Zhong Jinzhi, Meng Yanjun, Liu Zehao
School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China.
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 0330024, China.
ACS Omega. 2024 Jun 17;9(26):28611-28625. doi: 10.1021/acsomega.4c02692. eCollection 2024 Jul 2.
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.
作为一种非常规天然气资源，致密砂岩气主要储存在岩石砂粒之间的微小孔隙中。深入了解致密砂岩储层的孔隙结构特征对于制定开采计划和提高气田开发效率至关重要。砂岩中的孔隙结构和矿物成分可以通过薄片直接观察到。然而，以往砂岩薄片自动识别方法存在一定局限性，包括识别速度慢、准确率低以及颗粒尺寸识别困难等问题。为了实现更准确、便捷的矿物成分识别，本研究引入了一种基于增强型DeepLab V3 Plus模型构建的多通道识别方法。首先，将所有224×224×3的正交偏光（XPL）和十字偏光（CPL）砂岩薄片合并为224×224×6的多通道（六个通道）图像。随后，使用多个网络对三个偏振数据集进行训练，并通过筛选选择最优的语义分割架构和数据集。接着，将注意力机制嵌入语义分割网络以提高识别准确率。最后，计算矿物尺寸，以便对砂岩薄片进行更精确的分类和命名。结果表明，新方法在识别准确率方面表现更优，平均像素精度（Mean PA）达到89.8%，平均交并比（Mean IOU）达到81.2%。这种新方法更高的细节程度能够更精确地识别矿物成分和孔隙结构，这是评估储层和预测油气产量的关键环节。它还可以为识别和分类其他具有相似成分的薄片提供新的思路。
