Permeability estimation based on thin-section image analysis and 2D flow modeling in grain-dominated carbonates

Abstract

Permeability estimation based on image analysis is a good alternative when an intact core-plug is not available for laboratory measurement. While there is much research on permeability estimation from 2D and 3D images or models, accurately estimating permeability for carbonates, a type of rock with substantial heterogeneity, is still challenging. In this study, a method for permeability estimation based on thin-section image analysis and 2D permeability simulation is developed for grain-dominated carbonates based on semi-theoretical analysis of 2D and 3D permeability (K2D and K3D) relationship. The mathematical expression of the K2D/K3D is examined and validated with a grainstone carbonate sample, for which both K2D and K3D were obtained through permeability simulation based on micro-CT images. The method was applied to 24 grain-dominated carbonate samples collected from different locations with a large variation in rock textures and fabrics and a broad range of permeability from 0.1 mD to 3200 mD. Representativeness of the thin section and the correct determination of the effective pores are the key for accurate estimation. The estimated permeability was compared to the measured value, and 92% of the results are within a factor of ±5, and 46% are with a factor of ±2. Potential sources of systematic error are discussed both qualitatively and quantitatively.

Rock classification in petrographic thin section images based on concatenated convolutional neural networks

“…Moreover, the deep-learning methods classify the thin-section image pixel by pixel, which is called image semantic segmentation, creating the labeled output image, where every single labeled pixel represents a mineral class or pore [11,12]. After that, the extracted pore information can be used to estimate rock permeability and anisotropy in reservoir simulation, hydrology, and environmental engineering [13][14][15].…”

Macroscopic visual displacement of a polymer solution for enhanced oil recovery: Hele-Shaw cell experiments and computational simulation

“…The parameters that did not change in the simulation were porosity, permeability, cell geometry, quantity of embedded fluids, injection times, atmospheric pressure, and temperature. The bidimensional image results obtained during the experiment and computational simulation were compared by counting pixels, finding that the displaced fluid was similar in both cases [34,35], where the computational program found a solution singularity, the results of which are shown in Fig. 8.…”

Applications of digital core analysis and hydraulic flow units in petrophysical characterization

“…X-ray CT can achieve wider field of view and very high resolution in 3D space (Ge et al, 2015). Despite the lower resolution in comparison with µxCT and SEM images, the thin section images have other advantages such as a wider range of view, saved time and cheaper availability for petrophysical studies, such as pore microstructure (Desbois et al, 2011;Rabbani et al, 2014a;Borazjani et al, 2016;Gundogar et al, 2016;Rabbani et al, 2016;Xiao et al, 2016), mineral recognition and classification (Hofmann et al, 2013;Asmussen et al, 2015;Izadi et al, 2015;Izadi et al, 2017b), specific surface area (Rabbani and Jamshidi, 2014;Rabbani et al, 2014b), elastic modulus (Arns et al, 2002;Dvorkin et al, 2011;Madonna et al, 2012;Saxena and Mavko, 2016), rock type determination (Mynarczuk, 2010;Mynarczuk et al, 2013;Ge et al, 2015;Mollajan et al, 2016), pore-grain analysis (Rabbani and Jamshidi, 2014;Rabbani et al, 2014b;Song et al, 2016), flowing property (Peng et al, 2016;Wang et al, 2016b). Using thin section images, the interconnected pore structure can be marked out visually as they are filled by color epoxy resin.…”

“…Recently, the methodology to reconstruct pore network based on 2D thin section was developed for conventional sandstone reservoir using constant coordination number and stereological correction factor (Baveye et al, 2010;Houston et al, 2013a). For carbonate rock, which is a multiple porosity system, a fast but reliable method was proposed by Peng et al (2016), through the relationship of K 2D (estimated by thin section) and K 3D (obtained by micro-CT scan and simulation) that are defined as:…”