Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

Applied Soft Computing

Volumn 26, Issue , 2015, Pages 483-496

  Anifowose, Fatai ^a   Labadin, Jane ^a   Abdulraheem, Abdulazeez ^b  

^a UNIVERSITI MALAYSIA SARAWAK   (Malaysia)

^b KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

Author keywords

Permeability; Porosity; Regularization parameter; Stacked generalization ensemble; Support vector machines

Indexed keywords

DECISION TREES; FORECASTING; GASOLINE; LEARNING SYSTEMS; MECHANICAL PERMEABILITY; PARAMETERIZATION; PETROLEUM INDUSTRY; PETROLEUM PROSPECTING; PETROLEUM RESERVOIR ENGINEERING; POROSITY; PREDICTIVE ANALYTICS; SUPPORT VECTOR MACHINES;

CORRELATION COEFFICIENT; GEOLOGICAL FORMATION; INDUSTRIAL PROBLEM; MACHINE LEARNING TECHNOLOGY; PETROLEUM RESERVOIR CHARACTERIZATION; PETROLEUM RESOURCES; REGULARIZATION PARAMETERS; STACKED GENERALIZATION;

RESERVOIR MANAGEMENT;

EID: 84912062615     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.10.017     Document Type: Article

References (87)

1. M. Re, and G. Valentini Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction JMLR: Workshop and Conference Proceedings Mach. Learn. Syst. Biol. 8 2010 98 111
2. A. Chandra, and X. Yao Evolving hybrid ensembles of learning machines for better generalisation Neurocomputing 69 2006 686 700
3. L. Nanni, and A. Lumini An ensemble of support vector machines for predicting virulent proteins Expert Syst. Appl. 36 2009 7458 7462
4. M.L. Martin, D. Santos-Muñoz, F. Valero, and A. Morata Evaluation of an ensemble precipitation prediction system over the Western Mediterranean area Atmos. Res. 98 2010 163 175
5. I. Zaier, C. Shu, T.B.M.J. Ouarda, O. Seidou, and F. Chebana Estimation of ice thickness on lakes using artificial neural network ensembles J. Hydrol. 383 3-4 2010 330 340
6. J. Sun, and H. Li Financial distress prediction using support vector machines: ensemble vs. individual Appl. Soft Comput. 12 8 2012 2254 2265
7. R. Polikar Scholarpedia 4 1 2009 2776
8. J.K. Ali Neural networks: a new tool for the petroleum industry? Proceedings of Society of Petroleum Engineers European Petroleum Computer Conference Aberdeen, UK 1994 217 231
9. L. Jong-Se Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea J. Pet. Sci. Eng. 49 2005 182 192
10. A. Abdulraheem, E. Sabakhi, M. Ahmed, A. Vantala, I. Raharja, and G. Korvin Estimation of permeability from wireline logs in a middle eastern carbonate reservoir using fuzzy logic Proceedings The 15th SPE Middle East Oil and Gas Show and Conference Bahrain, 11-14 March 2007
11. D. Kaviani, T.D. Bui, J.L. Jensen, and C.L. Hanks The application of artificial intelligence neural networks with small data sets: an example for analysis of fracture spacing in the Lisbourne formation, northeastern Alaska SPE J. Reserv. Eval. Eng. 11 3 2008 598 605
12. A. Khoukhi, M. Oloso, A. Abdulraheem, and M. El-Shafei Optimized adaptive neural networks for viscosity and gas/oil ratio curves prediction Proceedings IASTED International Conference Banff, Canada 2010 14 17
13. S. Abe Fuzzy LP-SVMs for multiclass problems Proceedings of the European Symposium on Artificial Neural Networks 2004 429 434
14. S. Mohsen, A. Morteza, and Y.V. Ali Design of neural networks using genetic algorithm for the permeability estimation of the reservoir J. Pet. Sci. Eng. 59 2007 97 105
15. M.B. Shahvar, R. Kharrat, and R. Mahdavi Incorporating fuzzy logic and artificial neural networks for building hydraulic unit-based model for permeability prediction of a heterogeneous carbonate reservoir Proceedings International Petroleum Technology Conference Doha, Qatar, 7-9 December 2009
16. T. Weldu, S. Ghedan, and O. Al-Farisi Hybrid AI and conventional empirical model for improved prediction of log-derived permeability of heterogeneous carbonate reservoir Proceedings prepared of the Society of Petroleum Engineers Production and Operations Conference and Exhibition Tunis, Tunisia, 8-10 June 2010
17. F. Anifowose, J. Labadin, and A. Abdulraheem A hybrid of functional networks and support vector machine models for the prediction of petroleum reservoir properties Proceedings of 11th International Conference on Hybrid Intelligent Systems, IEEExplore 2011 85 90
18. F. Anifowose, J. Labadin, and A. Abdulraheem Prediction of petroleum reservoir properties using different versions of adaptive neuro-fuzzy inference system hybrid models Int. J. Comput. Inform. Syst. Ind. Manag. Appl. 5 2013 413 426
19. L. Rokach Pattern Classification Using Ensemble Methods 2009 World Scientific Publishing Co.
20. C. Caragea, J. Sinapov, A. Silvescu, D. Dobbs, and V. Honavar Glycosylation site prediction using ensembles of support vector machine classifiers BMC Bioinform. 8 438 2007 doi:10.1186/1471-2105-8-438
21. S. Chen, W. Wang, and H. Zuylen Construct support vector machine ensemble to detect traffic incident Exp. Syst. Appl. 36 2009 10976 10986
22. J. Wu, and J.M. Rehg Object detection C. Zhang, Y. Ma, Ensemble Machine Learning: Methods and Applications 2012 Springer Science+Business Media, LLC 10.1007/978-1-4419-9326-7 8
23. V. Landassuri-Moreno, and J.A. Bullinaria Neural network ensembles for time series forecasting Proceedings of GECCO'09 Montréal Québec, Canada 2009 8 12
24. L. Yu, S. Wang, and K.K. Lai Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm Energy Econ. 30 2008 2623 2635
25. L. Breiman Bagging predictors Mach. Learn. 24 2 1996 123 140
26. G. Liang, X. Zhu, and C. Zhang An empirical study of bagging predictors for different learning algorithms Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence San Francisco, California, USA 2011 1802 1803
27. T. Helmy, F. Anifowose, and K. Faisal Hybrid computational models for the characterization of oil and gas reservoirs Int. J. Exp. Syst. Appl. 37 2010 5353 5363
28. S. Mohaghegh, and S. Ameri Artificial neural network as a valuable tool for petroleum engineers Unsolicited paper for Society of Petroleum Engineers: 1-6 1995
29. F. Anifowose, and A. Abdulraheem A functional networks-type-2 fuzzy logic hybrid model for the prediction of porosity and permeability of oil and gas reservoirs Proceedings 2nd International Conference on Computational Intelligence, Modeling and Simulation, IEEEXplore 2010 193 198
30. F. Anifowose, and A. Abdulraheem Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization J. Nat. Gas Sci. Eng. 3 3 2011 505 517
31. H. Kim, S. Pang, H. Je, D. Kim, and S.Y. Bang Constructing support vector machine ensemble Pattern Recognit. 36 2003 2757 2767
32. Y. Peng A novel ensemble machine learning for robust microarray data classification Comput. Biol. Med. 36 2006 553 573
33. Y. Chen, and Y. Zhao A novel ensemble of classifiers for microarray data classification Appl. Soft Comput. 8 2008 1664 1669
34. G. Valentini, M. Muselli, and F. Ruffino Cancer recognition with bagged ensembles of support vector machines Neurocomputing 56 2004 461 466
35. K.W.D. Bock, and D.V. Poel Ensembles of probability estimation trees for customer churn prediction N. GarcIa-Pedrajas, al. et, IEA/AIE 2010, Part II, LNAI 6097 2010 Springer-Verlag 57 66
36. D. Pardoe, M. Ryoo, and R. Miikkulainen Evolving neural network ensembles for control problems Proceedings of the GECCO'05 Washington, DC, USA 2005 25 29
37. L. Baker, and D. Ellison Optimisation of pedotransfer functions using an artificial neural network ensemble method Geoderma 144 2006 212 224
38. L.L. Minku, and X. Yao DDD: a new ensemble approach for dealing with concept drift IEEE Trans. Knowl. Data Eng. 24 4 2012 619 633
39. M.D. Felice, and X. Yao Short-term load forecasting with neural network ensembles: a comparative study IEEE Comput. Intell. Mag. 6 3 2011 47 56
40. Z. Wang, V. Palade, and Y. Xu Neuro-fuzzy ensemble approach for microarray cancer gene expression data analysis Proceedings 2006 International Symposium on Evolving Fuzzy Systems, IEEEXplore 2006 241 246
41. A. Tsymbal, S. Puuronen, and D.W. Patterson Ensemble feature selection with the simple Bayesian classification Inf. Fusion 4 2003 87 100
42. P.D. Castro, G.P. Coelho, M.F. Caetano, and F.J.V. Zuben C. Jacob, ICARIS 2005, LNCS 3627 2005 469 482
43. Z. Bray, and P.O. Kristensson Using ensembles of decision trees to automate repetitive tasks in web applications Proceeding of the EICS'10 Berlin, Germany, June 19-23 2010
44. M. Heeswijk, Y. Miche, T. Lindh-Knuutila, P.A.J. Hilbers, T. Honkela, E. Oja, and A. Lendasse Adaptive ensemble models of extreme learning machines for time series prediction C. Alippi, ICANN 2009, Part II, LNCS 5769 2009 305 314
45. C.J. Burges A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discov. 2 1998 121 167
46. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer-Verlag
47. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 1st ed. 2000 Cambridge University Press UK
48. J. Taboada, J.M. Matías, C. Ordóñez, and P.J. García Creating a quality map of a slate deposit using support vector machines J. Comput. Appl. Maths. 20 4 2007 84 94
49. Y. Xing, X. Wu, and Z. Xu Multiclass least squares auto-correlation wavelet support vector machines Int. J. Innov. Comput. Inf. Control Express Lett. 2 4 2008 345 350
50. E. Alpaydin Introduction to Machine Learning 2nd ed. 2010 The MIT Press 224
51. V. Kecman Learning and Soft Computing Support Vector Machines, Neural Networks, and Fuzzy Logic Models 2001 MIT Press 541
52. T. Joachims Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms 2002 The Springer International Series in Engineering and Computer Science 40
53. J. Shawe-Taylor, and N. Cristianini Kernel Methods for Pattern Analysis 2004 Cambridge University Press
54. V.S. Cherkassky, and P. Mulier Learning from Data: Concepts, Theory and Methods 2nd ed. 2007 John Wiley & Sons
55. G. Brown, J.L. Wyatt, and P. Tino Managing diversity in regression ensembles J. Mach. Learn. Res. 6 2005 1621 1650
56. R. Polikar Ensemble based systems in decision making IEEE Circuits Syst. Mag. 3 2006 21 45
57. A. Liaw, and M. Wiener Classification and regression by random forest R News 2 3 2002 18 22
58. R. Polikar Ensemble learning C. Zhang, Y. Ma, Ensemble Machine Learning: Methods and Applications 8 2012 Springer Science+Business Media, LLC 1 34
59. L. Breiman Random forests Mach. Learn. 45 1 2001 5 32
60. T.K. Ho Random decision forest Proceedings of the 3rd International Conference on Document Analysis and Recognition Montreal, QC, 14-16 August 1995 278 282
61. T.K. Ho The random subspace method for constructing decision forests IEEE Trans. Pattern Anal. Mach. Intell. 20 8 1998 832 844
62. P. Sherrod DTREG Predictive Modeling Software 2008 324 pp., available from: www.dtreg.com
63. H. Park, S. Kwon, and K. Hyuk-Chul Complete gini-index text (GIT) feature-selection algorithm for text classification 2nd International Conference on Software Engineering and Data Mining 2010 366 371
64. D.G. Leibovici, L. Bastin, and M. Jackson Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data Comput. Geosci. 37 2011 382 389
65. R. Caruana, N. Karampatziakis, and A. Yessenalina An empirical evaluation of supervised learning in high dimensions Proceedings of the 25th International Conference on Machine Learning (ICML) Helsinki, Finland 2008
66. A. Altmann, L. Tolosi, O. Sander, and T. Lengauer Permutation importance: a corrected feature importance measure Bioinformatics 26 10 2010 1340 1347
67. H. Deng, G. Runger, and E. Tuv Bias of importance measures for multi-valued attributes and solutions Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN) 2011 293 300
68. L. Tolosi, and T. Lengauer Classification with correlated features: unreliability of feature ranking and solutions Bioinformatics 27 2011 1986 1994
69. A. Tsymbal, M. Pechenizkiy, and P. Cunningham Diversity in search strategies for ensemble feature selection Information Fusion 6 1 2005 83 98 Special issue on diversity in multiple classifier Systems
70. L.L. Minku, A.P. White, and X. Yao The impact of diversity on online ensemble learning in the presence of concept drift IEEE Trans. Knowl. Data Eng. 22 5 2010 730 742
71. E.K. Tang, P.N. Suganthan, and X. Yao An analysis of diversity measures Mach. Learn. 65 2006 247 271
72. T. Lofstrom, U. Johansson, and H. Bostrom On the use of accuracy and diversity measures for evaluating and selecting ensembles of classifiers Seventh International Conference on Machine Learning and Applications (ICMLA) 2008 127 132
73. S. Wang, and X. Yao Relationships between diversity of classification ensembles and single-class performance measures IEEE Trans. Knowl. Data Eng. 25 1 2013 206 219
74. H. Dutta Measuring diversity in regression ensembles B. Prasad, P. Lingras, A. Ram, Proceedings of the 4th Indian International Conference on Artificial Intelligence (IICAI 2009) Tumkur, Karnataka, India, 16-18 December 2009 2220 2236
75. L.I. Kuncheva, and C.J. Whitaker Ten measures of diversity in classifier ensembles: limits for two classifiers A DERA/IEE Workshop on Intelligent Sensor Processing 2001 10/1-10/10
76. F. Anifowose, and A. Abdulraheem Prediction of porosity and permeability of oil and gas reservoirs using hybrid computational intelligence models Proceedings SPE North Africa Technical Conference and Exhibition (NATC 2010) Cairo, Egypt 2010
77. E. Ip, I. Cadez, and P. Smyth Psychometric methods of latent variable modeling N. Ye, The Handbook of Data Mining 2003 Lawrence Erlbaum Associates 238
78. A. Khoukhi, and S. Albukhitan PVT properties prediction using hybrid genetic neuro-fuzzy systems Int. J. Oil Gas Coal Technol. 4 1 2011 47 63
79. V. Cherkassky, and Y. Ma Selection of Meta-Parameters for Support Vector Regression Proceedings of the ICANN 2002 687 693
80. A.W.F. Edwards Occam's bonus A. Zellner, H.A. Keuzenkamp, M. McAleer, Simplicity, Inference and Modeling: Keeping it Sophisticatedly Simple 2004 Cambridge University Press UK 128 132
81. B. Hamming What explains complexity? A. Zellner, H.A. Keuzenkamp, M. McAleer, Simplicity, Inference and Modeling: Keeping it Sophisticatedly Simple 2004 Cambridge University Press UK 120 127
82. H.A. Keuzenkamp, M. Mcaleer, and A. Zellner The enigma of simplicity A. Zellner, H.A. Keuzenkamp, M. McAleer, Simplicity, Inference and Modeling: Keeping it Sophisticatedly Simple 2004 Cambridge University Press UK 1 10
83. Least Squares SVM (LS-SVM), Basic Version available online, http://www.esat.kuleuven.be/sista/lssvmlab/ (accessed 25.12.12)
84. X. Peng, and Y. Wang A normal least squares support vector machine (NLS-SVM) and its learning algorithm Neurocomputing 72 2009 3734 3741
85. Bailer-Jones C.A.L., Statistical Methods, A Computer Course, Available from: http://www.mpiahd.mpg.de/~calj/statistical-methods-ss2011/lectures/05-regressi on.pdf (accessed 21.12.12).
86. MATLAB CENTRAL, Random Forest, Available at: http://www.mathworks.com/matlabcentral/fileexchange/31036-random-forest (accessed 04.12.12).
87. Netlab Toolbox Neural Computing Research Group, Information Engineering 2012 Aston University Birmingham, United Kingdom Available from: http://www.ncrg.aston.ac.uk/netlab (accessed 11.12.12)