Developing a Grid-Based Surrogate Reservoir Model Using Artificial Intelligence
暂无分享,去创建一个
[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 .