Characterizing rock proprieties is crucial in the oilfield to evaluate hydrocarbon reserves. Several studies showed a high correlation between rock properties and textures. Therefore, we propose integrating texture information in the images to identify precisely the most representative textures in highly heterogeneous rocks to estimate their properties. First, we implemented a steerable pyramid decomposition to extract the texture features. Then, those parameters were used as input for the Self-organizing map to classify the textures. Finally, by applying several models and comparing their results, we suggested the best approach to implement for texture classification.

中文翻译：

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3D X 射线微计算机断层扫描图像的无监督纹理分类

表征岩石特性对于油田评估碳氢化合物储量至关重要。多项研究表明岩石特性和纹理之间存在高度相关性。因此，我们建议整合图像中的纹理信息，以精确识别高度异质岩石中最具代表性的纹理，以估计其特性。首先，我们实现了可操纵金字塔分解来提取纹理特征。然后，这些参数用作自组织映射的输入以对纹理进行分类。最后，通过应用多个模型并比较它们的结果，我们提出了实现纹理分类的最佳方法。