Simulation of liquid production and water cut dynamics using fluid flow model and neural networks

Abstract

In the oil industry, there is a noticeable tendency to use proxy modeling of various levels of complexity to perform operational predictive calculations, in particular machine learning methods that are actively developing in the context of digitalization and intellectualization of production processes. In this paper, using the example of a synthetic oil reservoir model development element, we present an approach to the joint use of a physically meaningful fluid flow model and machine learning methods for solving adaptation and prediction problems. A feature of the considered synthetic model is the presence of a pronounced zonal inhomogeneity of the permeability field. Within the framework of the proposed approach, a single-phase filtration model, simplified in comparison with the original formulation was used, the history matching of which was carried out by restoring the field of reservoir filtration parameters using a network of radial basis functions. Based on the reconstructed field, the connection coefficients between the wells were calculated, which qualitatively and quantitatively correspond to the true well connections. The next step was to train a recurrent neural network in order to predict the water cut of the produced fluid. The use of a recurrent neural network made it possible to reproduce the characteristic non-monotonic behavior of the water cut of the produced fluid, caused by non-stationary modes of operation of injection and production wells. A combination of the presented models makes it possible to predict the volume of the produced fluid and its phase composition. To assess the predictive properties of the models, the actual data set was divided into training and test intervals.