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OverviewReservoir 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. Full Product DetailsAuthor: Shuyu Sun (King Abdullah University of Science and Technology. Thuwal, Saudi Arabia) , Tao Zhang (King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia)Publisher: Elsevier Science & Technology Imprint: Gulf Professional Publishing Weight: 0.700kg ISBN: 9780128209578ISBN 10: 0128209577 Pages: 340 Publication Date: 15 June 2020 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsPreface 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 algorithmsReviewsAuthor InformationShuyu 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 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 Tab Content 6Author Website:Countries AvailableAll regions |
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