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Numerical computation from high-resolution 3-D microtomographic 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. The physical size of sample is often too small to present heterogeneities at a larger scale and the image resolution is insufficient to characterize the details of rocks. Also, the grayscale values of different minerals in microtomographic images are often similar, and previous attempts to segment them as separate phases are not very successful. 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). Based on a set of images, we obtain a suite of postcomputation corrections to compensate for the effects of sample size and resolution of the microtomographic images. Furthermore, we build a segmentation workflow that effectively detects feldspar and clay minerals, despite their grayscale similarity to quartz. A moduli-porosity trend is derived from the subsamples of the original digital images. Bulk moduli agree well with the ultrasonic measurements on the dry samples at 40 MPa. 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.

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