Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence

................................................................................................................................. ii ACKNOWLEDGEMENT ............................................................................................................ iv CHAPTER

[1]  Shahab D. Mohaghegh,et al.  Virtual-Intelligence Applications in Petroleum Engineering: Part 1—Artificial Neural Networks , 2000 .

[2]  Shahab D. Mohaghegh,et al.  Quantifying Uncertainties Associated With Reservoir Simulation Studies Using a Surrogate Reservoir Model , 2006 .

[3]  Shahab D. Mohaghegh,et al.  Virtual-Intelligence Applications in Petroleum Engineering: Part 2—Evolutionary Computing , 2000 .

[4]  P. Holmes,et al.  Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 1996 .

[5]  L. Durlofsky,et al.  Reduced-Order Modeling for Compositional Simulation by Use of Trajectory Piecewise Linearization , 2014 .

[6]  Guillaume Caumon,et al.  Reservoir flow uncertainty assessment using response surface constrained by secondary information , 2008 .

[7]  Arnold Heemink,et al.  Reduced models for linear groundwater flow models using empirical orthogonal functions , 2004 .

[8]  R. Markovinović System-theoretical model reduction for reservoir simulation and optimization , 2009 .

[9]  Rainer Helmig,et al.  An integrative approach to robust design and probabilistic risk assessment for CO2 storage in geological formations , 2011 .

[10]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[11]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[12]  Patricia Elva Carreras,et al.  Tahiti: Development Strategy Assessment Using Design of Experiments and Response Surface Methods , 2006 .

[13]  Louis J. Durlofsky,et al.  Linearized reduced-order models for subsurface flow simulation , 2010, J. Comput. Phys..

[14]  K. Aziz,et al.  Petroleum Reservoir Simulation , 1979 .

[15]  Keith Holdaway,et al.  Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data-Driven Models , 2014 .

[16]  L. Sirovich TURBULENCE AND THE DYNAMICS OF COHERENT STRUCTURES PART I : COHERENT STRUCTURES , 2016 .

[17]  Louis J. Durlofsky,et al.  Reduced-order modeling for thermal recovery processes , 2013, Computational Geosciences.

[18]  H. Tran,et al.  Modeling and control of physical processes using proper orthogonal decomposition , 2001 .

[19]  Shahab D. Mohaghegh,et al.  Reservoir Simulation and Modeling Based on Pattern Recognition , 2011 .

[20]  Shahab D. Mohaghegh,et al.  Petroleum reservoir characterization with the aid of artificial neural networks , 1996 .

[21]  Nikolaos V. Sahinidis,et al.  Uncertainty Quantification in CO2 Sequestration Using Surrogate Models from Polynomial Chaos Expansion , 2013 .

[22]  Harm Dijk,et al.  Structured Uncertainty Assessment for Fahud Field through the Application of Experimental Design and Response Surface Methods , 2005 .

[23]  M. A. Cardoso,et al.  Development and application of reduced-order modeling procedures for reservoir simulation , 2009 .

[24]  Louis J. Durlofsky,et al.  Enhanced linearized reduced-order models for subsurface flow simulation , 2011, J. Comput. Phys..

[25]  Tai-hoon Kim,et al.  Use of Artificial Neural Network in Pattern Recognition , 2010 .

[26]  R. Chalaturnyk,et al.  Reservoir Characterization: Application of Extended Kalman Filter and Analytical Physics- Based Proxy Models in Thermal Recovery , 2011 .

[27]  Christopher M. Bishop,et al.  Neural Network for Pattern Recognition , 1995 .

[28]  Mort Webster,et al.  Application of the probabilistic collocation method for an uncertainty analysis of a simple ocean model , 1996 .

[29]  Damsleth Elvind,et al.  Maximum Information at Minimum Cost: A North Sea Field Development Study With an Experimental Design , 1992 .

[30]  Rick Chalaturnyk,et al.  A Mathematical Improvement to SAGD Using Geomechanical Modelling , 2010 .

[31]  T. Ertekin,et al.  A Neurosimulation Tool for Predicting Performance in Enhanced Coalbed Methane and CO2, Sequestration Projects , 2005 .

[32]  Y. Hajizadeh,et al.  Assisted History Matching Using Pattern Recognition Technology , 2015 .

[33]  Larry W. Lake,et al.  Response Surface Methods for Upscaling Heterogeneous Geologic Models , 1999 .

[34]  John R. Fanchi Fundamentals of Reservoir Simulation , 2006 .

[35]  Soodabeh Esmaili,et al.  Production History Matching and Forecasting of Shale Assets Using Pattern Recognition , 2013 .

[36]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[37]  Clayton V. Deutsch,et al.  Uncertainty Assessment of SAGD Performance Using a Proxy Model Based on Butler's Theory , 2008 .

[38]  Shahab D. Mohaghegh,et al.  Development of Surrogate Reservoir Model (SRM) for fast track analysis of a complex reservoir , 2009 .

[39]  M. Eldred,et al.  Comparison of Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Quantification , 2009 .

[40]  Louis J. Durlofsky,et al.  Computational Optimization of Shale Resource Development Using Reduced-Physics Surrogate Models , 2012 .

[41]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[42]  Ryadh Ben Salem,et al.  Uncertainty Quantification Workflow for Mature Oil Fields: Combining Experimental Design Techniques and Different Response Surface Models , 2013 .

[43]  Grant S. Bromhal,et al.  Uncertainty Analysis of a CO2 Sequestration Project Using Surrogate Reservoir Modeling Technique , 2012 .

[44]  R. Markovinovic,et al.  Use of POD in control of flow through porous media , 2006 .

[45]  Jan Dirk Jansen,et al.  Generation of Low-Order Reservoir Models Using System-Theoretical Concepts , 2003 .

[46]  R. Butler,et al.  The rise of interfering solvent chambers: solvent analog model of steam-assisted gravity drainage , 1993 .

[47]  V. A. Krasil’nikov,et al.  Atmospheric turbulence and radio-wave propagation , 1962 .

[48]  Hector Klie Unlocking Fast Reservoir Predictions via Nonintrusive Reduced-Order Models , 2013, ANSS 2013.

[49]  Burak Yeten,et al.  A Comparison Study on Experimental Design and Response Surface Methodologies , 2005 .

[50]  C. Chu,et al.  Prediction of Steamflood Performance in Heavy Oil Reservoirs Using Correlations Developed by Factorial Design Method , 1990 .

[51]  Jacob K. White,et al.  A trajectory piecewise-linear approach to model order reduction and fast simulation of nonlinear circuits and micromachined devices , 2001, ICCAD 2001.

[52]  Yalchin Efendiev,et al.  Nonlinear Complexity Reduction for Fast Simulation of Flow in Heterogeneous Porous Media , 2013, ANSS 2013.

[53]  Karen Willcox,et al.  Reduced-order, trajectory piecewise-linear models for nonlinear computational fluid dynamics , 2004 .

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

[55]  Edward M. Greitzer,et al.  Reduced-order , trajectory piecewise-linear models for nonlinear computational fluid dynamics . by , 2022 .

[56]  P. A. Slotte,et al.  Response Surface Methodology Approach for History Matching and Uncertainty Assessment of Reservoir Simulation Models , 2008 .

[57]  Stefan Bachu,et al.  Sequestration of CO2 in geological media: criteria and approach for site selection in response to climate change , 2000 .

[58]  P. Astrid,et al.  Reduction of process simulation models : a proper orthogonal decomposition approach , 2004 .

[59]  Jan Dirk Jansen,et al.  Accelerating iterative solution methods using reduced‐order models as solution predictors , 2006 .

[60]  Tadeusz W Patzek,et al.  Prediction of Formation Damage During Fluid Injection into Fractured, Low Permeability Reservoirs via Neural Networks , 1996 .

[61]  Christopher J. Jablonowski,et al.  A method for integrating response surfaces into optimization models with real options: A case study in gas flooding , 2010 .