Pore-network extraction from micro-computerized-tomography images
Pore-network extraction from micro-computerized-tomography images
复制标题
DOI：
10.1103/physreve.80.036307
复制
发表时间：
2009-09-01
影响因子：
2.4
通讯作者：
Blunt, Martin J.
中科院分区：
文献类型：
--
作者：
Dong, Hu;Blunt, Martin J.
网络模型通过喉道连接的孔隙晶格来表示岩石的空隙空间，一旦知道孔隙的几何形状和润湿性，就可以预测相对渗透率。微电脑断层扫描提供了孔隙空间的三维图像。然而，这些图像不能直接输入到网络模型中。本文提出了一种改进的极大球算法，扩展了Silin和Patzek的工作[D]。Silin和T. Patzek，物理学报，371,336(2006)]，开发了从孔隙空间图像中提取具有参数化几何形状和互联性的简化孔隙和喉道网络的方法。计算孔隙网络的配位数、孔喉尺寸分布等参数，并与其他方法提取的网络基准数据、实验数据以及直接计算底层图像上的渗透率和地层因子进行比较。在大多数情况下达成了良好的协议，允许从各种岩石类型中获得的网络用于预测建模。
Network models that represent the void space of a rock by a lattice of pores connected by throats can predict relative permeability once the pore geometry and wettability are known. Micro-computerized-tomography scanning provides a three-dimensional image of the pore space. However, these images cannot be directly input into network models. In this paper a modified maximal ball algorithm, extending the work of Silin and Patzek [D. Silin and T. Patzek, Physica A 371, 336 (2006)], is developed to extract simplified networks of pores and throats with parametrized geometry and interconnectivity from images of the pore space. The parameters of the pore networks, such as coordination number, and pore and throat size distributions are computed and compared to benchmark data from networks extracted by other methods, experimental data, and direct computation of permeability and formation factor on the underlying images. Good agreement is reached in most cases allowing networks derived from a wide variety of rock types to be used for predictive modeling.