A novel deep learning-based automatic search workflow for CO2 sequestration surrogate flow models

Abstract

Numerical simulation can significantly enhance subsurface resource utilisation's efficiency and economic benefits by multiphase flow in heterogeneous porous media. However, numerical simulation brings enormous computational demands and time consumption due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes. Surrogate models can accelerate the establishment of complex models without sacrificing accuracy. However, creating well-performing surrogate models requires extensive human intervention and trial-and-error processes, even for cross-domain experts. This study proposes an automated surrogate flow model workflow based on deep learning called Surrogate Flow Model Search (SFMS). By incorporating neural architectures and loss functions into joint hyperparameter optimisation, SFMS automates many complex and time-consuming tasks involved in model development, such as designing neural architectures and loss functions. The automated surrogate model construction workflow enables researchers to develop high-quality surrogate models without extensive deep-learning expertise. We demonstrate the effectiveness of SFMS using saline aquifer CO2 injection as an example. The results show that SFMS can automatically generate a highly accurate surrogate flow model in a short time (<1300 s), capable of accurately predicting 120-time steps under different well controls and placements with a low average relative error (<0.4%). Therefore, SFMS can significantly reduce the time and effort required to develop accurate and reliable surrogate models, providing a new approach to building surrogate models.