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Reservoir Simulations,

Edition 1 Machine Learning and Modeling

Editors: By Shuyu Sun and Tao Zhang

Publication Date: 15 Jun 2020

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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.

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.

About the author

By Shuyu Sun, King Abdullah University of Science and Technology. Thuwal, Saudi Arabia and Tao Zhang, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

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

ISBN: 9780128209578

Page Count: 340

Retail Price :

9780128191507; 9780128155639; 9780128138687; 9780128022382

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