Multichannel Sandstone Thin Sections Identification Based on Improved DeepLab V3 Plus Neural Network

As an unconventional natural gas resource, tight sandstone gas is primarily stored in the minuscule pores between rocky sand grains. A thorough understanding of the pore structure characteristics of tight sandstone reservoirs is essential for formulating an extraction plan and enhancing the efficiency of gas field development. The pore structure and mineral composition in the sandstone can be directly observed by thin sections. Nevertheless, previous approaches for the automated identification of sandstone thin sections exhibit certain limitations including slow identification, low accuracy, and challenges in the recognition of particle sizes. To achieve more accurate and convenient mineral component identification, this study introduces a multichannel identification method built upon the enhanced DeepLab V3 Plus model. Initially, all 224 × 224 × 3 cross-polarized light (CPL) and orthogonal polarized light (XPL) sandstone thin sections were amalgamated into 224 × 224 × 6 multichannel (six channels) images. Subsequently, multiple networks were employed to train the three polarized data sets, and the optimal semantic segmentation architecture and data set were selected through filtering. Following that, embedding the attention mechanism into the semantic segmentation network enhanced the identification accuracy. Ultimately, mineral sizes were calculated to enable more precise classification and naming of sandstone thin sections. The results show that the new method outperforms in terms of recognition accuracy, achieving 89.8% for Mean PA and 81.2% for Mean IOU. The novel approach's enhanced level of detailing enables more precise identification of mineral composition and pore structure, a crucial aspect in evaluating reservoirs and predicting oil and gas production. It can also provide new insights into identifying and categorizing other thin sections with similar compositions.