资源环境科技发展态势分析平台

Estimating the longitudinal dispersion coefficient in flow through heterogeneous porous media is paramount to many problems in geological formations. Moreover, although it is well-known that is sensitive to the morphology of such formations, it has been very difficult to establish a firm link between the two. We describe a novel deep convolutional neural network (DCNN) for estimating . The inputs for training of the network are a large and diverse set of data consisting of three-dimensional images of porous media, as well as their porosity, and the associated values computed by random-walk particle-tracking (RWPT) simulations. The trained network predicts very rapidly, and its predictions are in excellent agreement with the data not used in the training. Thus, a combination of the DCNN and RWPT simulation provides a powerful tool for studying many flow-related phenomena in geological formations, and estimating their properties.