Reservoir Simulations Machine Learning and Modeling
Auteurs : Sun Shuyu, Zhang Tao
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.
Preface1. Introduction2. Review of classical reservoir simulation3. Recent progress in pore scale reservoir simulation4. Recent progress in Darcy’s scale reservoir simulation5. Recent progress in multiscale and mesoscopic reservoir simulation6. Recent progress in machine learning applications in reservoir simulation7. Recent progress in accelerating flash cal culation using deep learning algorithms
Reservoir engineers; graduate-level petroleum engineers; computer scientists; petroleum researchers; data analysts in oil and gas research
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
- 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.
Date de parution : 06-2020
Ouvrage de 340 p.
19x23.3 cm
Thème de Reservoir Simulations :
Mots-clés :
?Black oil model; Convolutional neural network; Darcy’s scale; Deep learning algorithm; Diffuse interface models; Discontinuous Galerkin; FOV; Flash calculation; Galerkin finite element methods; Generalized multiscale finite element method; High- and low-resolution images; IMPES; Image-degradation mechanism; LBM; Mass transport; Max-pooling; Modeling and solver; Multicomponent thermodynamic equilibrium; Multipoint flux approximation; NVT flash calculation; Network optimization; Partial miscibility; Phase equilibria; Reactive transport; Recent progress; Reservoir simulation; SAV method; SEM; Two-phase porous flow; Upscaling; Van der Waals theory
