Intelligent pore type characterization: Improved theory for rock physics modelling

ABSTRACT Thanks to the recent developments in both hardware and software capabilities of computers, intelligent rock physics modelling has emerged as an alternative to the conventional approach to the rock physics. Respecting the crucial contribution of the pore geometry into the rock physics modelling, I propose an accurate yet cost‐ and time‐efficient intelligent framework to measure pore space based on digital rock physics. In this method, total pore space was calculated after estimating the pore geometry through pattern recognition on thin section images captured through polarized‐light microscopy. Next, applying three different multi‐class classifiers (radial basis function, support vector machine and k‐nearest neighbours) for estimating pore type and aspect ratio, the best results were obtained using the fuzzy Sugeno integral, and the pore types were classified according to the most widely used pore type classification scheme. Next, an artificial neural network was applied to interpolate discrete data points (thin sections) into continuous profiles of pore type and aspect ratio. Subsequently, as a case study, the proposed approaches were applied to a real‐world carbonate reservoir for modelling the P‐ and S‐wave velocities through a rock physics model. Verifying the modelling results against ultrasonic and measured well‐logging data, the methodology showed promising performance at acceptable levels of uncertainty. The most significant advantage of the intelligent pore type quantification over the conventional methods was found to be its ability to estimate elastic properties with good accuracy. The key findings of this research include automatic detection of the pore types and aspect ratio, provision of a database of pore geometry, attenuation of uncertainty in pore type characterization and improvement of rock physics modelling in the absence of reliable S‐wave velocity data.