Towards efficient and accurate CO2 sequestration optimization: Integrating hierarchical spatio-temporal information into deep-learning-based surrogate models

Abstract

Optimizing CO2 storage in brine water is a critical task that requires process-level methods based on geological modeling and numerical simulation. However, these methods can be computationally expensive, especially when simulating larger grid scales and longer simulation times. To address this challenge, we proposed a novel approach that utilizes advanced deep learning techniques to develop a U-LSTM-net neural network capable of incorporating spatio-temporal information at varying hierarchies. The method employs an encoder-decoder architecture that extracts, memorizes, and integrates localized and holistic information from changing receptive fields. Multi-task learning algorithms enable simultaneous learning and predicting multiple flow fields. Additionally, we employed transfer learning to transfer learned knowledge and experience to new tasks and a fine-tuning algorithm to improve the retention of domain-general knowledge. We demonstrated the effectiveness of our model on synthetic models and an adjusted 3D Brugge mode. The U-LSTM-net effectively captures and integrates spatio-temporal information, exhibiting powerful memory capacity. Balancing algorithms improve multiple tasks' learning efficiency and avoid the dominance of tasks with higher loss values. The fine-tuning algorithm reduces overfitting and preserves pre-trained knowledge better, thereby reducing the demand for extensive information. Furthermore, when transferred to the 3D authentic problem, the surrogate model exhibits impressive predictive capabilities for various attributes across different layers. This study integrated multiple learning methods and established a precise and practical learning workflow for accelerating the prediction of the CO2 sequestration dynamic.