Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization
Description
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.
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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.
Mohammad Ahmadi is currently an Associate Professor of reservoir engineering at Amirkabir University of Technology (Tehran Polytechnic). He has got his MSc and PhD degrees from the French Petroleum Institute (IFP) and Heriot-Watt University in reservoir engineering and petroleum engineering, respectively. His research activities has been focused on Numerical Methods for Flow in Porous Media, inverse modeling in porous media, and Deep Learning and Artificial Intelligence for reservoir geomodeling and uncertainty quantification. Mohammad is currently working on development of closed loop field development and data assimilation techniques for history matching and production optimization. He has published more than 50 technical papers in this area of research. He is specially working on Computational Methods for Modeling of Heterogeneous Reservoirs with uncertain Geometrically Complex Geological Structure.Dr Jebraeel Gholinezhad, BSc, MSc, PhD, CEng is currently a senior lecturer in energy systems engineering in the School of Energy and Electronic Engineering at the University of Portsmouth, UK. He is programme manager for two MSc courses and teaches energy- and engineering-related subjects including thermofluid properties and thermodynamics, engineering economics and risk analysis, well engineering and reservoir simulation. His current research is focused on the application of machine learning algorithms in fluid compositional characterisation, numerical simulation of underground CO2 storage and energy storage materials.
Dr Gholinezhad obtained his PhD from Heriot-Watt University where he conducted his research on hydrogen storage and CO2 capture using semi-clathrate hydrates resulting in five journal and conference publications. His MSc studies was on reservoir engineering at IFP School which was concluded by doing a placement in TOTAL on chemical and mechanical methods of mitigating scale depositions in oil and gas wells and downhole equipment, a project which led to his first publication in 2005. Since then, he has published his work on energy-related projects in various peer-reviewed journals including Energy and Fuels, Fluid Phase Equilibria, Chemical Engineering Research and Design and so on. Prior to becoming an academic, Dr Gholinezhad worked at Research Institute of Petroleum Industry (RIPI) in Tehran for 6 years where he contributed to various research projects on reservoir fluid studies, well performance analysis, artificial lifting, flow assurance (wax, asphaltene, gas hydrates) and water production management. Majority of these projects involved experimental work with various laboratory measurements using high-pressure equipment.
Content
Chapter 1. Introduction to Uncertainty in Reservoir Engineering.- Chapter 2. Geological Uncertainty Quantification.- Chapter 3. Reducing the Geological Uncertainty by History Matching.- Chapter 4. Dimensionality Reduction Methods used in History Matching.- Chapter 5. Field Development Optimization under Geological Uncertainty.- Chapter 6. History Matching and Robust Optimization Using Proxies.