RESEARCH ON AUTOMATIC THRESHOLD GENERATION METHOD FOR SHALE SLICE HOLE SEGMENTATION
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摘要:
随着页岩气勘探开发领域的快速发展，页岩储层的微观结构表征及分析技术显得愈发重要。其中基于阈值分割法获取页岩孔隙结构参数是目前页岩微观结构表征的一种重要手段。现有的阈值分割方法主要有最大类间方差法、最大熵阈值分割法等，它们在各种图像分割任务上都取得了不错的成绩。然而，在页岩孔隙分割问题上，它们均存在耗时较长且不能有效分离薄片扫描电镜图像中的孔隙和基质等元素的问题。页岩薄片孔缝分割的自动阈值生成方法能根据页岩薄片的不同特点，自适应地快速生成对应图像的最佳灰度阈值，自动识别页岩孔隙和基质等地质元素。在足206井的页岩扫描电镜图像上进行了实验，与传统方法进行对比，实验结果表明，页岩薄片孔缝分割的自动阈值生成方法能准确实现孔隙和基质等元素的分离，在各类图像上都能高效地自动生成最佳灰度阈值，为页岩微观图像孔隙结构定量分析提供可靠基础。
Abstract:With the rapid progress of shale gas exploration and exploitation, microstructural characteristics and their analysis techniques have become more and more important. Among them, to acquire the shale pore structure parameters based on the threshold segmentation method is an key mean for shale microstructural characterization. The existing methods mainly include the maximum between-class variance method and the maximum entropy threshold segmentation method. They have gained good results in various image segmentation. However, they are all quite time-consuming and cannot effectively separate the pores from matrix in the SEM images of thin slices sometimes. In this paper, an automatic threshold generation method for shale slices and fissures segmentation is proposed, which can adaptively and quickly generate the optimal grayscale threshold for the related image according to different characteristics of shale slices, and automatically recognize shale pores and matrix as well as other geological elements. In this paper, experiments were conducted for the shale scanning electron microscopy images from the Well 206, and comparison is made with the results from traditional methods. The experimental results show that the algorithm of this paper can automatically generate the optimal grayscale threshold on various images, and accurately identify such elements as pores and matrix. Therefore, the automatic threshold generation method for shale slice hole segmentation introduced in this paper can efficiently generate the optimal gray threshold of shale slice scanning electron microscope image and provide a reliable basis for quantitative analysis of pore structure on shale microscopic images.
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