Computing Effective Elastic Properties From Microtomographic Images: An Efficient 3D Digital Rock Workflow

Abstract

Digital rock physics (DRP), using high‐resolution 3D microtomographic rock images, is widely employed to predict elastic and transport properties of subsurface formations. Accurate microstructural characterization is often hindered by limited image resolution and overlapping grayscale values of minerals. Consequently, DRP simulations are often restricted to samples with simple structures and mineralogies. We propose a practical workflow that leverages a voxel‐resolved finite element solver to rapidly predict effective elastic properties from segmented 3D digital rock images, incorporating mineral phases and their corresponding volume fractions. We selected Bentheimer and Doddington sandstones to assess the accuracy and performance of the workflow and to demonstrate its practical application. Including feldspar and clay in the solver, despite the grayscale similarities to quartz, significantly improved the predictions of elastic moduli and produced modulus‐porosity trends that closely matched laboratory ultrasonic measurements for Bentheimer sandstone. Uncertainty and sensitivity analyses evaluated how mineral volume fractions, input mineral moduli, grain contact size, and mineralogy, including aspect ratios and coordination numbers, impacted the predictions. The simulation results reveal that the dominant source of uncertainty is attributed to the level of sophistication of the modeling approach–whether a single‐mineral (quartz only) or multi‐mineral segmented model is applied. The quartz‐only model exhibited errors of up to 36% in bulk modulus and 22% in shear modulus relative to laboratory measurements. These errors were reduced to 22% and 9.9%, respectively, when a proper mineral‐weighted heterogeneous segmentation was employed. These results demonstrate that the workflow can be extended to estimate rock physical properties beyond elastic parameters.