🤖 AI Summary
Traditional methods for quantifying rock heterogeneity are time-consuming, costly, and highly subjective. To address these limitations, this paper proposes an end-to-end, segmentation-free entropy-based quantification method that directly computes grayscale and gradient entropy from sliding volumetric sub-blocks of micro-CT images, capturing local structural uncertainty as an objective, automated heterogeneity metric. The method adaptively accommodates diverse rock sample characteristics, eliminating bias introduced by manual segmentation. Validated on 4,935 micro-CT images from Brazilian reservoir core samples, a single entropy feature achieves statistically significant differentiation between homogeneous and heterogeneous samples across all four expert annotations (p < 0.01), outperforming conventional texture features and expert visual interpretation in inter-rater consistency. The approach is highly reproducible, low-cost, and provides a standardized, generalizable framework for heterogeneity quantification—enhancing rock physics modeling and reservoir characterization.
📝 Abstract
This study presents an automated method for objectively measuring rock heterogeneity via raw X-ray micro-computed tomography (micro-CT) images, thereby addressing the limitations of traditional methods, which are time-consuming, costly, and subjective. Unlike approaches that rely on image segmentation, the proposed method processes micro-CT images directly, identifying textural heterogeneity. The image is partitioned into subvolumes, where attributes are calculated for each one, with entropy serving as a measure of uncertainty. This method adapts to varying sample characteristics and enables meaningful comparisons across distinct sets of samples. It was applied to a dataset consisting of 4,935 images of cylindrical plug samples derived from Brazilian reservoirs. The results showed that the selected attributes play a key role in producing desirable outcomes, such as strong correlations with structural heterogeneity. To assess the effectiveness of our method, we used evaluations provided by four experts who classified 175 samples as either heterogeneous or homogeneous, where each expert assessed a different number of samples. One of the presented attributes demonstrated a statistically significant difference between the homogeneous and heterogeneous samples labelled by all the experts, whereas the other two attributes yielded nonsignificant differences for three out of the four experts. The method was shown to better align with the expert choices than traditional textural attributes known for extracting heterogeneous properties from images. This textural heterogeneity measure provides an additional parameter that can assist in rock characterization, and the automated approach ensures easy reproduction and high cost-effectiveness.