Predicting Subsurface Reservoir Flow Dynamics at Scale with Hybrid Neural Network Simulator

Abstract

Abstract In this paper we demonstrate the application of state-of-the-art deep learning using hybrid neural networks (HNN) that generalize and scale to multi-million, structurally diverse reservoir model grids and generate long-term spatio-temporal predictions of fluid and pressure propagation. The HNN simulator (HNNS) is a surrogate framework that consists of a subsurface graph neural network (SGNN) to model the evolution of fluids, and a 3D-U-Net to model the evolution of pressure. We benchmark the HNNS with two conceptually different reservoir models: a) modified SPE-10 model, with approx. 1 million grid size and variable number and positioning of vertical producers and injectors, b) synthetic fractured model, 15+ million grid size and 100+ injector and producer wells with variable geometry. We construct the network graph, where graph objects (nodes), representing reservoir grid cells are encoded with tens of static, dynamic, computed (relative permeability, gradients) and control (well rates) features. The graph edges represent interactions between the nodes with encoded features like transmissibility, direction and fluxes. We implement sector-based training with multi-step rollout to avail for the use of large-scale models. To properly perform the sector-based training the masking of sector boundary effects, sector stride and mixing sectors were used. We present the comparative results between the HNNS and the full-physics simulation for up to 30-year prediction of the 3D flow dynamics.