Integration of genetic algorithm and a coactive neuro-fuzzy inference system for permeability prediction from well logs data

Transport in Porous Media

Volumn 71, Issue 3, 2008, Pages 273-288

  Saemi, Mohsen ^a   Ahmadi, Morteza ^a  

^a TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Artificial intelligence; Coactive neuro fuzzy inference systems; Genetic algorithm; Permeability; Petrophysical data

Indexed keywords

CORE ANALYSIS; FLOW OF FLUIDS; FUZZY NEURAL NETWORKS; GENETIC ALGORITHMS; INFERENCE ENGINES; OIL FIELD DEVELOPMENT; OIL WELL LOGGING; RESERVOIR MANAGEMENT;

COACTIVE NEURO-FUZZY INFERENCE SYSTEM; PETROPHYSICAL DATA;

MECHANICAL PERMEABILITY;

CORE ANALYSIS; FLOW OF FLUIDS; FUZZY NEURAL NETWORKS; GENETIC ALGORITHMS; INFERENCE ENGINES; MECHANICAL PERMEABILITY; OIL FIELD DEVELOPMENT; OIL WELL LOGGING; RESERVOIR MANAGEMENT;

ARTIFICIAL INTELLIGENCE; FUZZY MATHEMATICS; GAS FIELD; GENETIC ALGORITHM; PERMEABILITY; PETROLEUM; RESERVOIR; WELL LOGGING;

EID: 38649088267     PISSN: 01693913     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11242-007-9125-4     Document Type: Article

References (30)

1. Bauer R.J. (1994). Genetic Algorithms and Investment Strategies. Wiley, New York
2. Bishop C. (1995). Neural Network for Pattern Recognition. Clarendon Press, Oxford
3. Burrows, W.R., Walmsley, J., Montpetit, J., Faucher M.: CART-NEURO-FUZZY statistical data modeling, Part 2: Results. In: Preprints, 14th Conference on Probability and Statistics in the Atmospheric Sciences, Phoenix, AZ, pp. 160-167. American Meteorological Society, Boston, MA (1998)
4. Camps-Valls G., Chova L.G., Maravilla J.C., Guerrero J.D., Olivas E.S., Chorda L.A. and Moreno J. (2004). Robust support vector method for hyperspectral data classification and knowledge discovery. Trans. Geosci. Rem. Sens. 42(7): 1530-1542
5. Carman P.C. (1937). Fluid flow through granular beds. Tran. Inst. Chem. Eng. 15: 150-166
6. Jong K.A.D (1992). Are genetic algorithms function optimizers?. In: Manner, R. and Manderick, B. (eds) Parallel Problem Solving from Nature 2., pp. Elsevier, Amsterdam
7. Duda R.O., Hart P.E. and Stork D.G. (2001). Pattern Classification. Wiley-Interscience Publications, New York
8. Ghezelayagh H. and Lee K.Y. (1999). Training neuro-fuzzy boiler identifier with genetic algorithm and error back-propagation. IEEE Pow. Eng. Soc. Summer Meeting 2: 978-982
9. Golderg D.E. (1989). Genetic Algorithm in Search, Optimization & Machine Learning. Addison-Wesley, Reading, MA
10. Gorzalczany M.B. and Gradzki P.A. (2000). Neuro-fuzzy-genetic classifier for technical applications. Proc. IEEE Int. Conf. Ind. Technol. 1: 503-508
11. Gupta J.N.D. and Sexton R.S. (1999). Comparing backpropagation with a genetic algorithm for neural network training. Omega 27(6): 679-684
12. Heckerling P.S., Gerber B.S., Tape T.G. and Wigton R.S. (2004). Use of genetic algorithms for neural networks to predict community-acquired pneumonia. Art. Int. Med. 30: 71-84
13. Hertz A. and Kobler D. (2000). A framework for the description of evolutionary algorithms. J. Oper. Res. 126(1): 1-12
14. Holland J.H. (1975). Adaptation in Neural and Artificial Systems. The University of Michigan Press, Ann Arbor, Michigan
15. Jagielska I., Matthews C. and Whitfort T. (1999). An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems. Neurocomposites 24: 37-54
16. Jang, J.S.R., Sun, C.T.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Englewood Cliffs, NJ (1997)
17. Javadi A.A., Farmani R. and Tan T.P. (2005). A hybrid intelligent genetic algorithm. Adv. Eng. Inf. 19: 255-262
18. Kaufmann, A., Gupta, M.M.: Introduction to Fuzzy Arithmetic. V.N. Reinhold, New York (1985)
19. Kim K.J. and Han I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Exp. Sys. Appl. 19(2): 125-132
20. Mamdani E.H. and Assilian S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Machine Stud. 7(1): 1-13
21. Michalewicz Z. (1992). Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin
22. Nikravesh M. (2004). Soft computing-based computational intelligent for reservoir characterization. Exp. Syst. Appl. 26: 19-38
23. Oh S.K., Pedrycz W. and Park H.S. (2003). Hybrid identification in fuzzy-neural networks. Fuzzy Sets Syst. 138: 399-426
24. Roger J.J.S., Chuen-Tsai S. and Eiji M. (1997). Neuro-Fuzzy and Soft-Computing. Prentice-Hall, Englewood Cliffs, NJ
25. Sexton R.S., Dorsey R.E. and Johnson J.D. (1998). Toward global optimization of neural networks: A comparison of the genetic algorithm and backpropagation. Dec. Supp. Syst. 22(2): 171-185
26. Tang A.M., Quek C. and Ng G.S. (2005). GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms. Exp. Sys. Appl. 29: 769-781
27. Timur, A.: An investigation of permeability, porosity and 1273 residual water saturation relationships. Transactions SPWLD, 1274 Paper K (1968)
28. Wyllie M.R.J. and Rose W.D. (1950). Some theoretical considerations related to the quantitative evaluations of the physical characteristics of reservoir rock from electric log data. Trans. Am. Inst. Mining Eng. (AIME) 189: 105-118
29. Xie, D., Wilkinson, D., Yu, T., Ramon, S.: Permeability estimation using a hybrid genetic programming and fuzzy/neural inference approach. In: SPE 95167, Proceeding of SPE, Annual Technical Conference, Dallas, Texas, U.S.A., October (2005)
30. Yanga T., Linb H.C. and Chena M.L. (2006). Metamodelling approach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study. Rob. Comp-Int. Manuf. 22: 322-331