Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs

International Journal of Computational Intelligence and Applications

Volumn 9, Issue 4, 2010, Pages 313-337

  Helmy, Tarek ^a,b   Fatai, Anifowose ^a  

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

^b TANTA UNIVERSITY   (Egypt)

Author keywords

FL; functional networks; Hybrid computational intelligence; reservoir characterization; support vector machine

Indexed keywords

COMPUTATIONAL INTELLIGENCE; CORRELATION COEFFICIENT; DATA SETS; EXECUTION TIME; FEATURE SPACE; FL; FUNCTIONAL APPROXIMATION; FUNCTIONAL NETWORK; HYBRID COMPUTATIONAL INTELLIGENCE; HYBRID MODEL; INFERENCE RULES; INPUT DATAS; LEAST-SQUARE FITTING; OIL AND GAS; OIL AND GAS RESERVOIR; PERMEABILITY PREDICTION; PREDICTION ACCURACY; PREDICTOR VARIABLES; RESERVOIR CHARACTERIZATION; TYPE-2 FUZZY LOGIC; WELL LOG DATA;

APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; ENERGY RESOURCES; FEATURE EXTRACTION; FORECASTING; FUZZY LOGIC; GAS PERMEABILITY; GEARS; INPUT OUTPUT PROGRAMS; MODELS; PETROLEUM DEPOSITS; PETROLEUM PROSPECTING; RESERVOIR MANAGEMENT; SUPPORT VECTOR MACHINES; WELL LOGGING;

PETROLEUM RESERVOIR ENGINEERING;

EID: 78349266079     PISSN: 14690268     EISSN: None     Source Type: Journal    
DOI: 10.1142/S1469026810002902     Document Type: Article

References (49)

1. J. Lim, Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea, J. Petrol. Sci. Eng. 49 (2005) 182-192.
2. D. Hosmer and S. Lemeshow, Applied Logistic Regression (John Wiley and Sons, 2000).
3. A. Salah, S. Rahman and K. Nath, An enhancement of k-nearest neighbor classification using genetic algorithm, Midwest Instruction and Computing Symp. (2005).
4. W. Duch, R. Adamczak and N. Jankowski, Initialization and optimization of mul-tilayered perceptrons, 3rd Conference on Neural Networks and Their Applications, Kule, Poland (1997).
5. L. Guojie, Radial basis function neural network for speaker verification, Masters Engineering Thesis, Nanyang Technological University, Singapore (2004).
6. J. M. Lauŕ?a and J. P. Duchessi, A Bayesian belief network for IT implementation decision support, Int. Fed. Infor. Proc. Int. Conf. Decision Support System (2006).
7. J. He, Computational intelligence, research interests, http://www.cs.bham.ac. uk/∼jxh/hejunrs.html (2008).
8. A. Giovanni and L. Vincenzo, Using FML and fuzzy technology in adaptive ambient intelligence environments, Int. J. Comput. Intell. Res. 1(2) (2005) 171-182.
9. L. A. Symeonidis and A. P. Mitkas, Agent intelligence through data mining, in Multia-gent Systems, Artificial Societies, and Simulated Organizations Series, Vol. 14 (International Book Series, 2005).
10. E. Triantaphyllou and G. Felici, Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Science+Business Media, Springer, 2006), p. 48.
11. T. Munakata, Fundamentals of the New Artificial Intelligence Neural, Evolutionary, Fuzzy and More, 2nd edn. (Science+Business Media, Springer, 2008).
12. G. Phillips-Wren, N. Ichalkaranje and C. Jain, Intelligent decision making: An AI-based approach, in Studies in Computational Intelligence, Vol. 97 (Springer-Verlag, Berlin, Heidelberg, 2008), pp. 88-355.
13. Y. Nong, The Handbook of Data Mining (Lawrence Erlbaum Associates, 2003), pp. 561-617.
14. L. Wang and X. Fu, Data Mining with Computational Intelligence (Springer-Verlag, Berlin, Heidelberg, 2005), p. 229.
15. S. Peddabachigari, A. Abraham, C. Grosan and J. Thomas, Modeling intrusion detection system using hybrid intelligent systems, J. Network Comput. Appl. 30 (2007) 114-132.
16. Z. Sun, K. Au and T. Choi, A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines, IEEE Trans. Syst. Man Cybernet. 37(5) (2007) 1321-1331.
17. J. K. Ali, Neural networks: A new tool for the petroleum industry? Proc. Soc. Petrol. Engineers European Petroleum Computer Conf., Aberdeen, U. K. (1994), pp. 217-231.
18. O. Abdul Kadir, D. Tiab and A. Mazouzi, Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud oil field, Algeria, J. Petrol. Sci. Eng. 49 (2005) 122-141.
19. Schlumberger Excellence in Educational Development, Science Lab Project for Permeability, http://www.seed.slb.com/en/scictr/lab/permeability/index.htm (2007).
20. S. Mohsen, A. Morteza and Y. V. Ali, Design of neural networks using genetic algorithm for the permeability estimation of the reservoir, J. Petrol. Sci. Eng. 59 (2007) 97-105.
21. A. Maqsood and C. Adwait, Using artificial intelligence to predict permeability from petrographic data, Comput. Geosci. 26 (2000) 915-925.
22. N. Karnik and J. Mendel, Type-2 fuzzy logic systems, IEEE Trans. Fuzzy Systems, Vol. 7 (1999).
23. J. Mendel, Type-2 fuzzy sets: Some questions and answers, IEEE Connections, Newsletter of the IEEE Neural Networks Society 1 (2003) 10-13.
24. M. F. Zarandi, B. Rezaee, I. B. Turksen and E. Neshat, A Type-2 fuzzy rule-based expert system model for stock price analysis, Expet. Sys. Appl., doi: 10.1016/j.eswa (2007), pp. 9-34.
25. J. H. Fang and H. C. Chen, Fuzzy modeling and the prediction of porosity and permeability from the compositional and textural attributes of sandstone, J. Petrol. Geol. 20 (1997) 185-204.
26. W. Littman, Introduction to support vector machines, Lecture Notes in Computer Science (LNCS ), Vol. 536 (2000).
27. H. M. Goda and H. R. Behrenbruch, Use of artificial intelligence techniques for predicting irreducible water saturation in Australian hydrocarbons basins, Society of Petroleum Engineers Asia Pacific Oil & Gas Conf. Exhibition, Jakarta, Indonesia (2007).
28. H. Jian and H. Wenfen, Novel approach to predict potentiality of enhanced oil recovery, Proc. Soc. Petrol. Engineers Intelligent Energy Conf. Exhibition, Amsterdam, The Netherlands (2006).
29. C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery 2 (1998) 121-167.
30. J. Taboada, J. M. Mat́?as, C. Ord́oñez and P. J. Garć?a, Creating a quality map of a slate deposit using support vector machines, J. Comput. Appl. Math. 204 (2007) 84-94.
31. E. Castillo, Functional networks, Neural Proc. Lett. 7 (1998) 151-159.
32. E. Castillo, S. Hadi and B. Lacruz, Optimal transformations in multiple linear regression using functional networks, Int. Work-Conf. Artificial and Natural Neural Networks, Lecture Notes in Computer Science, Series 2084 (2001) 316-324.
33. E. Castillo, J. Gutíerrez, S. Hadi and B. Lacruz, Some applications of functional networks in statistics and engineering, Technometrics 43 (2001) 10-24.
34. X. Chen, Y. Li, R. Harrison and Y. Q. Zhang, Type-2 fuzzy logic based classifier fusion for support vector machines, Appl. Soft Comput. J. (2007).
35. C. Salim, A fuzzy ART versus hybrid NN-HMM methods for lithology identification in the triasic province, 2nd Information and Communication Technologies (ICTTA '06), Vol. 1 (2006), pp. 1884-1887.
36. S. Chikhi and M. Batouche, Probabilistic neural method combined with radial-bias functions applied to reservoir characterization in the Algerian triassic province, J. Geophys. Eng. 1 (2004) 134-142.
37. X. Deyi, W. Dave, Y. Tina and R. San, Permeability estimation using a hybrid genetic programming and fuzzy/neural inference approach, Society of Petroleum Engineers Annual Technical Conf. Exhibition, Dallas, Texas, USA (2005).
38. S. Abe, Fuzzy LP-SVMs for multiclass problems, Proc. Euro. Symp. Artificial Neural Networks, Belgium (2004), pp. 429-434.
39. Y. Wang, Fuzzy clustering analysis by using genetic algorithm, Innovat. Comput. Infor. Cont. Express Lett. 2(4) (2008) 331-337.
40. O. Castillo and P. Melin, Intelligent systems with interval type-2 fuzzy logic, Int. J. Innovat. Comput. Infor. Cont. 4(4) (2008) 771-784.
41. Y. Xing, X. Wu and Z. Xu, Multiclass least squares auto-correlation wavelet support vector machines, Int. J. Innovat. Comput. Infor. Cont. Express Lett. 2(4) (2008) 345-350.
42. J. Habibi, B. Moshiri and A. K. Sedigh, Contractive predictive control of mixed logical dynamical hybrid systems, Int. J. Innovat. Comput. Infor. Cont. 4(6) (2008) 1283-1298.
43. E. Castillo, J. Gutíerrez, A. Cobo and C. Castillo, A minimax method for learning functional networks, Neural Process. Lett. 11 (2000) 39-49.
44. A. Iglesias and A. Ǵalvez, Applying functional networks to fit data points from B-spline surfaces, Computer Graphics Int. (CGI'01 ) (2001), pp. 329-332.
45. W. Dongrui and W. T. Woei, Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers, J. Eng. Appl. Artif. Intell. 19 (2006) 829-841.
46. W. Dongrui and J. M. Mendel, A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets, Infor. Sci. 178 (2008) 381-402.
47. R. I. John and C. Czarnecki, An adaptive type-2 fuzzy system for learning linguistic membership grades, IEEE Int. Fuzzy Syst. Conf. Proc. (1999), pp. 1552-1556.
48. Y. Li, X. Sun, J. Hua and C. Gong, Modeling redundant structure in ecosystem by type-2 fuzzy logic system, Ecological Modeling (2007).
49. T. Helmy, F. Anifowose and K. Faisal, Hybrid computational models for the characterization of oil and gas reservoirs, Int. J. Expert Syst. Appl. 37 (2010) 5353-5363.