Description: Subsurface Data Assimilation: Theory and Applications provides a comprehensive exploration of data assimilation algorithms applied to subsurface characterization and monitoring. The book begins with data assimilation methods, including multilevel data assimilation, coupled data assimilation with machine learning, and generative neural networks for geological parameterization. It also introduces Latent-Space Data Assimilation (LSDA), leveraging deep learning for feature-based analysis and forecasting, and geostatistical seismic inversion techniques. The second part of the book looks into the practical applications of data assimilation in various subsurface problems. Chapters explore CO2 monitoring, geologic CO2 sequestration, and the use of data assimilation for earthquake or CO2 storage scenarios.
Hierarchical data assimilation procedures for carbon storage with uncertain geological scenarios are discussed, along with applications of data assimilation in geothermal energy contexts. The book also addresses practical uncertainty management practices and challenges related to CO2 storage and geothermal energy projects.Brief description:
Xiaodong Luo has more than 10 years of experience in research, developments and applications of ensemble-based data assimilation methods to subsurface characterization and monitoring problems, and is currently leading two projects in the NCS2030 petro-center, Norway, on geological CO2 and H2 storage. Dr. Luo's recent technical developments were incorporated into a US Geological Survey (USGS) software and Equinor's Ensemble Reservoir Tool (ERT). Dr. Luo is an associate editor of the journal Frontiers in Applied Mathematics and Statistics, and a recipient of Outstanding Reviewer Award in the Journal of Petroleum Science and Engineering (2019) and the SPE Journal (2023).