Image-based rock typing is carried out to quantitatively assess the heterogeneity of the reservoir specimen at a pore scale by classifying an image of a heterogeneous rock sample into a number of relatively homogeneous regions. Image-based rock typing can be treated as a special application of texture classification in the field of the digital core. In conventional texture classification algorithms, a single size window or a set of windows with different size are applied to scan the image to extract various local structure features, and then a classification algorithm is used to classify the image into different regions where each region possesses unique structure features. Due to the local features are extracted within a window, it is still challenging to identify the class of the voxels close to the boundary between different regions. In this paper, a rock typing method is proposed, which uses the geometry features of the grains instead of local structure features for classification. Inspired by the fact that in some cases the heterogeneity of the reservoir is mainly affected by the sedimentary process, which means each rock type always has certain specific grain features such as size and sphericity. To this kind of rock samples, the proposed grain-based rock typing algorithm can effectively address the boundary ambiguousness problem. In this study, the grains of the rock sample are partitioned firstly, and then their geometry features are calculated. Then a support vector machine algorithm is used to classify these grains into different rock types. Finally, the pore voxels are given a rock type, which is identical to its nearest grain. The proposed method shows excellent performance in the heterogeneous samples whose grains are available to be partitioned and distinguishable.

通过将非均质岩石样品的图像分类为多个相对均质的区域，进行基于图像的岩石分型以定量评估储层标本在孔隙尺度上的非均质性。基于图像的岩石分型可以被认为是数字核心领域中纹理分类的一种特殊应用。在传统的纹理分类算法中，使用单个大小的窗口或一组大小不同的窗口来扫描图像以提取各种局部结构特征，然后使用分类算法将图像分类为不同的区域，每个区域具有唯一的特征。结构特点。由于在一个窗口中提取了局部特征，因此识别接近不同区域之间边界的体素的类别仍然是一项挑战。本文提出了一种岩石分型方法，该方法利用晶粒的几何特征代替局部结构特征进行分类。受以下事实的启发：在某些情况下，储层的非均质性主要受沉积过程的影响，这意味着每种岩石类型始终具有某些特定的颗粒特征，例如大小和球形。对于这种岩石样品，提出的基于晶粒的岩石分型算法可以有效地解决边界不明确的问题。在这项研究中，首先对岩石样品的颗粒进行分区，然后计算其几何特征。然后使用支持向量机算法将这些颗粒分类为不同的岩石类型。最后，为孔隙体素指定了一种岩石类型，该类型与其最近的晶粒相同。