Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
<|reference_start|>Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning: 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 performance, providing improved decisions for new scenarios based on knowledge from previous ones.<|reference_end|>
@article{wang2024optimization,
    title={Optimization of geological carbon storage operations with multimodal
  latent dynamic model and deep reinforcement learning},
    author={Zhongzheng Wang, Yuntian Chen, Guodong Chen, Dongxiao Zhang},
    journal={arXiv preprint arXiv:2406.04575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04575},
    primaryClass={cs.LG cs.AI stat.AP stat.ML}
}