Quantitative Identification of Sandstone Lithology Based On Thin-Section Micrographs Using the U-net and U-net++ Semantic Segmentation Network

Quantitative identification of sandstone microscopic images is an essential task for sandstone reservoir characterization. The widely-used classical Gazzi-Dickinson point-counting method can be subjective, inconsistent and time-consuming. Furthermore, by directly putting labeled microscopic images of all rock types into image recognition models for training, most previous studies did not address the petrographic principle of artificial identification. In this study, U-Net and U-Net++ semantic segmentation networks that incorporated the sandstone petrographic principle in quantitative identification of sandstone was introduced. Automatic identification of sandstone microscopic images requires prior knowledge learned from the identified sandstones with similar compositions. First, hundreds of thin-sections of typical sandstone reservoirs were selected from several key petroleum basins in China. Second, one-to-one single and orthogonal polarized images were taken for them. Third, the annotation software was used to label the type of each skeleton grain, including quartz, feldspar, lithic fragment and pore. Finally, 480 sets of data, each of which includes single and orthogonal polarized images and their ".json" format annotation results, were obtained for training and testing of the U-Net model to quantitatively analyze sandstone microscopic images. Within the 480 sets of data, 6798 sandstone skeleton grains, including 4542 quartzes, 796 feldspars, 1248 lithic fragments and 212 pores were labeled. The sandstone thin-section quantitative identification model trained by 392 data sets achieved a training accuracy of 96% with the intersection over union at 78% for quartz, and a training accuracy of 88% with the intersection over union at 56% for lithic fragments. The remaining 88 data sets were used for testing, and the accuracy was 87% with its intersection over union at 74% for quartz and a training accuracy of 77% with the intersection over union at 54% for lithic fragments. As a classic fully convolutional network that excels in processing medical images, the U-Net or U-Net++ semantic segmentation network has also performed very well in quantitative identification of sandstone microscopic images. After the proportion of each sandstone skeleton grain has been identified, the simple subdivision descriptive petrographic classification of the sandstone was determined according to the classic Dickinson sandstone taxonomic criteria. In other words, most current deep learning algorithms classify sandstones at the bulk rock level, but this U-Net model has been extended to the mineral level for comprehensive identification. Our vision-based sandstone lithology identification model has not only improved the accuracy of artificial identification but also reduced the instability and subjectivity of the traditional manual processing and expert decision-making approach. In the future, we plan to increase the number and coverage of labeled thin-section images to evaluate the impact on the accuracy and consistency of the U-Net or U-Net++ model, and to expand the approach to identify other terrigenous clastic rock. Furthermore, we hope to improve the capability of the model to identify grains, such as monocrystalline and polycrystalline quartz from "quartz", K-feldspar and plagioclase from "feldspar", and igneous, metamorphic and sedimentary lithic fragments from "lithic fragments".