Reservoir Simulations: Machine Learning and Modeling

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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)

Reservoir Simulations

Machine Learning and Modeling

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)

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English

Gulf Professional Publishing
15 June 2020

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.

By: 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)
Imprint: Gulf Professional Publishing
Country of Publication: United States
Dimensions: Height: 234mm, Width: 191mm,
Weight: 700g
ISBN: 9780128209578
ISBN 10: 0128209577
Pages: 340
Publication Date: 15 June 2020
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Preface1. Introduction2. Review of classical reservoir simulation3. Recent progress in pore scale reservoir simulation4. Recent progress in Darcy’s scale reservoir simulation5. Recent progress in multiscale and mesoscopic reservoir simulation6. Recent progress in machine learning applications in reservoir simulation7. Recent progress in accelerating flash cal culation using deep learning algorithms

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 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