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Quantification of Uncertainty and Best Practice in Computing Interfacial Curvature from Complex Pore Space Images

机译:从复杂孔隙空间图像计算界面曲率的不确定度和最佳实践量化

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摘要

Recent advances in high-resolution three-dimensional X-ray CT imaging have made it possible to visualize fluid configurations during multiphase displacement at the pore-scale. However, there is an inherited difficulty in image-based curvature measurements: the use of voxelized image data may introduce significant error, which has not—to date—been quantified. To find the best method to compute curvature from micro-CT images and quantify the likely error, we performed drainage and imbibition direct numerical simulations for an oil/water system on a bead pack and a Bentheimer sandstone. From the simulations, local fluid configurations and fluid pressures were obtained. We then investigated methods to compute curvature on the oil/water interface. The interface was defined in two ways; in one case the simulated interface with a sub-resolution smoothness was used, while the other was a smoothed interface extracted from synthetic segmented data based on the simulated phase distribution. The curvature computed on these surfaces was compared with that obtained from the simulated capillary pressure, which does not depend on the explicit consideration of the shape of the interface. As distinguished from previous studies which compared an average or peak curvature with the value derived from the measured macroscopic capillary pressure, our approach can also be used to study the pore-by-pore variation. This paper suggests the best method to compute curvature on images with a quantification of likely errors: local capillary pressures for each pore can be estimated to within 30% if the average radius of curvature is more than 6 times the image resolution, while the average capillary pressure can also be estimated to within 11% if the average radius of curvature is more than 10 times the image resolution.
机译:高分辨率三维X射线CT成像技术的最新进展使得可视化孔隙尺寸的多相位移过程中的流体构造成为可能。但是,在基于图像的曲率测量中存在一个固有的困难:使用体素化的图像数据可能会引入显着的误差,迄今为止,该误差尚未被量化。为了找到从微型CT图像计算曲率并量化可能误差的最佳方法,我们对珠状填料和Bentheimer砂岩上的油/水系统进行了排水和吸收直接数值模拟。通过模拟,获得了局部流体构造和流体压力。然后,我们研究了在油/水界面上计算曲率的方法。接口有两种定义方式:在一种情况下,使用具有亚分辨率平滑度的模拟接口,而另一种是基于模拟相位分布从合成分段数据中提取的平滑接口。将在这些表面上计算出的曲率与从模拟毛细管压力获得的曲率进行了比较,这并不取决于界面形状的明确考虑。与先前的研究(将平均曲率或峰值曲率与所测得的宏观毛细管压力得出的值进行比较)不同,我们的方法还可用于研究逐孔变化。本文提出了一种在图像上计算曲率并量化可能误差的最佳方法:如果平均曲率半径大于图像分辨率的6倍,则每个孔的局部毛细压力可估计在30%以内。如果平均曲率半径大于图像分辨率的10倍,则还可估计压力在11%以内。

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