Summary Core measurements are used for rock classification and improved formation evaluation in both cored and noncored wells. However, the acquisition of such measurements is time-consuming, delaying rock classification efforts for weeks or months after core retrieval. On the other hand, well-log-based rock classification fails to account for rapid spatial variation of rock fabric encountered in heterogeneous and anisotropic formations due to the vertical resolution of conventional well logs. Interpretation of computed tomography (CT) scan data has been identified as an attractive and high-resolution alternative for enhancing rock texture detection, classification, and formation evaluation. Acquisition of CT scan data is accomplished shortly after core retrieval, providing high-resolution data for use in petrophysical workflows in relatively short periods of time. Typically, CT scan data are used as two-dimensional (2D) cross-sectional images, which is not suitable for quantification of three-dimensional (3D) rock fabric variation, which can increase the uncertainty in rock classification using image-based rock-fabric-related features. The methods documented in this paper aim to quantify rock-fabric-related features from whole-core 3D CT scan image stacks and slabbed whole-core photos using image analysis techniques. These quantitative features are integrated with conventional well logs and routine core analysis (RCA) data for fast and accurate detection of petrophysical rock classes. The detected rock classes are then used for improved formation evaluation. To achieve the objectives, we conducted a conventional formation evaluation. Then, we developed a workflow for preprocessing of whole-core 3D CT-scan image stacks and slabbed whole-core photos. Subsequently, we used image analysis techniques and tailor-made algorithms for the extraction of image-based rock-fabric-related features. Then, we used the image-based rock-fabric-related features for image-based rock classification. We used the detected rock classes for the development of class-based rock physics models to improve permeability estimates. Finally, we compared the detected image-based rock classes against other rock classification techniques and against image-based rock classes derived using 2D CT scan images. We applied the proposed workflow to a data set from a siliciclastic sequence with rapid spatial variations in rock fabric and pore structure. We compared the results against expert-derived lithofacies, conventional rock classification techniques, and rock classes derived using 2D CT scan images. The use of whole-core 3D CT scan image-stacks-based rock-fabric-related features accurately captured changes in the rock properties within the evaluated depth interval. Image-based rock classes derived by integration of whole-core 3D CT scan image-stacks-based and slabbed whole-core photos-based rock-fabric-related features agreed with expert-derived lithofacies. Furthermore, the use of the image-based rock classes in the formation evaluation of the evaluated depth intervals improved estimates of petrophysical properties such as permeability compared to conventional formation-based permeability estimates. A unique contribution of the proposed workflow compared to the previously documented rock classification methods is the derivation of quantitative features from whole-core 3D CT scan image stacks, which are conventionally used qualitatively. Furthermore, image-based rock-fabric-related features extracted from whole-core 3D CT scan image stacks can be used as a tool for quick assessment of recovered whole core for tasks such as locating best zones for extraction of core plugs for core analysis and flagging depth intervals showing abnormal well-log responses.

中文翻译：

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集成 3D 体积计算机断层扫描图像数据与常规测井记录以检测岩石物理类别

总结 岩心测量用于岩石分类和改进的取芯井和非取芯井的地层评估。然而，获取此类测量值非常耗时，在取芯后将岩石分类工作延迟数周或数月。另一方面，由于常规测井的垂直分辨率，基于测井的岩石分类无法解释在异质和各向异性地层中遇到的岩石结构的快速空间变化。计算机断层扫描 (CT) 扫描数据的解释已被确定为增强岩石纹理检测、分类和地层评估的有吸引力的高分辨率替代方案。CT 扫描数据的采集是在取芯后不久完成的，在相对较短的时间内提供用于岩石物理工作流程的高分辨率数据。通常，CT扫描数据被用作二维（2D）横截面图像，不适合量化三维（3D）岩石结构变化，这会增加使用基于图像的岩石分类的不确定性。面料相关的功能。本文中记录的方法旨在使用图像分析技术从全芯 3D CT 扫描图像堆栈和平板全芯照片中量化与岩石织物相关的特征。这些定量特征与常规测井记录和常规岩心分析 (RCA) 数据相结合，可快速准确地检测岩石物理类别。然后将检测到的岩石类别用于改进地层评估。为实现目标，我们进行了常规的编队评估。然后，我们开发了一个用于预处理全核 3D CT 扫描图像堆栈和整核切片照片的工作流程。随后，我们使用图像分析技术和量身定制的算法来提取基于图像的岩石织物相关特征。然后，我们使用基于图像的岩石织物相关特征进行基于图像的岩石分类。我们使用检测到的岩石类别来开发基于类别的岩石物理模型，以改进渗透率估计。最后，我们将检测到的基于图像的岩石类别与其他岩石分类技术以及使用 2D CT 扫描图像派生的基于图像的岩石类别进行了比较。我们将建议的工作流程应用于来自硅碎屑序列的数据集，该序列在岩石结构和孔隙结构中具有快速的空间变化。我们将结果与专家衍生的岩相、常规岩石分类技术和使用 2D CT 扫描图像衍生的岩石类别进行了比较。使用基于全芯 3D CT 扫描图像堆栈的岩石织物相关特征准确捕捉评估深度区间内岩石特性的变化。基于全岩心 3D CT 扫描图像堆栈和基于板状全岩心照片的岩石结构相关特征的整合衍生的基于图像的岩石类别与专家衍生的岩相一致。此外，与传统的基于地层的渗透率估计相比，在评估的深度区间的地层评估中使用基于图像的岩石类别改进了对岩石物理特性（例如渗透率）的估计。与先前记录的岩石分类方法相比，所提出的工作流程的一个独特贡献是从全芯 3D CT 扫描图像堆栈中推导定量特征，这些特征通常用于定性。此外，从全岩心 3D CT 扫描图像堆栈中提取的基于图像的岩石结构相关特征可用作快速评估回收的全岩心的工具，用于定位提取岩心塞进行岩心分析的最佳区域和标记显示异常测井响应的深度间隔。