Smart Proxy Modeling: Artificial Intelligence and Machine Learning in Numerical Simulation

Description

Numerical simulation models are used in all engineering disciplines for modeling physical phenomena to learn how the phenomena work, and to identify problems and optimize behavior. Smart Proxy Models provide an opportunity to replicate numerical simulations with very high accuracy and can be run on a laptop within a few minutes, thereby simplifying the use of complex numerical simulations, which can otherwise take tens of hours. This book focuses on Smart Proxy Modeling and provides readers with all the essential details on how to develop Smart Proxy Models using Artificial Intelligence and Machine Learning, as well as how it may be used in real-world cases. Covers replication of highly accurate numerical simulations using Artificial Intelligence and Machine Learning Details application in reservoir simulation and modeling and computational fluid dynamics Includes real case studies based on commercially available simulators Smart Proxy Modeling is ideal for petroleum, chemical, environmental, and mechanical engineers, as well as statisticians and others working with applications of data-driven analytics. Cover Half Title Title Page Copyright Page Dedication Contents Preface About the author 1. Artificial Intelligence and Machine Learning 1.1. Machine learning 1.2. Artificial neural networks 1.3. Deep learning 1.4. Fuzzy clustering 1.5. Featuring generation 1.6. Partitioning 1.7. Note 2. Numerical simulation and modeling 2.1. Numerical reservoir simulation (NRS) 2.2. Computational fluid dynamics (CFD) 3. Proxy modeling 3.1. Traditional proxy modeling 3.1.1. Reduced order model (ROM) 3.1.2. Response surface method (RSM) 3.2. Smart proxy modeling 4. Smart Proxy Modeling for numerical reservoir simulation 4.1. Well-based smart proxy modeling 4.2. Cell-based smart proxy modeling 5. Smart Proxy Modeling for computational fluid dynamics (CFD) References Index