Stochastic pix2vid: A new spatiotemporal deep learning method for image-to-video synthesis in geologic CO $$_2$$ storage prediction

Numerical simulation of multiphase flow in porous media is an important step in understanding the dynamic behavior of geologic CO\(_2\) storage (GCS). Scaling up GCS requires fast and accurate high-resolution modeling of the storage reservoir pressure and saturation plume migration; however, such modeling is challenging due to the high computational costs of traditional physics-based simulations. Deep learning models trained with numerical simulation data can provide a fast and reliable alternative to expensive physics-based numerical simulations. We propose a Stochastic pix2vid neural network architecture for solving multiphase fluid flow problems with significant speed, accuracy, and efficiency. The Stochastic pix2vid model is designed based on the principles of computer vision and video synthesis and is able to generate dynamic spatiotemporal predictions of fluid flow from static reservoir models, closely mimicking the performance of traditional numerical simulation. We apply the Stochastic pix2vid model to a highly-complex CO\(_2\)-water multiphase problem with a wide range of reservoir models in terms of porosity and permeability heterogeneity, facies distribution, and injection configurations. The Stochastic pix2vid method is first-of-its-kind in static-to-dynamic prediction of reservoir behavior, where a single static input is mapped to its dynamic response with a fixed number of timesteps. The Stochastic pix2vid method provides notable performance in highly heterogeneous geologic formations and complex estimation such as CO\(_2\) saturation and pressure buildup plume determination. The trained model can serve as a general-purpose, static-to-dynamic (image-to-video) alternative to traditional numerical reservoir simulation of 2D CO\(_2\) injection problems with up to 6,500\(\times \) speedup compared to traditional numerical simulation using the MATLAB Reservoir Simulation Toolbox.