Reservoir Simulations: Machine Learning and Modeling

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 simulationWorld leading modeling and algorithms to study flow and transport behaviors in reservoirs, as well as the application of machine learningGain practical knowledge with hand-on trainings on modeling and simulation through well designed case studies and numerical examples.

Preface 1. Introduction 2. Review of classical reservoir simulation 3. Recent progress in pore scale reservoir simulation 4. Recent progress in Darcy’s scale reservoir simulation 5. Recent progress in multiscale and mesoscopic reservoir simulation 6. Recent progress in machine learning applications in reservoir simulation 7. Recent progress in accelerating flash cal culation using deep learning algorithms