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Optimization of Geological Carbon Storage Operations with Multimodal Latent Dynamic Model and Deep Reinforcement Learning by Wang, Zhongzheng; Chen, Yuntian; Chen, Guodong; Zhang, Dongxiao is a scholarly article available to read on EtoBox.

What is Optimization of Geological Carbon Storage Operations with Multimodal Latent Dynamic Model and Deep Reinforcement Learning about?

Maximizing storage performance in geological carbon storage (GCS) is crucial for commercial deployment, but traditional optimization demands resource-intensive simulations, posing computational challenges. This study introduces the multimodal latent dynamic (MLD) model, a deep learning framework for fast flow prediction and well control optimization in GCS. The MLD model includes a representation module for compressed latent representations, a transition module for system state evolution, and a prediction module for flow responses. A novel training strategy combining regression loss and joint-embedding consistency loss enhances temporal consistency and multi-step prediction accuracy. Unlike existing models, the MLD supports diverse input modalities, allowing comprehensive data interactions. The MLD model, resembling a Markov decision process (MDP), can train deep reinforcement learning agents, specifically using the soft actor-critic (SAC) algorithm, to maximize net present value (NPV) through continuous interactions. The approach outperforms traditional methods, achieving the highest NPV while reducing computational resources by over 60%. It also demonstrates strong generalization

Author

Wang, Zhongzheng; Chen, Yuntian; Chen, Guodong; Zhang, Dongxiao

Published

2024

Language

EN