Super-resolution in thin section of lacustrine shale reservoirs and its application in mineral and pore segmentation

Abstract

Lacustrine shale reservoirs present intricate attributes such as the prevalence of lamination, rapid sedimentary phase transitions, and pronounced heterogeneity. These factors introduce substantial challenges in analyzing and comprehending reservoir characteristics. Thin-section imaging offers a direct medium to observe these traits, yet the intrinsic compromise between image resolution and field of view impedes the concurrent capture of comprehensive details and contextual overview. This study delves into the application of super-resolution (SR) techniques to augment the segmentation of thin-section images from lacustrine shale, an unconventional reservoir. SR application furnishes high-resolution images, facilitating a robust analysis of morphology, texture, edge properties, and target classification. Utilizing data from the lacustrine shale reservoir of the Ordos Basin, we evaluate our methodology and assess the impact of SR enhancement on segmentation. Quantitative results indicate significant improvements, with the Jaccard index for shale increasing from 0.4790 (Low-Resolution) to 0.7803 (ESRGAN) in the Y channel of the YCrCb color space after level set segmentation, exemplifying the efficacy of SR in shale gas and oil reservoirs. This research underscores the necessity to consider lacustrine shale's unique features while formulating and implementing SR techniques for improved information extraction. Furthermore, it highlights SR's potential for propelling future research and industry-specific applications.