Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization
Language: English
Published by Springer, Berlin|Springer International Publishing|Springer, 2023
Series: Book 25 of 25 - SpringerBriefs in Petroleum Geoscience & Engineering
- Softcover
- New
Seller: moluna, Greven, Germanymoluna
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sourc.
Seller Inventory # 812308039
- Title
- Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization
- Author
- Yousefzadeh, Reza|Kazemi, Alireza|Ahmadi, Mohammad|Gholinezhad, Jebraeel
- Publisher
- Springer, Berlin|Springer International Publishing|Springer
- Publication year
- 2023
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3031280784
- ISBN 13
- 9783031280788
- Series
- Book 25 of 25: SpringerBriefs in Petroleum Geoscience & Engineering
This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sources of uncertainty and the challenges in different phases of reservoir management, along with general methods to manage it. The second chapter focuses on geological uncertainty, explaining its impact on field development and methods to handle it using prior information, seismic and petrophysical data, and geological parametrization. The third chapter deals with reducing geological uncertainty through history matching and the various methods used, including closed-loop management, ensemble assimilation, and stochastic optimization. The fourth chapter presents dimensionality reduction methods to tackle high-dimensional geological realizations. The fifth chapter covers field development optimization using robust optimization, including solutions for its challenges such as high computational cost and risk attitudes. The final chapter introduces different types of proxy models in history matching and robust optimization, discussing their pros and cons, and applications.
The book will be of interest to researchers and professors, geologists and professionals in oil and gas production and exploration.
"Synopsis" may belong to another edition of this title.
About the Author
Reza Yousefzadeh is currently a postgraduate researcher in reservoir engineering at Amirkabir University of Technology (Tehran Polytechnic). He got his master's and bachelor's degree from the same university in reservoir engineering and petroleum engineering, respectively. His research fields included well placement optimization, facilitating well placement optimization using fast marching method, uncertainty management in well placement optimization under geological uncertainty, and applying machine learning algorithms to different petroleum-related problems such as field development optimization and history matching.
Reza is currently working on addressing some of the common challenges in uncertainty management in robust field development optimization and has published several technical papers in this regard. He is specially working on reducing the computational cost and improving the parametrization quality of the geological realizations. In this regard, his primary focus is on using deep learning methods capable of handling three-dimensional models with complex and non-Gaussian distributions.Dr Alireza Kazemi, BSc, MSc, PhD, obtained his PhD from Heriot-Watt University where he conducted his research on time lapse seismic history matching and his MSc studies was on reservoir engineering at IFP School.
He is currently an assistant professor in the department of petroleum and chemical engineering at Sultan Qaboos University in Oman. He teaches subjects including reservoir simulation, enhanced oil recovery, fluid flow in the porous media, carbon capture and storage and advanced reservoir engineering. His current research is focused on the application of machine learning algorithms in scale deposition, reservoir simulation history matching and uncertainty quantification and modelling and simulation of underground CO2 and hydrogen storage. He has published more than 45 technical papers.
"About the title" may belong to another edition of this title.
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