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.

翻译：在地质碳封存（GCS）中最大化封存性能对其商业化部署至关重要，但传统优化方法依赖资源密集型的数值模拟，带来了显著的计算挑战。本研究提出了多模态潜在动态（MLD）模型，这是一个用于GCS快速流动预测与井控优化的深度学习框架。MLD模型包含用于生成压缩潜在表征的表征模块、用于系统状态演化的转移模块以及用于流动响应的预测模块。一种结合回归损失与联合嵌入一致性损失的新型训练策略，增强了时间一致性与多步预测精度。与现有模型不同，MLD支持多样化的输入模态，允许全面的数据交互。MLD模型类似于马尔可夫决策过程（MDP），能够训练深度强化学习智能体（具体采用软演员-评论家（SAC）算法），通过持续交互来最大化净现值（NPV）。该方法优于传统方法，在实现最高NPV的同时，将计算资源需求降低了60%以上。它还展现出强大的泛化性能，能够基于先前场景的知识为新场景提供更优的决策。