Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part One: Proof of Concept; NETL-PUB-21574; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2017.

[1]  Masoud Rahimi,et al.  CFD and artificial neural network modeling of two-phase flow pressure drop☆ , 2009 .

[2]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[3]  M. Syamlal,et al.  MFIX documentation theory guide , 1993 .

[4]  Shahab D. Mohaghegh,et al.  Converting detail reservoir simulation models into effective reservoir management tools using SRMs; case study – three green fields in Saudi Arabia , 2014 .

[5]  Shahab D. Mohaghegh,et al.  Smart Proxy: An Innovative Reservoir Management Tool; Case Study of a Giant Mature Oilfield in the UAE , 2015 .

[6]  Shahab D. Mohaghegh,et al.  Full field reservoir modeling of shale assets using advanced data-driven analytics , 2016 .

[7]  Shahab D. Mohaghegh,et al.  Modeling pressure and saturation distribution in a CO 2 storage project using a Surrogate Reservoir Model (SRM) , 2014 .

[8]  Shahab D. Mohaghegh,et al.  Design and Development of An Artificial Neural Network for Estimation of Formation Permeability , 1995 .

[9]  Ng Niels Deen,et al.  Review of discrete particle modeling of fluidized beds , 2007 .

[10]  Shahab D. Mohaghegh,et al.  Coupling numerical simulation and machine learning to model shale gas production at different time resolutions , 2015 .

[11]  Aytekin Gel,et al.  Quantifying uncertainty of a reacting multiphase flow in a bench-scale fluidized bed gasifier: A Bayesian approach , 2017 .

[12]  Shahab D. Mohaghegh,et al.  Data-driven proxy at hydraulic fracture cluster level: A technique for efficient CO2- enhanced gas recovery and storage assessment in shale reservoir , 2015 .

[13]  Mohaghegh Artificial Neural Network As A Valuable Tool For Petroleum Engineers , 1995 .

[14]  Aytekin Gel,et al.  The Effect of Grid Resolution and Reaction Models in Simulation of a Fluidized Bed Gasifier Through Nonintrusive Uncertainty Quantification Techniques , 2016 .

[15]  William D. Fullmer,et al.  Quantitative assessment of fine-grid kinetic-theory-based predictions of mean-slip in unbounded fluidization , 2016 .

[16]  Grant S. Bromhal,et al.  Pattern Recognition and Data-Driven Analytics for Fast and Accurate Replication of Complex Numerical Reservoir Models at the Grid Block Level , 2014 .

[17]  Aytekin Gel,et al.  Applying uncertainty quantification to multiphase flow computational fluid dynamics , 2013 .

[18]  Arthur T. Andrews,et al.  Multiscale modeling of gas-fluidized beds , 2006 .

[19]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[20]  Ng Niels Deen,et al.  Numerical Simulation of Dense Gas-Solid Fluidized Beds: A Multiscale Modeling Strategy , 2008 .