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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.
- 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.
Reservoir engineers; graduate-level petroleum engineers; computer scientists; petroleum researchers; data analysts in oil and gas research
1.3 Single-phase rock properties
1.5 Fluid displacement processes
1.6 Multiphase rock/fluid properties
2. Review of classical reservoir simulation
2.1 Sharp interface models
2.2 Cahn-HILLIARD-BASED diffuse interface models
2.3 Dynamic Van der Waals theory
2.4 Multiphase porous flow solvers
2.5 Wellbore modeling
2.6 Solute transport in porous media
2.7 Dynamic sorption in porous media
2.8 Black oil model
3. Recent progress in pore scale reservoir simulation
3.1 Phase equilibria in subsurface reservoirs
3.2 Stable dynamic NVT algorithm with capillarity
3.3 Multicomponent two-phase diffuse interface models based on Peng-Robinson equation of state
3.4 Multiphase flow with partial miscibility
4. Recent progress in Darcy’s scale reservoir simulation
4.1 Introductions on popular finite element methods
4.2 Links between finite-difference methods and finite element methods
4.3 Improved IMPES scheme
4.4 Bound-preserving fully implicit reservoir simulation on parallel computers
4.5 Reactive transport modeling in CO2 sequestration
4.6 Discontinuous Galerkin methods
4.7 Exercises for reservoir simulator designing
5. Recent progress in multiscale and mesoscopic reservoir simulation
5.1 Upscaling technique
5.2 Generalized multiscale finite element methods for porous media
5.3 Multipoint flux approximation methods
5.4 Lattice Boltzmann method
6. Recent progress in machine learning applications in reservoir simulation
6.1 Local-similarity-based porous structure reconstruction
6.2 Numerical reconstruction of porous structure
6.3 Procedures of sparse representation reconstruction
7. Recent progress in accelerating flash cal culation using deep learning algorithms
7.1 Accelerated flash calculation using deep learning algorithm with experimental data as input
7.2 Accelerated flash calculation using deep learning algorithm with flash data as input
7.3 Realistic case studies
- No. of pages:
- © Gulf Professional Publishing 2020
- 15th June 2020
- Gulf Professional Publishing
- Paperback ISBN:
- eBook ISBN:
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
King Abdullah University of Science and Technology. Thuwal, Saudi Arabia
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
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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