Subsurface Data Assimilation

Theory and Applications

Häftad, Engelska, 2026

AvXiaodong Luo,Olwijn Leeuwenburgh,Alexandre Anoze Emerick

2 039 kr

Kommande

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.

Bridges the potential gaps between theoretical analysis and practical applicability
Analyzes state-of-the-art research fronts in subsurface data assimilation problems
Describes hands-on experience from respective field experts in diverse subsurface data assimilation cases

Produktinformation

Utgivningsdatum2026-10-25
Mått152 x 229 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor300
FörlagElsevier Science
ISBN9780443415432

Tillhör följande kategorier

Företagsekonomi inom Ekonomi och Ledarskap
Tillämpad fysik inom Naturvetenskap och teknik
Geovetenskap inom Naturvetenskap och teknik
Affärsapplikationer inom Data och IT

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). 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.

Part I: Theoretical Foundations of Data Assimilation Algorithms1. Recent Progresses of Data Assimilation Methods Applied to Subsurface Characterization and Monitoring Problems2. Multilevel Data Assimilation3. Coupled Data Assimilation and Machine Learning4. Generative Neural Networks for Geological Parameterization5. Latent-Space Data Assimilation (LSDA): Leveraging Deep Learning for Feature-Based Analysis and Forecasting6. Geostatistical Seismic InversionPart II: Applications to Various Subsurface Problems7. CO₂ Monitoring8. Geologic CO2 Sequestration9. Earthquake or CO2 Storage10. Hierarchical Data Assimilation Procedures for Carbon Storage with Uncertain Geological Scenario11. Geothermal Energy12. Practical Uncertainty Management, Practices, and Challenges in CO2 Storage/Geothermal Energy