Real-time relative permeability prediction using deep learning

A review of the existing two- and three-phase relative permeability correlations shows a lot of pitfalls and restrictions imposed by (a) their assumptions (b) generalization ability and (c) difficulty with updating in real-time for different reservoirs systems. These increase the uncertainty in its prediction which is crucial owing to the fact that relative permeability is useful for predicting future reservoir performance, effective mobility, ultimate recovery, and injectivity among others. Laboratory experiments can be time-consuming, complex, expensive and done with core samples which in some circumstances may be difficult or impossible to obtain. Deep Neural Networks (DNNs) with their special capability to regularize, generalize and update easily with new data has been used to predict oil–water relative permeability. The details have been presented in this paper. In addition to common parameters influencing relative permeability, Baker and Wyllie parameter combinations were used as input to the network after comparing with other models such as Stones, Corey, Parker, Honapour using Corey and Leverett-Lewis experimental data. The DNN automatically used the best cross validation result (in a five-fold cross validation) for its training until convergence by means of Nesterov-accelerated gradient descent which also minimizes the cost function. Predictions of non-wetting and wetting-phase relative permeability gave good match with field data obtained for both validation and test sets. This technique could be integrated into reservoir simulation studies, save cost, optimize the number of laboratory experiments and further demonstrate machine learning as a promising technique for real-time reservoir parameters prediction.