- 340 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experiments and speeding up potential collaboration opportunities in research and technology. This reference explains common terminology, concepts, and equations through multiple figures and rigorous derivations, better preparing the engineer for the next step forward in a modeling project and avoid repeating existing progress. Well-designed exercises, case studies and numerical examples give the engineer a faster start on advancing their own cases. Both computational methods and engineering cases are explained, bridging the opportunities between computational science and petroleum engineering. This book delivers a critical reference for today's petroleum and reservoir engineer to optimize more complex developments.- Understand commonly used and recent progress on definitions, models, and solution methods used in reservoir simulation- World leading modeling and algorithms to study flow and transport behaviors in reservoirs, as well as the application of machine learning- Gain practical knowledge with hand-on trainings on modeling and simulation through well designed case studies and numerical examples.
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Information
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Chapter one. Introduction
- Chapter two. Review of classical reservoir simulation
- Chapter three. Recent progress in pore scale reservoir simulation
- Chapter four. Recent progress in Darcy’s scale reservoir simulation
- Chapter five. Recent progress in multiscale and mesoscopic reservoir simulation
- Chapter six. Recent progress in machine learning applications in reservoir simulation
- Chapter seven. Recent progress in accelerating flash calculation using deep learning algorithms
- Index