Prediction of Field Saturations Using a Fully Convolutional Network Surrogate

Abstract

Summary We are interested in the development of surrogate models for the prediction of field saturations using a fully convolutional encoder/decoder network based on the dense convolutional network (DenseNet; Huang et al. 2017), similar to the approaches used for image/image-regression tasks in deep learning. In the surrogate model, the encoder network automatically extracts the multiscale features from the raw input data, and the decoder network then uses these data to recover the input image resolution at the output of the model. The input of multiple influencing factors is considered to make our surrogate model more consistent with the physical laws, which has achieved good results in the prediction of output fields in our experiments. Various reservoir parameters including the static reservoir properties (i.e., permeability field) and dynamic reservoir properties (i.e., well placement) are used as input features, and the water-saturation distributions in different periods are taken as the output. Compared with traditional numerical reservoir simulation, which has a high computational cost and is time consuming, not only does it present the same precision, but it costs less time. At the same time, it can also be used for production optimization and history matching.