We use a generalized pore network model in combination with image-based experiments to understand the parameters that control upscaled flow properties. The study is focued on water-flooding through a reservoir sandstone under water-wet and mixed-wet conditions. A set of sensitivity studies is presented to quantify the role of wettability, pore geometry, initial and boundary conditions as well as a selection of model parameters used in the computation of fluid volumes, curvatures and flow and electrical conductivities.

We quantify the uncertainty in the model predictions, which match the measured relative permeability and capillary pressure within the uncertainty of the experiments. Our results show that contact angle, initial saturation, image quality and image processing algorithm are amongst the parameters which introduce the largest variance in the predictions of upscaled flow properties for both mixed-wet and water-wet conditions.

我们使用广义孔隙网络模型结合基于图像的实验来了解控制放大流动特性的参数。该研究的重点是在水湿和混合湿条件下通过储层砂岩进行注水。提出了一组敏感性研究，以量化润湿性、孔隙几何形状、初始和边界条件以及用于计算流体体积、曲率和流动和电导率的模型参数的选择。

我们量化了模型预测中的不确定性，这与实验不确定性内测得的相对渗透率和毛细管压力相匹配。我们的结果表明，接触角、初始饱和度、图像质量和图像处理算法是在混合湿和水湿条件下的放大流动特性预测中引入最大方差的参数之一。