Non-linear feature selection-based hybrid computational intelligence models for improved natural gas reservoir characterization
暂无分享,去创建一个
Jane Labadin | Abdulazeez Abdulraheem | Fatai Anifowose | A. Abdulraheem | J. Labadin | F. Anifowose
[1] Manfred G. Prammer,et al. Applying NMR Total and Effective Porosity to Formation Evaluation , 1997 .
[2] Chao Gao,et al. Attribute Reduction Based on the Fuzzy Information Filter Operators , 2008, ICIC.
[3] A. Timur,et al. An Investigation Of Permeability, Porosity, & Residual Water Saturation Relationships For Sandstone Reservoirs , 1968 .
[4] Yifei Wang,et al. A normal least squares support vector machine (NLS-SVM) and its learning algorithm , 2009, Neurocomputing.
[5] Tarek Helmy,et al. Hybrid computational models for the characterization of oil and gas reservoirs , 2010, Expert Syst. Appl..
[6] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[7] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[8] Thomas Graf,et al. Candidate Selection Using Stochastic Reasoning Driven by Surrogate Reservoir Models , 2011 .
[9] Emad A. El-Sebakhy. Functional networks as a novel data mining paradigm in forecasting software development efforts , 2011, Expert Syst. Appl..
[10] Ursula Iturrarán-Viveros,et al. The Gamma Test Applied to Select Seismic Attributes to Estimate Effective Porosity , 2005 .
[11] Gerard V. Trunk,et al. A Problem of Dimensionality: A Simple Example , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Zhang Chang-kai,et al. Seismic Attributes Selection Based On SVM For Hydrocarbon Reservoir Prediction , 2010 .
[13] Max Bramer,et al. Using J-pruning to reduce overfitting in classification trees , 2002, Knowl. Based Syst..
[14] Michael Andrew Christie,et al. Reservoir Modelling with Feature Selection: Kernel Learning Approach , 2011, ANSS 2011.
[15] Enrique F. Castillo,et al. Some Applications of Functional Networks in Statistics and Engineering , 2001, Technometrics.
[16] Djebbar Tiab,et al. Porosity and Permeability , 2004 .
[17] Ali Selamat,et al. Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems , 2011, Expert Syst. Appl..
[18] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[19] Kevin P. Dorrington,et al. A genetic algorithm/neural network approach to seismic attribute selection for well log prediction , 2002 .
[20] Ramón López de Mántaras,et al. A distance-based attribute selection measure for decision tree induction , 1991, Machine Learning.
[21] Djebbar Tiab,et al. Enhanced Reservoir Description: Using Core and Log Data to Identify Hydraulic (Flow) Units and Predict Permeability in Uncored Intervals/Wells , 1993 .
[22] David G. Stork,et al. Pattern Classification , 1973 .
[23] A. R. Gregory,et al. ELASTIC WAVE VELOCITIES IN HETEROGENEOUS AND POROUS MEDIA , 1956 .
[24] Wei Zhong Liu,et al. Bias in information-based measures in decision tree induction , 1994, Machine Learning.
[25] Allan P. White,et al. Technical Note: Bias in Information-Based Measures in Decision Tree Induction , 1994, Machine Learning.
[26] I. D. Gates,et al. Support vector regression to predict porosity and permeability: Effect of sample size , 2012, Comput. Geosci..
[27] Jane Labadin,et al. Prediction of Petroleum Reservoir Properties using Different Versions of Adaptive Neuro-Fuzzy Inference System Hybrid Models , 2013, CISIM 2013.
[28] Abdulazeez Abdulraheem,et al. A Functional Networks-Type-2 Fuzzy Logic Hybrid Model for the Prediction of Porosity and Permeability of Oil and Gas Reservoirs , 2010, 2010 Second International Conference on Computational Intelligence, Modelling and Simulation.
[29] Pedro Larrañaga,et al. A review of feature selection techniques in bioinformatics , 2007, Bioinform..
[30] J. W. Amyx,et al. Petroleum reservoir engineering : physical properties , 1960 .
[31] Heum Park,et al. Complete Gini-Index Text (GIT) feature-selection algorithm for text classification , 2010, The 2nd International Conference on Software Engineering and Data Mining.
[32] Enrique Castillo,et al. Functional Networks , 1998, Neural Processing Letters.
[33] J. A. Ajienka,et al. Well and Reservoir Parameters Estimation (K and Skin) Using the Statistical Diagnostic Approach Part II , 2010 .
[34] Abdulazeez Abdulraheem,et al. Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization , 2011 .
[35] Ian D. Gates,et al. Innovative Data-Driven Permeability Prediction in a Heterogeneous Reservoir , 2009 .
[36] Ali Selamat,et al. Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems , 2011, Comput. Ind..
[37] Enrique F. Castillo,et al. Optimal Transformations in Multiple Linear Regression Using Functional Networks , 2001, IWANN.
[38] Peter Grünwald,et al. A tutorial introduction to the minimum description length principle , 2004, ArXiv.
[39] Gábor Horváth,et al. A Robust LS-SVM Regression , 2007, IEC.
[40] Pasi Luukka,et al. Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..
[41] Mitchell E. Sprowso,et al. Decision Tree Analysis Of Exploration Activities , 1979 .
[42] Walter Rose,et al. Some Theoretical Considerations Related To The Quantitative Evaluation Of The Physical Characteristics Of Reservoir Rock From Electrical Log Data , 1950 .