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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 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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Preface 1. Introduction 2. Review of classical reservoir simulation 3. Recent progress in pore scale reservoir simulation 4. Recent progress in Darcy's scale reservoir simulation 5. Recent progress in multiscale and mesoscopic reservoir simulation 6. Recent progress in machine learning applications in reservoir simulation 7. Recent progress in accelerating flash cal culation using deep learning algorithms

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Sun, Shuyu
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 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. Professor Sun has published about 400 articles, including 220+ refereed journal papers
Zhang, Tao
Tao Zhang is currently a PhD candidate at King Abdullah University of Science and Technology (KAUST), Earth Science and Engineering, researching computational fluid dynamics and thermodynamics in reservoirs, as well as geological data analysis. Tao's research specialties also include deep learning and AI in reservoir simulation. He earned a master's 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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