A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction

Information Fusion

Volumn 16, Issue 1, 2014, Pages 29-45

  Olatunji, S O ^a   Selamat, Ali ^a   Abdulraheem, Abdulazeez ^b  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

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

Author keywords

Extreme learning machines (ELM); Feedforward neural networks; Hybrid intelligent systems; Permeability; Type 2 fuzzy logic systems; Well logs

Indexed keywords

CARBONATE RESERVOIR; COMPARATIVE STUDIES; EXTREME LEARNING MACHINE; HYBRID INTELLIGENT SYSTEM; MODEL UNCERTAINTIES; PERMEABILITY PREDICTION; TYPE-2 FUZZY LOGIC SYSTEM; WELL LOGS;

FEEDFORWARD NEURAL NETWORKS; FORECASTING; HYBRID SYSTEMS; INTELLIGENT SYSTEMS; KNOWLEDGE ACQUISITION; MECHANICAL PERMEABILITY; STATISTICAL TESTS; SUPPORT VECTOR MACHINES; UNCERTAINTY ANALYSIS; WELL LOGGING;

FUZZY LOGIC;

EID: 84887049157     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2012.06.001     Document Type: Article

References (69)

1. A. Giovanni, and L. Vincenzo Using FML and fuzzy technology in adaptive ambient intelligence environments International Journal of Computational Intelligence Research 1 2005 171 182
2. N.N. Karnik, J.M. Mendel, Type-2 fuzzy logic systems: type-reduction, in: 1998 IEEE International Conference on Systems, Man and Cybernetics, 1998.
3. Y. Li, X. Sun, J. Hua, and C. Gong Modelling redundant structure in ecosystem by type-2 fuzzy logic system Ecological Modeling 2007
4. Q. Liang, N.N. Karnik, and J.M. Mendel Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems: applications and reviews IEEE Transactions on Systems, Man and Cybernetics: Part C 30 2000 329 339
5. Q. Liang, and J.M. Mendel Interval type-2 fuzzy logic systems: Theory and design IEEE Transactions Fuzzy Systems 8 2000 535 550
6. Q. Liang, and J.M. Mendel Overcoming Time-Varying Co-channel Interference Using Type-2 Fuzzy Adaptive Filters IEEE Transactions on Circuits and Systems 2000
7. J.M. Mendel Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions 2001 Prentice-Hall
8. M.H. Fazel Zarandi, B. Rezaee, I.B. Turksen, and E. Neshat A type-2 fuzzy rule-based expert system model for stock price analysis Expert Systems with Applications 36 2009 139 154
9. J.M. Mendel, Fuzzy Sets for Words: A New Beginning, in: The IEEE conference on Fuzzy Systems, 2003.
10. W. Dongrui, and J.M. Mendel A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets Information Sciences 178 2008 381 402
11. M.R. Mashinchi, and Ali Selamat Constructing a Customer's Satisfactory Evaluator System Using GA-based Fuzzy Artificial Neural Networks World Applied Sciences Journal 5 2008 432 440
12. M.R. Mashinchi, and Ali Selamat An Improvement of Genetic-based Learning Method for Fuzzy Artificial Neural Networks Applied Soft Computing, Elsevier Science 2009
13. S.O. Olatunji, A. Selamat, and A. Abdulraheem Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems Computers in Industry 62 2011 147 163
14. S.O. Olatunji, A. Selamat, and A.A.A. Raheem Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems Expert Systems with Applications 38 2011 10911 10922
15. K.I. Ali, R. Mohammadreza, and A.M. Seyed A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field Journal of Geophysics Engineering 2006 356 369
16. G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 2004.
17. G.B. Huang, Q.Y. Zhu, K.Z. Mao, C.K. Siew, P.N. Saratchandran, and N. Sundararajan Can threshold networks be trained directly? IEEE Transactions Circuits Syst. II 53 2006 187 191
18. G.B. Huang, Q.Y. Zhu, and C.K. Siew Extreme learning machine: Theory and applications Neurocomputing, ELSEVIER 70 2006 489 501
19. J.S. Lim, J. Kim, Reservoir porosity and permeability estimation from well logs using fuzzy logic and neural networks, in: The Society of Petroleum Engineering (SPE), Asia Pacific Oil and Gas Conference and Exhibition, Perth, Australia, 2004.
20. S.O.Olatunji, S. Ali, A. Abdul Azeez, Modeling Permeability Prediction Using Extreme Learning Machines, in: Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, AMS2010, IEEE, Kota-Kinabalu, Malaysia, 2010, pp. 29-33.
21. N. Hurtado, M. Aldana, and J. Torres Comparison between neuro-fuzzy and fractal models for permeability prediction Computational Geosciences 13 2009 181 186
22. S. Mohsen, A. Morteza, and Y.V. Ali Design of neural networks using genetic algorithm for the permeability estimation of the reservoir Journal of Petroleum Science and Engineering 2007 97 105
23. B. Balan, S. Mohaghegh, S. Ameri, State-of-the-Art in permeability determination from well log data: part I-A comparative study, model development in: SPE Eastern Regional Conference and Exhibition, West Virginia, 1995, pp. 17-21.
24. B. Balan, S. Mohaghegh, S. Ameri, State-Of-The-Art in Permeability Determination From Well Log Data: Part 2 - Verifiable, Accurate PermeabiIity Predictions, the Touchstone of All Models, in: SPE Eastern Regional Conference and Exhibition, West Virginia, USA, 1995, pp. 104 -109.
25. U. Ahmed, S.F. Crary, and G.R. Coates Permeability Estimation: The Various Sources and Their Interrelationships Journal of Petroleum Technology 1991
26. G.R. Coates, and J.L. Dumanoir A New Approach to Improved Log-Derived Permeability The Log Analyst 1974
27. W.A. Wendt, S. Sakurai, and P.H. Nelson Permeability Prediction From Well Logs Using Multiple Regression 1986 Academic Press New York
28. A.E. El-Sebakhy, A. Abdulraheem, M. Ahmed, A. Al-Majed, P. Raharja, F. Azzedin, T. Sheltami, Functional Network as a Novel Approach for Prediction of Permeability in a Carbonate Reservoir, in: SPE Conference, Bahrain, 2007.
29. H.C. Chang, D.C. Kopaska-Merkel, H.C. Chen, and S.R. Durrans Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system Computers and Geosciences, Elsevier 26 2000 591 601
30. S. Mohaghegh, R. Arefi, S. Ameri, and R. Nutter Petroleum Reservoir Characterization with the Aid of Artificial Neural Networks Journal of Petroleum Science & Engineering 16 1996 263 274
31. A.E. El-Sebakhy Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme Journal of Petroleum Science and Engineering 64 2009 25 34
32. N.N. Karnik, and J.M. Mendel Applications of type-2 fuzzy logic systems to forecasting of time-series Information Sciences 120 1999 89 111
33. N.N. Karnik, J.M. Mendel, and Q. Liang Type-2 fuzzy logic systems IEEE Transactions Fuzzy Systems 7 1999 643 658
34. G.E. Archie The Electrical Resistivity Log as an Aid in Determining Some Reservoir Characteristics Transactions on AIME 146 1942 54 62
35. M.P. Tixier Evaluation of Permeability From Electric-Log Resistivity Gradients Oil & Gas Journal 1949 111 113
36. M.R.J. Wyllie, and W.D. Rose Some theoretical considerations related to the quantitative evaluation of the physical characteristics of reservoir rock from electric log data Transactions on AIME 189 1950
37. S.J. Pirson Handbook of Well Log Analysis 1963 Prentice-Hall, Inc. Englewood Cliffs, NJ
38. A. Timur An investigation of permeability, porosity, and residual water saturation relationship for sandstone reservoirs The Log Analyst 9 1968 8 21
39. A.G. Bruce, P.M. Wong, Y. Zhang, H.A. Salisch, C.C. Fung, and T.D. Gedeon A state-of-the-art review of neural networks for permeability prediction APPEA Journal 2000
40. S.J. Rogers, J.H. Fang, C.L. Karr, and D.A. Stanley Determination of lithology from well logs using a neural network American Association of Petroleum Geologists Bulletin 76 1992 731 739
41. E. Abdelkader, T. Djebbar, and M. Amine Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud oil field Algeria Journal of Petroleum Science and Engineering 49 2005 122 141
42. A. Abdulraheem, A.E. El-Sebakhy, M. Ahmed, A. Vantala, P. Raharja, G. Korvin, Estimation of permeability from wireline logs in a middle eastern carbonate reservoir using fuzzy logic, in: The 15th SPE Middle East Oil & Gas Show and Conference, Kingdom of Bahrain, 2007.
43. S. Cuddy, The application of mathematics of fuzzy logic to petrophysics, in: SPWLA Annual Logging Symposium, 1997.
44. D. Tamhane, P.M. Wong, A.F., M. Nikravesh, Soft computing for intelligent reservoir characterization, in: SPE Asia Pacific Conference on Integrated Modelling for Asset Management, Yokohama, Japan, 2000.
45. J. Finol, Y. Ke Guo, and X.D. Jing A rule based fuzzy model for the prediction of petrophysical rock parameters Journal of Petroleum Science and Engineering 29 2001 97 113
46. T. Tagagi, M. Sugeno, Derivation of fuzzy control rules from human operator's control actions, in: Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, 1983, pp. 55-60.
47. M. Nikravesh, and F. Aminzadehm Mining and fusion of petroleum data with fuzzy logic and neural network agents Computers and Geosciences 2000
48. H. Yuantu, G. Tom, and W. Patrick An integrated neural-fuzzy-genetic- algorithm using hyper-surface membership functions to predict permeability in petroleum reservoirs Engineering Applications of Artificial Intelligence 14 2001 15 21
49. L. Jong-Se Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea Journal of Petroleum Science and Engineering 49 2005 182 192
50. H. Tarek, F. Anifowose, and F. Kanaan Hybrid computational models for the characterization of oil and gas reservoirs Expert Systems with Applications 37 2010 5353 5363
51. J.M. Mendel, and R.I.B. John Type-2 fuzzy sets made simple IEEE Transactions On Fuzzy Systems 10 2002
52. E. Cox Adaptive fuzzy systems IEEE Spectrum 1993
53. D. Dubois, and H. Prade Fuzzy Sets and Systems: Theory and Applications 1982 Academic Press New York
54. L.A. Zadeh The concept of a linguistic variable and its application to approximate reasoning - I Information Sciences 8 1975 199 249
55. L. Yu-ching, and L. Ching-hung System identification and adaptive filter using a novel fuzzy neuro system International Journal of Computational Cognition 5 2007 1 12
56. X. Chen, Y. Li, R. Harrison, and Y.-Q. Zhang Type-2 fuzzy logic-based classifier fusion for support vector machines Applied Soft Computing 8 2008 1222 1231
57. G.B. Huang, and H.A. Babri Feedforward neural networks with arbitrary bounded nonlinear activation functions IEEE Trans Neural Network 9 1998 224 229
58. Y. Mao, F. Xuqian, and L. Xue A class of self-stabilizing MCA learning algorithms IEEE Transactions on Neural Networks 17 2006 1634 1638
59. M.B. Li, G.B. Huang, P.N. Saratchandran, and N. Sundararajan Fully complex extreme learning machine Neurocomputing 68 2005 306 314
60. Stratify Sampling, 2008. < http://www.coventry.ac.uk/ec/∼nhunt/ meths/strati.html > (accessed on 27.01.2008).
61. Stratify Sampling, 2012. < http://en.wikipedia.org/wiki/Stratified- sampling > (accessed on 04.02.2012.
62. M.H.F. Zarandi, B. Rezaee, I.B. Turksen, and E. Neshat A type-2 fuzzy rule-based expert system model for stock price analysis Expert Systems with Applications 2007
63. S. Wermter, and R. Sun An overview of hybrid neural systems: hybrid neural systems S. Wermter, R. Sun, 2000 Springer Berlin/Heidelberg 1 13
64. P. Sandhya, A. Ajith, G. Crina, and T. Johnson Modeling intrusion detection system using hybrid intelligent systems Journal of Network and Computer Applications 30 2007 114 132
65. J. Feldman, and D. Bailey Layered hybrid connectionist models for cognitive science hybrid neural systems S. Wermter, R. Sun, 2000 Springer Berlin/Heidelberg 14 27
66. M. Panda, A. Abraham, and M.R. Patra A hybrid intelligent approach for network intrusion detection Procedia Engineering 30 2012 1 9
67. G.-B. Huang, MATLAB Codes of ELM Algorithm. < http://www.ntu.edu.sg/ home/egbhuang/ELM-Codes.htm >, 2006.
68. E.A. Osman, O.A. Abdel Wahab, M.A. Al-Marhoun, Prediction of oil properties using neural networks, in: SPE Middle East Oil Show, Bahrain, 2001.
69. R.O. Duda, P.E. Hart, and D.G. Stock Pattern Classification 2001 John Wiley and Sons New York