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首页> 外文期刊>Transport in Porous Media >Three-Dimensional Stochastic Characterization of Shale SEM Images
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Three-Dimensional Stochastic Characterization of Shale SEM Images

机译：页岩SEM图像的三维随机表征

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Complexity in shale-gas reservoirs lies in the presence of multiscale networks of pores that vary from nanometer to micrometer scale. Scanning electron microscope (SEM) and atomic force microscope imaging are promising tools for a better understanding of such complex microstructures. Obtaining 3D shale images using focused ion beam-SEM for accurate reservoir forecasting and petrophysical assessment is not, however, currently economically feasible. On the other hand, high-quality 2D shale images are widely available. In this paper, a new method based on higher-order statistics of a porous medium (as opposed to the traditional two-point statistics) is proposed in which a single 2D image of a shale sample is used to reconstruct stochastically equiprobable 3D models of the sample. Because some pores may remain undetected in the SEM images, data from other sources, such as the pore-size distribution obtained from nitrogen adsorption data, are integrated with the overall pore network using an object-based technique. The method benefits from a recent algorithm, the cross- correlation-based simulation, by which high-quality, unconditional/conditional realizations of a given sample porous medium are produced. To improve the ultimate 3D model, a novel iterative algorithm is proposed that refines the quality of the realizations significantly. Furthermore, a new histogram matching, which deals with multimodal continuous properties in shale samples, is also proposed. Finally, quantitative comparison is made by computing various statistical and petrophysical properties for the original samples, as well as the reconstructed model.
机译：页岩气藏的复杂性在于存在从纳米级到微米级变化的多尺度孔网。扫描电子显微镜（SEM）和原子力显微镜成像是有希望的工具，可以更好地理解这种复杂的微观结构。然而，使用聚焦离子束-SEM获得3D页岩图像以进行准确的储层预测和岩石物理评估尚不经济。另一方面，高质量的2D页岩图像可广泛获得。在本文中，提出了一种基于多孔介质的高阶统计量（与传统的两点统计量相反）的新方法，其中将页岩样品的单个2D图像用于重建随机等概率的3D模型。样品。由于某些孔可能仍无法在SEM图像中检测到，因此使用基于对象的技术将来自其他来源的数据（例如从氮吸附数据获得的孔径分布）与整个孔网络集成在一起。该方法得益于最新的算法，即基于互相关的仿真，通过该算法可以生成给定样本多孔介质的高质量，无条件/有条件的实现。为了改进最终的3D模型，提出了一种新颖的迭代算法，该算法显着改善了实现的质量。此外，还提出了一种新的直方图匹配方法，用于处理页岩样品中的多峰连续性。最后，通过计算原始样品的各种统计和岩石物理特性以及重建的模型进行定量比较。

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