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.
Part I: Theoretical Foundations of Data Assimilation Algorithms
1. Recent Progresses of Data Assimilation Methods Applied to Subsurface
Characterization and Monitoring Problems
2. Multilevel Data Assimilation
3. Coupled Data Assimilation and Machine Learning
4. Generative Neural Networks for Geological Parameterization
5. Latent-Space Data Assimilation (LSDA): Leveraging Deep Learning for
Feature-Based Analysis and Forecasting
6. Geostatistical Seismic Inversion
Part II: Applications to Various Subsurface Problems
7. CO Monitoring
8. Geologic CO2 Sequestration
9. Earthquake or CO2 Storage
10. Hierarchical Data Assimilation Procedures for Carbon Storage with
Uncertain Geological Scenario
11. Geothermal Energy
12. Practical Uncertainty Management, Practices, and Challenges in CO2
Storage/Geothermal Energy
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. Luos 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).
Olwijn Leeuwenburgh is Senior Scientist at The Geological Survey of the Netherlands, which is part of TNO, the national organisation for applied scientific research. He has 22 years of experience with adjoint-based and ensemble-based data assimilation in ocean and subsurface models. Current research interests include the use of uncertainty quantification and data assimilation methods for evaluation of monitoring strategies, especially in the context of CO2 storage operations. He has supervised 15 MSc students and co-supervised 4 PhD students at Delft University of Technology.
Alexandre A. Emerick is a Senior Advisor at the Petrobras Research Center in Rio de Janeiro, bringing over 20 years of experience in applied research in reservoir engineering. His research interests include reservoir simulation, data assimilation, uncertainty quantification and optimization. Dr. Emerick has authored and/or coauthored 30 papers peer-reviewed journals. He holds a PhD degree in petroleum engineering from The University of Tulsa. Dr. Emerick received the Outstanding Service Award as SPE Journal technical editor in 2013 and 2014.
