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Elsevier

Reservoir Simulations

Machine Learning and Modeling

  • 1st Edition - June 15, 2020
  • Latest edition
  • Authors: Shuyu Sun, Tao Zhang
  • Language: English

Reservoir Simulation: Machine Learning and Modeling helps the engineer step into the current and most popular advances in reservoir simulation, learning from current experimen… Read more

Description

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.

Key features

  • 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.

Readership

Reservoir engineers; graduate-level petroleum engineers; computer scientists; petroleum researchers; data analysts in oil and gas research

Table of contents

Preface1. Introduction1.1 Introduction1.2 Definitions1.3 Single-phase rock properties1.4 Wettability1.5 Fluid displacement processes1.6 Multiphase rock/fluid properties1.7 TermsReferencesFurther reading2. Review of classical reservoir simulation2.1 Sharp interface models2.2 Cahn-HILLIARD-BASED diffuse interface models2.3 Dynamic Van der Waals theory2.4 Multiphase porous flow solvers2.5 Wellbore modeling2.6 Solute transport in porous media2.7 Dynamic sorption in porous media2.8 Black oil modelReferencesFurther reading3. Recent progress in pore scale reservoir simulation3.1 Phase equilibria in subsurface reservoirs3.2 Stable dynamic NVT algorithm with capillarity3.3 Multicomponent two-phase diffuse interface models based on Peng-Robinson equation of state3.4 Multiphase flow with partial miscibilityReferencesFurther reading4. Recent progress in Darcy’s scale reservoir simulation4.1 Introductions on popular finite element methods4.2 Links between finite-difference methods and finite element methods4.3 Improved IMPES scheme4.4 Bound-preserving fully implicit reservoir simulation on parallel computers4.5 Reactive transport modeling in CO2 sequestration4.6 Discontinuous Galerkin methods4.7 Exercises for reservoir simulator designingReferencesFurther reading5. Recent progress in multiscale and mesoscopic reservoir simulation5.1 Upscaling technique5.2 Generalized multiscale finite element methods for porous media5.3 Multipoint flux approximation methods5.4 Lattice Boltzmann methodReferencesFurther reading6. Recent progress in machine learning applications in reservoir simulation6.1 Local-similarity-based porous structure reconstruction6.2 Numerical reconstruction of porous structure6.3 Procedures of sparse representation reconstructionReferencesFurther reading7. Recent progress in accelerating flash cal culation using deep learning algorithms7.1 Accelerated flash calculation using deep learning algorithm with experimental data as input7.2 Accelerated flash calculation using deep learning algorithm with flash data as input7.3 Realistic case studies

Product details

  • Edition: 1
  • Latest edition
  • Published: June 18, 2020
  • Language: English

About the authors

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Shuyu Sun

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
Affiliations and expertise
King Abdullah University of Science and Technology. Thuwal, Saudi Arabia

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Tao Zhang

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
Affiliations and expertise
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

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