi, Ali

AU - Kharrat, Riyaz

AU - Dashti, Nader

AU - Ayatollahi, Shahab

N1 - Publisher Copyright: © 2017 Elsevier B.V.

PY - 2017/3/12

Y1 - 2017/3/12

N2 - Petrography and image analysis have been widely used to identify and quantify porous characteristics in carbonate reservoirs. This paper uses the thin section images of 200 carbonate rock samples to predict the absolute permeability using intelligent and empirical methods. For each thin section, several pore network parameters are extracted from thin section images of rocks including the average pore size, average throat size, average throat length and average 2-D coordination number of pore network. A neural-based model successfully predicts the permeability of samples using pore network parameters as the inputs. Second neural network is applied for predicting absolute permeability considering lithology, pore type and fabric of the rock samples. Finally, an empirical formula containing porosity and average coordination number as inputs is proposed to predict the permeability. It has been found that the porosity and coordination number can directly describe the permeability of carbonates while pore and throat sizes extracted from a single 2-D cross section of rock cannot explain the permeability of carbonates very well. The results of this study indicate the better performance of pore network extraction method compared to the simple regression analysis for prediction of the permeability.

AB - Petrography and image analysis have been widely used to identify and quantify porous characteristics in carbonate reservoirs. This paper uses the thin section images of 200 carbonate rock samples to predict the absolute permeability using intelligent and empirical methods. For each thin section, several pore network parameters are extracted from thin section images of rocks including the average pore size, average throat size, average throat length and average 2-D coordination number of pore network. A neural-based model successfully predicts the permeability of samples using pore network parameters as the inputs. Second neural network is applied for predicting absolute permeability considering lithology, pore type and fabric of the rock samples. Finally, an empirical formula containing porosity and average coordination number as inputs is proposed to predict the permeability. It has been found that the porosity and coordination number can directly describe the permeability of carbonates while pore and throat sizes extracted from a single 2-D cross section of rock cannot explain the permeability of carbonates very well. The results of this study indicate the better performance of pore network extraction method compared to the simple regression analysis for prediction of the permeability.

KW - Carbonate rocks

KW - Pore network modeling

KW - Thin section images

UR - http://www.scopus.com/inward/record.url?scp=85015749437&partnerID=8YFLogxK

U2 - 10.1016/j.jngse.2017.02.045

DO - 10.1016/j.jngse.2017.02.045

M3 - Article

AN - SCOPUS:85015749437

VL - 42.2017

SP - 85

EP - 98

JO - Journal of Natural Gas Science and Engineering

JF - Journal of Natural Gas Science and Engineering

SN - 1875-5100

IS - June

ER -