论文标题
数字岩石中的高精度毛细管网络表示揭示了渗透率缩放功能
High accuracy capillary network representation in digital rock reveals permeability scaling functions
论文作者
论文摘要
渗透性是量化多孔岩石中流体流动的关键参数。了解连接孔隙空间的空间分布的知识原则上可以预测岩石样品的渗透性。然而，到目前为止，在微观量表上的特征分辨率和近似值的局限性排除了渗透率预测的系统上尺度。在这里，我们报告了旨在克服此类限制的毛细管网络表示中的流体流量模拟。孔尺度模拟在微观尺度上以前所未有的准确性进行了准确性，可预测在同一岩石样品中以实验室量表测量的实验渗透率，而无需进行校准或校正。通过将方法应用于更广泛的代表性地质样本，渗透率值涵盖了两个数量级，我们获得了缩放关系，这些关系揭示了中尺度渗透率如何从微观毛细管直径和流体速度分布中出现。
Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in capillary network representations designed to overcome such limitations. Performed with an unprecedented level of accuracy in geometric approximation at microscale, the pore scale flow simulations predict experimental permeabilities measured at lab scale in the same rock sample without the need for calibration or correction. By applying the method to a broader class of representative geological samples, with permeability values covering two orders of magnitude, we obtain scaling relationships that reveal how mesoscale permeability emerges from microscopic capillary diameter and fluid velocity distributions.
