A reservoir rock porosity estimation through image analysis and fuzzy logic techniques

Abstract

Petrophysical properties of petroleum reservoir rocks are usually obtained by laborious core laboratory measurements. The present study investigates the capability of petrographic image analysis applied on thin sections of reservoir rock and fuzzy logic for predicting porosity in carbonate rocks. The proposed methodology comprises two steps: first, the petrographic parameters, including porosity type, grain size, mean geometrical shape coefficient of grains, and texture type, were extracted for each thin section based on image analysis techniques. Consequently, the petrographic parameters were formulated to core porosity using a Takagi and Sugeno fuzzy inference system. Petrographic image analysis is an emerging technology, which provides fast and accurate quantitative evaluation from reservoir rock. The results of single petrographic image analysis showed inaccurate estimation of total porosity in all rocks except those that have an extremely isotropic pore structure. A quantitative evaluation of thin section images and fuzzy model was successfully used to improve the accuracy of porosity prediction and the results of thin section analysis were generalized to core plug analysis. The mean square error and correlation coefficient between two-dimensional measurements and core plug were obtained at 0.0262 and 86.3, respectively, which shows acceptable prediction of three-dimensional porosity from two-dimensional thin sections. Therefore, the results confirmed the validity of the propounded methodology.