Physics-Informed Neural Network for Modeling One-Dimensional Transient Two-Phase Flow with Capillary Pressure at Varying Injection Rates

Abstract

we developed a physics-informed neural network parameterized by injection rate for modeling two-phase flow. We trained the network using the saturation transport equation, initial and boundary conditions, and the integral water balance, without fitting it to observed curves. For validation, we used data generated by the tNavigator reservoir simulator and results obtained with an independently developed finite-volume code. We compared six integral characteristics for three injection regimes. The maximum root-mean-square error of the neural network relative to the tNavigator data, normalized by the range of the corresponding reference series, was 1.5%. At the intermediate injection rate, the neural network reproduced the integral displacement characteristics obtained with tNavigator without retraining.