New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network

[1]  A. Abdulraheem,et al.  Applying Artificial Intelligence Techniques to Develop Permeability Predictive Models using Mercury Injection Capillary-Pressure Data , 2013 .

[2]  Amir Hatampour,et al.  Improving performance of a neural network model by artificial ant colony optimization for predicting permeability of petroleum reservoir rocks , 2013 .

[3]  Mohammad,et al.  Estimation of permeability using artificial neural networks and regression analysis in an Iran oil field , 2012 .

[4]  L. Klinkenberg The Permeability Of Porous Media To Liquids And Gases , 2012 .

[5]  Pejman Tahmasebi,et al.  A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation , 2012, Comput. Geosci..

[6]  M. Blunt,et al.  Artificial neural networks workflow and its application in the petroleum industry , 2012, Neural Computing and Applications.

[7]  A. Moradzadeh,et al.  Support vector regression for prediction of gas reservoirs permeability , 2012 .

[8]  M. Jamali Paghaleh,et al.  Prediction of Hydrocarbon Reservoirs Permeability Using Support Vector Machine , 2012 .

[9]  Ali Selamat,et al.  Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems , 2011, Comput. Ind..

[10]  J. Schmidhuber,et al.  A Novel Connectionist System for Unconstrained Handwriting Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[12]  E. M. El-M. Shokir,et al.  A Novel Model for Permeability Prediction in Uncored Wells , 2006 .

[13]  Jong-Se Lim,et al.  Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea , 2005 .

[14]  A. Datta-Gupta,et al.  The Role of Electrofacies, Lithofacies, and Hydraulic Flow Units in Permeability Predictions from Well Logs: A Comparative Analysis Using Classification Trees , 2005 .

[15]  James W. Jennings,et al.  Predicting Permeability From Well Logs in Carbonates With a Link to Geology for Interwell Permeability Mapping , 2003 .

[16]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..

[17]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[18]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[19]  A. Abdulraheem,et al.  Prediction of Rock Mechanical Parameters for Hydrocarbon Reservoirs Using Different Artificial Intelligence Techniques , 2009 .

[20]  William H. Press,et al.  Numerical recipes: the art of scientific computing, 3rd Edition , 2007 .

[21]  Pablo M. Carrica,et al.  A method to estimate permeability on uncored wells , 2003 .

[22]  Marc Fleury,et al.  RESISTIVITY IN CARBONATES: NEW INSIGHTS , 2002 .

[23]  J. D. Miller,et al.  Early Determination of Reservoir Flow Units Using an Integrated Petrophysical Method , 1997 .

[24]  J. E. Glynn,et al.  Numerical Recipes: The Art of Scientific Computing , 1989 .

[25]  W. P. Biggs,et al.  Using Log-Derived Values Of Water Saturation And Porosity , 1967 .