We predict waterflood displacement on a pore-by-pore basis using pore network modelling. The pore structure is captured by a high-resolution image. We then use an energy balance applied to images of the displacement to assign an average contact angle, and then modify the local pore-scale contact angles in the model about this mean to match the observed displacement sequence. Two waterflooding experiments on oil-wet rocks are analysed where the displacement sequence was imaged using time-resolved synchrotron imaging. In both cases the capillary pressure in the model matches the experimentally obtained values derived from the measured interfacial curvature. We then predict relative permeability for the full saturation range. Using the optimised contact angles distributed randomly in space has little effect on the predicted capillary pressures and relative permeabilities, indicating that spatial correlation in wettability is not significant in these oil-wet samples. The calibrated model can be used to predict properties outside the range of conditions considered in the experiment.

我们使用孔隙网络模型在逐孔基础上预测注水驱替量。孔结构被高分辨率图像捕获。然后，我们将能量平衡应用于位移的图像以指定平均接触角，然后围绕该均值修改模型中的局部孔尺度接触角以匹配观察到的位移序列。分析了两个在油湿岩石上的注水实验，其中使用时间分辨同步加速器成像对位移序列进行了成像。在这两种情况下，模型中的毛细压力都与从测量的界面曲率得出的实验获得的值相匹配。然后，我们预测整个饱和范围内的相对磁导率。使用在空间中随机分布的优化接触角对预测的毛细管压力和相对渗透率影响很小，这表明在这些油湿样品中，润湿性的空间相关性并不显着。校准的模型可用于预测实验中考虑的条件范围之外的性质。