Numerical computation from high resolution 3D microtomographic (micro-CT) images of rocks (known as digital rock physics) has the potential to predict elastic properties more accurately. However, successful examples are limited to samples with simple structure and mineralogy. Here, we propose a practical digital rock physics workflow for somewhat more complex and ubiquitous rocks, namely, sandstones that contain mostly quartz and a small fraction of dispersed clay (known as arenites). We build a segmentation workflow that effectively detects feldspar and clay minerals from microCT images, despite their greyscale similarity to quartz. We apply a suite of post-computation corrections to compensate for the effects of sample size and resolution of the micro-CT images on the computed moduli. To compare the computation results against laboratory ultrasonic measurements, we divide the digital samples into subsamples to reconstruct the porosity-moduli trends and compare this trend against laboratory measurements. Computed bulk moduli agree well with the ultrasonic measurements on the dry samples at 40MPa. Computed shear moduli remain overestimated, which is likely caused by poor knowledge of the mineral stiffness. We compensate for this effect using a heuristic correction to the matrix moduli. The final version of the workflow provides accurate elastic moduli trends with porosity and clay content based on only two samples of Bentheimer sandstone.

Open-Access Online Publication: March 03, 2023