Enhancement of thin-section image using super-resolution method with application to the mineral segmentation and classification in tight sandstone reservoir

Liu, Ye; Zhang, Qidi; Zhang, Nan; Lv, Jintao; Gong, Meichen; Cao, Jie

doi:10.1016/j.petrol.2022.110774

Cited by 7 publications

(1 citation statement)

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“…High-frequency information can be well preserved by introducing deep learning methods, which are widely used in nature images, medical images, satellite aerial images, military fields, and robotics [2][3][4][5]. This is also true in the field of geophysics [6][7][8][9][10]. However, the traditional pixel loss function in deep learning methods often results in the generated images looking too smooth and lacking details, which predisposes them to being less capable of reconstructing texture in high-frequency images such as thin slices.…”

Section: Introductionmentioning

confidence: 99%

OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method

Liu,

Xu,

Tang

et al. 2024

Applied Sciences

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With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section.

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