Support-vector regression for permeability prediction in a heterogeneous reservoir: A comparative study

SPE Reservoir Evaluation and Engineering

Volumn 13, Issue 3, 2010, Pages 485-495

  Al Anazi, A ^a   Gates, I D ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; FORECASTING; MULTILAYER NEURAL NETWORKS; PETROLEUM RESERVOIR ENGINEERING; PETROPHYSICS; PREDICTIVE ANALYTICS; PROFESSIONAL ASPECTS; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES; VECTORS; WELL LOGGING;

EMPIRICAL RISK MINIMIZATION; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; RADIAL BASIS FUNCTION NEURAL NETWORKS; STATISTICAL ERROR ANALYSIS; STATISTICAL LEARNING THEORY; STRUCTURAL RISK MINIMIZATION PRINCIPLE; SUPPORT VECTOR MACHINE (SVMS); SUPPORT VECTOR REGRESSION (SVR);

SUPPORT VECTOR REGRESSION;

COMPARATIVE STUDY; CORRELATION; HETEROGENEITY; PERMEABILITY; REGRESSION ANALYSIS; RESERVOIR; STATISTICAL APPLICATION;

EID: 77952424965     PISSN: 10946470     EISSN: None     Source Type: Journal    
DOI: 10.2118/126339-PA     Document Type: Article

References (40)

1. Al-Anazi, A., Gates, I. D., and Azaiez, J. 2009. Innovative Data-Driven Permeability Prediction in a Heterogeneous Reservoir. Paper SPE 121159 presented at the EUROPEC/EAGE Conference and Exhibition, Amsterdam, 8-11 June, doi: 10.2118/121159-MS.
2. Basak, D., Pal, S., and Patranabis, D. C. 2007. Support Vector Regression. Neural Information Processing-Letters and Reviews 10(10):203-224.
3. Boser, B. E., Guyon, I. M., and Vapnik, V. N. 1992. A training algorithm for optimal margin classifiers. 3tIn Proceedings of the fifth annual workshop on Computational learning theory, ed. D. Haussler, 144-152. New York: ACM Press.
4. Burges, C. J. C. 1996. Simplified support vector decision rules. In Proceedings of the International Conference on Machine Learning, ed. L. Saitta, L., 71-77. San Mateo, CA: Morgan Kaufmann Publishers.
5. Burges, C. J. C. and Scholkopf, B. 1997. Improving the accuracy and speed of support vector learning machines. In Advances in Neural Information Processing Systems 9, ed. M. C. Mozer, M. I. Jordan, and T. Petsche, 375-381. Cambridge, Massachusetts: MIT Press.
6. Chen, S., Cowan, C. F. N., and Grant, P. M. 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2):302-309.
7. Cortes, C. and Vapnik, V. 1995. Support-Vector Networks. Machine Learning 20:273-297.
8. Cristianini, C. and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge, UK: Cambridge University Press.
9. Cuddy, S. J. 2000. Litho-Facies and Permeability Prediction From Electrical Logs Using Fuzzy Logic. SPE Res Eval & Eng 3(4):319-324. SPE-65411-PA. doi: 10.2118/65411-PA.
10. Fletcher, R. 1987. Practical Methods of Optimization. New York: John Wiley & Sons.
11. Guyon, I., Boser, B., and Vapnik, V. 1993. Automatic capacity tuning of very large VC-dimension classifiers. In Advances in Neural Information Processing Systems 5, ed. S. J. Hanson, J. D. Cowan, and C. L. Giles, 147-155. San Mateo, California: Morgan Kaufmann Publishers.
12. Haykin, S. 1999. Neural Networks, a Comprehensive Foundation, second edition. Upper Saddle River, New Jersey: Prentice-Hall Inc.
13. Huang, Y, Gedeon, T. D., and Wong, P. M. 2001. An integrated neuralfuzzy-genetic-algorithm using hyper-surface membership functions to predict permeability in petroleum reservoirs. Engineering Applications of Artificial Intelligence 14(1):15-21. doi: 10.1016/S0952-1976 (00) 00048-8.
14. Huang, Z., Shimeld, J., Williamson, M., and Katsube, J. 1996. Permeability prediction with artificial neural network modelling in the Venture gas field, offshore eastern Canada. Geophysics 61(2):422-436. doi: 10.1190/1.1443970.
15. Huber, P. J. 1981. Robust Statistics. New York: Wiley Series in Probability and Statistics, Wiley-Interscience.
16. Karush, W. 1939. Minima of functions of several variables with inequalities as side constraints. MSc thesis, Department of Mathematics, University of Chicago, Chicago, Illinois.
17. Kecman, V. 2005. Support Vector Machines-An Introduction. In Support Vector Machines: Theory and Applications, ed. L. Wang, Chap. 1, 1-47. Heidelberg, Germany: Studies in Fuzziness and Soft Computing, Springer-Verlag.
18. Kuhn, H. W. and Tucker, A. W. 1951. Nonlinear programming. In Proceedings of 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics, 481-492. Berkeley, California: University of California Press.
19. Morozov, V. A., Nashed, Z., and Aries, A. B. 1984. Methods for Solving Incorrectly Posed Problems. New York: Springer.
20. Müller, K.-R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., and Vapnik, V. 1997. Predicting time series with support vector machines. In Artificial Neural Networks-ICANN'97, Lausanne, Switzerland, October 8-10, 1997, ed. W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, No. 1327, 999-1004. Berlin, Germany: Lecture Notes in Computer Science, Springer.
21. Peng, K.-L., Wu, C.-H., and Goo, Y.-J. 2004. The Development of a New Statistical Technique for Relating Financial Information to Stock Market Returns. International Journal of Management 21(4):492-505.
22. Rogers, S. J., Chen, H. C., Kopaska-Merkel, D. C., and Fang, J. H. 1995. Predicting permeability from porosity using artificial neural networks. AAPG Bulletin 79(12):1786-1797.
23. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. 1986. Learning internal representations by error propagation. In Parallel Distributed Processing, Vol. I: Foundations, ed. D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, 318-362. Cambridge, Massachusetts: MIT Press.
24. Schölkopf, B. and Smola, A. 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, Massachusetts: Adaptive Computation and Machine Learning, MIT Press.
25. Scholkopf, B., Burges, C., and Vapnik, V. 1995. Extracting support data for a given task. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, ed. U. M. Fayyad and R. Uthurusamy, 252-257. Menlo Park, California: AAAI Press.
26. Scholköpf, B., Burges, C., and Vapnik, V. 1996. Incorporating Invariances in Support Vector Learning Machines. In Artificial Neural Networks: 6th International Conference Proceedings/ICANN'96, Bochum, Germany, July 16-19, 1996, ed. C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, and B. Sendhoff, No. 1112, 47-52. Berlin, Germany: Lecture Notes in Computer Science, Springer.
27. Schölkopf, B., Mika, S., Burges, C., Knirsch, P., Müller K.-R., Rätsch G., and Smola, A. 1999. Input Space vs. Feature Space in Kernel-Based Methods. IEEE Transactions on Neural Networks 10(5):1000-1017.
28. Schölkopf, B., Smola, A., Williamson, R. C., and Bartlett, P. L. 2000. New Support Vector Algorithms. Neural Computation 12(2):1207-1245.
29. Sherrod, P. H. 2009. DTREG Predictive Modeling Software (version 9.1 user's manual), http://www.dtreg.com.
30. Smola, A., Schölkopf, B., and Müller, K.-R. 1998. The connection between regularization operators and support vector kernels. Neural Networks 11(4):637-649. doi: 10.1016/S0893-6080 (98) 00032-X.
31. Specht, D. 1991. A general regression neural network. IEEE Transactions on Neural Networks 2(6):568-576. doi: 10.1109/72.97934. (Pubitemid 23563887)
32. Steinwart, I. and Christmann, A. 2008. Support Vector Machines. New York: Information Science and Statistics, Springer Science+Business Media.
33. Taghavi, A. A. 2005. Improved Permeability Estimation through Use of Fuzzy Logic in a Carbonate Reservoir from Southwest, Iran. Paper SPE 93269 presented at the SPE Middle East Oil and Gas Show and Conference, Bahrain, 12-15 March, doi: 10.2118/93269-MS.
34. Tikhonov, A. N. and Arsenin, V. Y. 1977. Solution of III Posed Problems. New York: V. H. Winston and Sons.
35. Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. New York: Springer-Verlag.
36. Vapnik, V. and Chervonenkis, A. 1964. A note on one class of perceptrons. Automation and Remote Control 25 (1).
37. Vapnik, V. and Chervonenkis, A. 1974. Theory of Pattern Recognition (in Russian). Nauka, Moscow. (German translation: Wapnik, W. and Tscherwonenkis, A. 1979. Theorie der Zeichenerkennung. Berlin, Germany: Akademie-Verlag).
38. Vapnik, V. and Lerner, A. 1963. Pattern recognition using generalized portrait method. Automation and Remote Control 24:774-780.
39. Vapnik, V., Golowich, S., and Smola, A. 1997. Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems 9, ed. M. C. Mozer, M. I. Jordan, and T. Petsche, 281-287. Cambridge, Massachusetts: MIT Press.
40. Vapnik, V. N. 1982. Estimation of Dependences Based on Empirical Data. Berlin, Germany: Springer Series in Statistics, Springer.