Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning
College of Engineering, Peking University（北京大学工学院）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工大学宁波数字孪生研究院）；The University of Hong Kong（香港大学）；Peng Cheng Laboratory（鹏城实验室）
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.
Exploration of Multi-Element Collaborative Research and Application for Modern Power System Based on Generative Large Models
Georgia Institute of Technology（佐治亚理工学院）；Bay Area Founders Club, INC.（湾区创始人俱乐部）；Duke University（杜克大学）；Pratt School of Engineering, Duke University（杜克大学普拉特工程学院）；Yellow River Conservancy Technical Institute（黄河水利职业技术学院）；Tianjin University（天津大学）；Meta；Electric Power Research Institute（电力研究院）
Integrating Score-Based Diffusion Models with Machine Learning-Enhanced Localization for Advanced Data Assimilation in Geological Carbon Storage
Faculty of Civil Engineering；Geosciences, TU Delft, Stevinweg 1, 2628 CN Delft, Netherlands；Centrum Wiskunde \& Informatica, Science Park 123, 1098 XG Amsterdam, Netherlands；Simulation Group, Imperial College London, London, United Kingdom；Department of Energy Resources Engineering, Stanford University, CA, USA
AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage
Faculty of Civil Engineering；Geosciences, TU Delft, Stevinweg 1, 2628 CN Delft, Netherlands；Centrum Wiskunde \& Informatica, Science Park 123, 1098 XG Amsterdam, Netherlands；Mathematical Institute, Utrecht University, Utrecht, Netherlands；Department of Earth Science；Engineering, Imperial College London, London, United Kingdom；Department of Energy Resources Engineering, Stanford University, CA, USA
Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
Systems and Computer Engineering and High Performance Computing Center, NACAD - COPPE, Federal University of Rio de Janeiro（里约热内卢联邦大学COPPE工程研究生院NACAD高性能计算中心，系统与计算机工程）；Civil Engineering and High Performance Computing Center, NACAD - COPPE, Federal University of Rio de Janeiro（里约热内卢联邦大学COPPE工程研究生院NACAD高性能计算中心，土木工程）；Mechanical Engineering and High Performance Computing Center, NACAD - COPPE, Federal University of Rio de Janeiro（里约热内卢联邦大学COPPE工程研究生院NACAD高性能计算中心，机械工程）；Shell Global Solutions International B.V.（壳牌全球解决方案国际公司）；TotalEnergies OneTech（道达尔能源OneTech）
Boundary condition fidelity for bottom-hole pressure and CO2 plume prediction in geological carbon storage
Department of Earth and Planetary Sciences, Jackson School of Geosciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校杰克逊地球科学学院地球与行星科学系）；Bureau of Economic Geology, The University of Texas at Austin（德克萨斯大学奥斯汀分校经济地质局）；Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校希尔德布兰德石油与地质系统工程系）
A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Total E&P Research and Technology（道达尔勘探与生产研究和技术公司）；Energy & Environmental Research Center, University of North Dakota（北达科他大学能源与环境研究中心）
Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；TotalEnergies E&P Research and Technology（道达尔能源勘探与生产研究技术中心）；University of North Dakota, Energy & Environmental Research Center（北达科他大学能源与环境研究中心）
通过同化监测数据快速预测地质碳封存储层压力分布是一个具有挑战性的问题。由于高钻井成本，GCS项目通常具有来自井的空间稀疏测量，导致储层压力预测的高不确定性。为了应对这一挑战，我们建议使用低成本的干涉合成孔径雷达（InSAR）数据作为监测数据来推断储层压力的建立。我们开发了一个深度学习加速的工作流程，以同化从InSAR解释的地表位移图，并预测动态储层压力。该工作流程采用Encourage Smoother Multiple Data Assimilation（ES-MDA）框架，更新三维（3D）地质属性，并预测具有量化不确定性的储层压力。我们使用一个合成的商业规模的GCS模型双峰分布的渗透率和孔隙度，以证明工作流程的有效性。一个两步CNN-PCA方法被用来参数化的双峰字段。工作流程的计算效率通过两个基于剩余U-Net的代理模型分别用于地表位移和储层压力预测来提高。该工作流程可在个人计算机上在半小时内完成数据同化和储层压力预测。
Physics-Informed Neural Networks for Predicting Hydrogen Sorption in Geological Formations: Thermodynamically Constrained Deep Learning Integrating Classical Adsorption Theory
University of Oslo（奥斯陆大学）；SINTEF Industry（挪威科技工业研究院）；China University of Petroleum, Beijing（中国石油大学（北京））
