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Reservoir Simulations

  • ID: 4991128
  • Book
  • August 2020
  • Region: Global
  • 320 Pages
  • Elsevier Science and Technology
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Reservoir Simulation: Machine Learning and Modeling provides the latest information and popular advances in reservoir simulation. The book explains common terminology, concepts and equations through multiple figures and short instructional videos, better preparing engineers for a modeling project that avoids problems. Well-designed exercises and an interactive website with downloadable algorithms give readers a faster start on advancing their own cases. Both computational methods and engineering cases are explained, thus bridging 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.

  • Helps readers understand commonly used definitions, equations and the solution methods used in reservoir simulation
  • Uses world-leading technology to enhance oil recovery by CO2 flooding, pore network modeling for unconventional reservoirs, and data analytics using machine learning
  • Presents practical knowledge with problems and solutions on modeling and simulation cases through a hands-on interactive website
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1. Introduction 2. Review of Classical Reservoir Simulation 3. Recent Progress in Pore Scale Reservoir Simulation 4. Recent Progress in Darcy Scale Reservoir Simulation 5. Recent Progress in Mesoscopic Reservoir Simulation 6. Recent Progress in Reservoir Simulation with Machine Learning

Appendix A. Notations and units B. Codes C. Exercises

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Sun, Shuyu
Prof. Shuyu Sun is currently the Director of the Computational Transport Phenomena Laboratory (CTPL) at King Abdullah University of Science and Technology (KAUST) and a Co-Director of the Center for Subsurface Imaging and Fluid Modeling consortium (CSIM) at KAUST. He is a founding faculty member jointly appointed by the program of Earth Sciences and Engineering (ErSE) and the program of Applied Mathematics and Computational Science (AMCS) at KAUST since 2009. He also holds a number of adjunct faculty positions across the world, including Adjunct Professorship in Xi'an Jiao Tong University, China University of Petroleum at Beijing, China University of Petroleum at Qingdao, and China University of Geosciences at Wuhan. He obtained his Ph.D. degree in computational and applied mathematics from The University of Texas at Austin. His research includes the modelling and simulation of porous media flow at Darcy scales, pore scales and molecular scales. Dr. Sun has published 300+ articles, including 190+ refereed journal papers.
Zhang, Tao
Tao Zhang is currently a PhD candidate at King Abdullah University of Science and Technology (KAUST) in Earth Science and Engineering, researching computational thermodynamics in reservoirs and geological data analysis. His research specialties also include deep learning and AI in reservoir simulation. Tao previously worked as a visiting scholar at multiple universities including the French Institute of Petroleum and the China Petroleum Engineering and Construction Corporation. Tao earned a Masters and a Bachelor of Engineering in storage and transportation of oil and gas, both from China University of Petroleum in Beijing.
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