Entropy-based measure of rock sample heterogeneity derived from micro-CT images

TL;DR

This paper introduces an automated, entropy-based method for quantifying rock heterogeneity directly from micro-CT images, improving objectivity and efficiency over traditional subjective and segmentation-based techniques.

Contribution

The study presents a novel direct-processing approach using entropy to measure heterogeneity, validated on a large dataset and compared with expert classifications.

Findings

Entropy-based attributes correlate strongly with structural heterogeneity.

The method aligns better with expert assessments than traditional textural measures.

One attribute showed a statistically significant difference across all expert labels.

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.