Effective Production Forecasting and Robust Rate Optimization Using Physics Informed Neural Networks

Abstract Waterflooding has a long history as a successful development strategy in oil recovery, yet maximizing its potential through optimized strategies remains a significant challenge. Traditionally, identifying the most effective waterflooding designs requires extensive numerical simulations, which can be computationally demanding. This study introduces a comprehensive framework that employs a Physics-Informed Neural Network (PINN) to optimize waterflooding designs for enhancing oil recovery. Specifically, the PINN incorporates fluid dynamics principles into deep learning algorithms and serves as a rapid surrogate method to effectively predicts oil production across a range of waterflooding scenarios. Furthermore, an optimization technique is designed to fine-tune injection designs, thereby optimizing oil recovery. Experiments on 2D synthetic and 3D Brugge benchmark cases demonstrate that the PINN model achieves higher accuracy compared to a pure data-driven neural network. Using the PINN surrogate, a genetic algorithm quickly searches the injection parameter space for optimal oil production. The optimized strategies are validated using the full numerical simulator, confirming the feasibility of the proposed approach. Overall, the integration of domain knowledge into deep learning not only improves the generalise ability of the pure data-driven model but also provides insightful physical interpretations for engineers.