Reservoir Simulations : Machine Learning and Modeling

Shuyu Sun

ISBN 10: 0128209577 / ISBN 13: 9780128209578

Published by Elsevier Inc Jun 2020, 2020

About this Item

Neuware - 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.

Seller Inventory # 9780128209578

Bibliographic Details

• Title: Reservoir Simulations : Machine Learning and Modeling
• Publisher: Elsevier Inc Jun 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 0128209577
• ISBN 13: 9780128209578
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 18D millimeters 235 millimeters width by 191 millimeters height by 18 millimeters depth
• Weight: 590 grams

About this title

Synopsis

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.

About the Authors

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