| Description |
1 online resource (viii, 332 pages) : illustrations |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Summary |
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. |
| Note |
Online resource; title from digital title page (viewed on July 24, 2020). |
| Contents |
Front Cover -- Reservoir Simulations -- Copyright Page -- Contents -- Preface -- 1. Introduction -- 1.1 Introduction -- 1.2 Definitions -- 1.2.1 General definitions -- 1.3 Single-phase rock properties -- 1.4 Wettability -- 1.5 Fluid displacement processes -- 1.6 Multiphase rock/fluid properties -- 1.6.1 Two-phase relative permeability -- 1.6.2 Three-phase relative permeability -- 1.7 Terms -- 1.7.1 Navier-Stokes equations -- 1.7.1.1 Conservation of mass (continuity equation) -- 1.7.1.2 Conservation of linear momentum -- 1.7.1.3 Conservation of energy -- References -- Further reading |
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2. Review of classical reservoir simulation -- 2.1 Sharp interface models -- 2.1.1 Modeling of two-phase flows at pore scale -- 2.1.2 Sharp interface models and interfacial conditions -- 2.1.2.1 Sharp interface models -- 2.1.2.2 Interfacial conditions -- 2.1.3 Numerical methods for sharp interface models -- 2.1.3.1 Volume of fluid method -- 2.1.3.2 Level set method -- 2.1.3.3 Volume of fluid and level set method -- 2.1.3.4 Method of moving grids -- 2.1.3.5 Method of marker particles -- 2.1.3.6 Comparison among numerical methods -- 2.2 Cahn-Hilliard-based diffuse interface models |
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2.2.1 Motivation and derivation of the Cahn-Hilliard model -- 2.2.1.1 Background -- 2.2.1.2 Motivation of (time-dependent) Cahn-Hilliard equation -- 2.2.2 A formal derivation of the N-S/C-H model -- 2.2.2.1 Anisotropy of the stress tensor -- 2.2.2.2 A planar interface -- 2.2.3 Consistency of the N-S interfacial term and C-H model -- 2.2.3.1 Equilibrium condition and partial differential equation from Cahn-Hilliard model -- 2.2.3.2 Implication of equilibrium partial differential equation -- 2.2.3.3 Equation for mechanical equilibrium -- 2.2.3.4 Consistency of equilibrium conditions |
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2.2.4 The N-S/C-H model with boundary and initial conditions -- 2.2.4.1 No-slip boundary conditions -- 2.2.4.2 Momentum and mass balances -- 2.2.4.3 Generalized Navier boundary condition -- 2.2.4.4 Dynamic boundary conditions and nonpenetration boundary conditions -- 2.2.4.5 Dimensionless modeling equations and boundary conditions -- 2.3 Dynamic Van der Waals theory -- 2.3.1 Motivation -- 2.3.2 Introduction of dynamic Van der Waals theory -- 2.3.2.1 van der Waals theory -- 2.3.2.2 Gradient theory and equilibrium conditions -- 2.3.3 Generalized hydrodynamic equations |
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2.4 Multiphase porous flow solvers -- 2.4.1 Incompressible two-phase flow solver -- 2.4.1.1 Choice of primary variables -- 2.4.1.2 Modeling of wells -- 2.4.1.3 Pressure equation for two-phase flow -- 2.4.1.4 Saturation equation for two-phase flow -- 2.4.1.5 The implicit pressure, explicit saturation formulation for incompressible two-phase flow -- 2.4.1.6 A revised implicit pressure, explicit saturation formulation by Hoteit and Firoozabadi -- 2.4.2 The implicit pressure, explicit saturation method for compressible two-phase porous flow -- 2.4.2.1 Compressible two-phase flow equations |
| Subject |
Machine learning.
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Computer simulation.
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Computer Simulation |
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Machine Learning |
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Apprentissage automatique.
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Simulation par ordinateur.
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simulation.
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Computer simulation
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Machine learning
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| Added Author |
Zhang, Tao, author.
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| Other Form: |
Print version: 0128209577 9780128209578 (OCoLC)1111948464 |
| ISBN |
9780128209622 (electronic book) |
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0128209623 (electronic book) |
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0128209577 |
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9780128209578 |
| Standard No. |
AU@ 000067628829 |
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