Deep mineralogical segmentation of thin section images based on QEMSCAN maps

TL;DR

This paper introduces a CNN-based method using U-Net architecture to automatically segment mineral phases in thin section images, mimicking QEMSCAN maps efficiently and at lower cost, with high accuracy and good generalization.

Contribution

The study presents a novel application of U-Net for mineralogical segmentation of thin sections, demonstrating effective generalization to unseen rock facies and addressing resolution challenges.

Findings

Segmentation accuracy depends on image resolution and rock texture diversity.

High correlation (R^2 > 0.97) for known facies, and R^2 > 0.88 for unseen facies.

The method provides a low-cost, automated alternative to QEMSCAN mapping.

Abstract

Interpreting the mineralogical aspects of rock thin sections is an important task for oil and gas reservoirs evaluation. However, human analysis tend to be subjective and laborious. Technologies like QEMSCAN(R) are designed to automate the mineralogical mapping process, but also suffer from limitations like high monetary costs and time-consuming analysis. This work proposes a Convolutional Neural Network model for automatic mineralogical segmentation of thin section images of carbonate rocks. The model is able to mimic the QEMSCAN mapping itself in a low-cost, generalized and efficient manner. For this, the U-Net semantic segmentation architecture is trained on plane and cross polarized thin section images using the corresponding QEMSCAN maps as target, which is an approach not widely explored. The model was instructed to differentiate occurrences of Calcite, Dolomite, Mg-Clay Minerals, Quartz, Pores and the remaining mineral phases as an unique class named "Others", while it was validated on rock facies both seen and unseen during training, in order to address its generalization capability. Since the images and maps are provided in different resolutions, image registration was applied to align then spatially. The study reveals that the quality of the segmentation is very much dependent on these resolution differences and on the variety of learnable rock textures. However, it shows promising results, especially with regard to the proper delineation of minerals boundaries on solid textures and precise estimation of the minerals distributions, describing a nearly linear relationship between expected and predicted distributions, with coefficient of determination (R^2) superior to 0.97 for seen facies and 0.88 for unseen.