Physics-informed Convolutional Recurrent Surrogate Model for Reservoir Simulation with Well Controls

TL;DR

This paper introduces a physics-informed convolutional recurrent neural network that accurately predicts reservoir fluid flow dynamics using well controls, without requiring labeled training data, and enforces boundary conditions inherently.

Contribution

The novel physics-informed ConvLSTM-based surrogate model integrates reservoir physics directly into the neural network, enabling efficient, data-free training and accurate future state prediction.

Findings

Effective in three numerical cases for reservoir dynamics prediction

Enforces boundary conditions without additional loss terms

Predicts future reservoir states based on initial conditions and well controls

Abstract

This paper presents a novel surrogate model for modeling subsurface fluid flow with well controls using a physics-informed convolutional recurrent neural network (PICRNN). The model uses a convolutional long-short term memory (ConvLSTM) to capture the spatiotemporal dependencies of the state evolution dynamics in the porous flow. The ConvLSTM is linked to the state space equations, enabling the incorporation of a discrete-time sequence of well control. The model requires initial state condition and a sequence of well controls as inputs, and predicts the state variables of the system, such as pressure, as output. By minimizing the residuals of reservoir flow state-space equations, the network is trained without the need for labeled data. The model is designed to serve as a surrogate model for predicting future reservoir states based on the initial reservoir state and input engineering controls. Boundary conditions are enforced into the state-space equations so no additional loss term is needed. Three numerical cases are studied, demonstrating the model's effectiveness in predicting reservoir dynamics based on future well/system controls. The proposed model provides a new approach for efficient and accurate prediction of subsurface fluid flow, with potential applications in optimal control design for reservoir engineering.