A new approach combining Karhunen-Loéve decomposition and artificial neural network for estimating tight gas sand permeability

Journal of Petroleum Science and Engineering

Volumn 18, Issue 1-2, 1997, Pages 101-112

  Smaoui, Nejib ^a   Garrouch, Ali A ^a  

^a KUWAIT UNIVERSITY   (Kuwait)

Author keywords

Artificial neural network; Karhunen Lo ve decomposition; Permeability; Tight gas sand

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; MECHANICAL PERMEABILITY; MINERALOGY; NATURAL SCIENCES COMPUTING; NEURAL NETWORKS; PARAMETER ESTIMATION; POROSITY;

KARHUNEN LOEVE DECOMPOSITION; TIGHT GAS SANDS;

NATURAL GAS DEPOSITS;

NEURAL NETWORK; PERMEABILITY; SAND;

GAS SAND; KARHUNEN-LOEVE DECOMPOSITION; NEURAL NETWORK; PERMEABILITY;

EID: 0031179927     PISSN: 09204105     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0920-4105(96)00077-0     Document Type: Article

References (20)

1. Armbruster, D., Nicolaenko, B., Smaoui, N. and Chossat, P., 1993. Analyzing bifurcations in the Kolmogorov flow equations. In: P. Chossat (Editor), Dynamics Bifurcations and Symmetries. NATO ASI, Cargèse, Kluwer, Dordrecht.
2. Armbruster, D., Nicolaenko, B., Smaoui, N. and Chossat, P., 1996. Symmetries and dynamics for the 2-D Navier-Stokes flow. Physica D, 95: 81-93.
3. Antsaklis, P., 1990. Neural networks in control systems. IEEE Contr. Syst. Mag., 10(3): 3-5.
4. Bourlard, H. and Wellekens, C.J., 1989. Speech dynamics and recurrent neural networks. Proc. ICASSP, pp. 33-36.
5. Brooks, C.L., Karplus, M. and Pettitt, B.M., 1988. Proteins: A Theoretical Perspective of Dynamics, Structure and Thermodynamics. Wiley, New York, NY.
6. Ephrain, Y. and Rabiner, L., 1990. On the relations between modelling approaches for speech recognition. IEEE Trans. Acoust. Speech. Signal Process. (Mar.), 36: 372-379.
7. Garrouch, A.A. and Smaoui, N., 1996. Application of artificial neural network for estimating tight gas sand permeability. Energy Fuels, 10(5): 1053-1059.
8. Gonzalez, R.C. and Wintz, P., 1987. Digital Image Processing. Addison-Wesley, Reading, MA, pp. 122-130.
9. Harman, H., 1960. Modern Factor Analysis. University of Chicago Press, Chicago, IL.
10. Herron, M.M., 1987. SPWLA Twenty-Eighth Annual Logging Symposium, June 29-July 2, Paper HH.
11. Hopfield, J.J. and Tank, D.W., 1985. Neural computation of decisions optimization problems. Biol. Cybern., Vol. 52, 141 pp.
12. Loéve, M.M., 1955. Probability Theory. Van Nostrand, Princeton, NJ.
13. Lorenz, E.N., 1963. Deterministic nonperiodic flow. J. Atmos. Sci., 20: 130-141.
14. Lumley, J.L., 1967. The structure of inhomogeneous turbulent flows. In: A.M. Yaglom and V.I. Tatarski (Editors), Atmospheric Turbulence and Radio Wave Propagation. Nauka, Moscow, pp. 166-178.
15. Ouenes, A., Doddi, R., Lin, Y. and Cunningham, G., 1994. A new approach combining neural networks and simulated annealing for solving petroleum inverse problems. Eur. Conf. on Mathematics of Oil Recovery. Roros, Norway, June 7-10.
16. Pao, J.H., 1989. Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading, MA.
17. Reid, M.B., Spirkovska, L. and Ochoa, E., 1989. Rapid training of higher-order neural networks for invariant pattern recognition. Proc. Joint Int. Conf. on Neural Networks, Washington, DC, pp. 689-692.
18. Sirovich, L., 1987. Turbulence and the dynamics of coherent structures. Parts I-III: Dynamics and scaling. Q. Appl. Math., 45: 538-590.
19. Smaoui, N., 1995. Karhunen-Loéve analysis on the 2-D Navier-Stokes flow. Bull. Am. Phys. Soc., 40(12).
20. Strang, G., 1976. Linear Algebra and its Applications. Academic Press, New York, NY.