A physics-guided autoregressive model for saturation sequence prediction

Abstract

A deep-learning-based surrogate model is developed and applied for predicting dynamic saturation sequences in oil-water two-phase reservoirs. Considering the limitations of most existing deep learning-based surrogate models in processing static and dynamic data simultaneously, and the lack of guidance from physical equations, a physics-guided autoregressive model is proposed to solve these problems. The surrogate model introduces the idea of explicit finite difference to make it work more in line with physical processes; the embedding of the mass balance equation makes its prediction accuracy significantly improved. To evaluate the performance of the proposed method, three representative models are comprehensively compared, namely the recurrent R-U-Net, the autoregressive model without the physical meaning embedded and the physics-guided autoregressive model. And experiments show that the physics-guided autoregressive model is capable of predicting accurate dynamic saturation maps and achieves very competitive results. Compared to the numerical simulation, the trained surrogate model is capable of predicting the saturation sequence efficiently, quickly, and accurately. We believe it has the potential to replace the forward process of numerical simulation when predicting saturation.