图像处理和机器学习在薄片切割样品岩性识别、分类和定量中的应用

M. Caja, A. Peña, J. R. Campos, Laura García Diego, J. Tritlla, T. Bover‐Arnal, J. Martín‐Martín
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引用次数: 6

摘要

岩屑提供了精确观察被钻岩石的机会。泥浆录井员和井场地质学家通常在钻井平台上使用常规双目显微镜对钻屑进行初步描述。经过这种初步描述后,通常将岩屑袋储存在仓库中,样品很少再检查一次。岩屑为地质学家提供了地质对比所需的地层岩性信息,了解储层质量、密封和烃源岩,也可以作为岩石物理学家的输入。在这项研究中,我们正在测试一种方法,以识别,分类和量化使用薄片图像切割样品中存在的岩性。该方法包括样品制备(洗涤、干燥和薄片岩屑制备)、图像采集(获得整个薄片千兆像素高分辨率显微镜图像)、虚拟显微镜(识别岩性)和自动图像分析(执行监督机器学习岩性分类)。虚拟显微镜可以在所有研究薄片中识别出四种主要岩性:石英岩(包括松散的石英颗粒)、粉砂岩、粘土岩和碳酸盐。通过图像分析,可以对两个致密气藏的16个钻切样品进行岩性分类和量化。这种创新的方法允许使用虚拟显微镜快速识别岩性,并通过图像分析和监督机器学习对其进行分类和量化。这种方法被广泛使用,因为开源软件被用于虚拟显微镜和图像分析。算法训练和模型生成相对较快,通过虚拟显微镜对其性能或准确率进行定性评价,分类效果良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Processing and Machine Learning Applied to Lithology Identification, Classification and Quantification of Thin Section Cutting Samples
Cuttings provide the opportunity to precisely look at the rock that has been drilled. A preliminary drill cuttings description is commontly performed by mudloggers and wellsite geologists using conventional binocular microscope at the drilling rig. After this preliminary description, often the bags of cuttings are stored in a warehouse and samples are seldom examined back again. Cuttings give the geologist information about the formation lithology needed for geologic correlation, understanding about reservoir quality, seals and source rocks, and can also be an input for the petrophysicist. In this study, we are testing a methodology to identify, classify and quantify lithologies present in cutting samples using thin section images. The method includes sample preparation (washing, drying and thin section cuttings preparation), image acquisition (to obtain whole thin section gigapixel high resolution microscopy images), virtual microscopy (to identify lithologies) and automatic image analysis (to perform supervised machine learning lithology clasiffication). Virtual microscopy allowed the identification of four main lithologies in all the studied thin sections: quartzites (including loose quartz grains), siltstones, claystones and carbonates. Image analysis allowed the classification and quantification of the identified lithologies in 16 drill cutting samples from two tight gas reservoirs. This innovative methodology allowed the fast identification of lithologies using virtual microscopy and their classification and quantification by image analysis and supervised machine learning. This approach is widely accessible as open source software was used for virtual microscopy and image analysis. Algorithm training and model generation was relativelly fast, and its performance or accuracy was qualititavely evaluated by virtual microscopy with good classification results.
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