An Improved Technique in Porosity Prediction: A Neural Network Approach

IEEE Transactions on Geoscience and Remote Sensing

Volumn 33, Issue 4, 1995, Pages 971-980

  Wong, Patrick M ^a   Gedeon, Tamás D ^b   Taggart, Ian J ^c  

^a Enretech Australasia PTY Ltd   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c West Australian Petroleum Pty Ltd   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; FLOW OF FLUIDS; HYDROCARBONS; PETROLEUM RESERVOIRS; POROSITY; ROCKS;

BACKPROPAGATION NEURAL NETWORK METHODS; COARSE GRAIN STRUCTURE; FACIES-BY-FACIES DESCRIPTION; RESERVOIR CHARACTERIZATION; SANDSTONE RESERVOIR; WEIGHT VISUALIZATION CURVES;

NEURAL NETWORKS;

EID: 0029339808     PISSN: 01962892     EISSN: 15580644     Source Type: Journal    
DOI: 10.1109/36.406683     Document Type: Article

References (30)

1. J. L. Baldwin, A. R. M. Bateman, and C. L. Wheatley, “Application of neural network to the problem of mineral identification from well logs,” Log Analyst, vol. 3, pp. 279–293, 1990.
2. J. L. Baldwin and D. N. Otte, “Computer emulation of human mental processes: Application of neural network simulators to problems in well log interpretation,” presented at the 64th Ann. Tech. Conf. Exhibition Soc. Petroleum Eng., 1989.
3. H. Bischof, W. Schneider, and A. J. Pinz, “Multispectral classification of landsat-images using neural networks,” IEEE Trans. Geosci. Remote Sensing, vol. 30, pp. 482–490, 1992.
4. M. Caudill, “Neural networks primer: Part III,” AI Expert, vol. 3, pp. 53–59, 1988.
5. D. Z. Cheng, X. L. Wu, and J. A. Cheng, “Determining reservoir properties in reservoir studies using a fuzzy neural network,” presented at the 68th Ann. Tech. Conf. Exhibition Soc. Petroleum Eng., 1993.
6. J. E. Dayhoff, Neural Network Architectures: An Introduction. New York: Van Nostrand, 1990.
7. P. C. Delfiner, O. Peyret, and O. Serra, “Automatic determination of lithology from well logs,” presented at the 59th Ann. Tech. Conf. Exhibition Soc. Petroleum Eng., 1984.
8. H. Derek, R. Johns, and E. Pasternack, “Comparative study of backpropagation neural network and statistical pattern recognition techniques in identifying sandstone lithofacies,” Conf. Artificial Intellig. Petroleum Exploration Production, 1990, pp. 41–49.
9. R. C. Eberhart and R. W. Dobbins, Neural Network PC Tools: A Practical Guide. San Diego, CA: Academic, 1990.
10. T. D. Gedeon and T. G. Bowden, “Heuristic pattern reduction,” Int. Joint Conf. Neural Networks, 1992, vol. 2, pp. 449–453.
11. T. D. Gedeon and D. Harris, “Hidden units in a plateau,” Proc. 1st Int. Conf. Intelligent Syst., 1992, pp. 391–395.
12. M. Gori and A. Tesi, “On the problem of local minima in backpropagation,” IEEE Trans. Pattern Analysis Mach. Intellig., vol. 14, pp. 76–86, 1992.
13. H. H. Haldorsen and E. Damsleth, “Challenges in reservoir characterization,” AAPG Bulletin, vol. 77, pp. 541–551, 1993.
14. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359–366, 1989.
15. C. L. Hearn, W. J. Ebanks, R. S. Tye, and V. Ranganathan, “Geological factors influencing reservoir performance of the Hartzog draw field, Wyoming,” J. Petroleum Technol., vol. 36, pp. 1335–1344, 1984.
16. B. Irie and S. Miyake, “Capabilities of three-layered perceptrons,” Int. Joint Conf. Neural Networks, 1988, vol. 3, pp. 1641–1648.
17. F. X. Jian, C. Y. Chork, I. J. Taggart, D. M. Mckay, and R. M. Barlett, “A genetic approach to the prediction of petrophysical properties,” J. Petroleum Geology, vol. 17, pp. 71–88, 1994.
18. R. P. Lippmann, “Pattern classification using neural networks,” IEEE Commun. Mag., vol. 27, pp. 47–64, 1989.
19. D. A. Osborne, “Permeability estimation using a neural network: A case study from the Roberts unit, Wasson field, Yoakum Country, Texas,” Conf. Artificial Intellig. Petroleum Exploration Production, 1992, pp. 89–96.
20. S. J. Rogers, J. H. Fang, C. L. Karr, and D. A. Stanley, “Determination of lithology from well logs using a neural network,” AAPG Bulletin, vol. 76, pp. 731–739, 1992.
21. D. E. Rumelhart, G. E. Hinton and R. H. Williams, “Learning representations by backpropagating errors,” Nature, vol. 323, pp. 533–536, 1986.
22. S. Sakurai and J. Melvin, “Facies discrimination and permeability estimation from well logs for the endicott fields,” Society of Professional Well Log Analyst 29th Ann. Logging Symp., Paper FF, 1988.
23. O. Serra and H. T. Abbott, “The contribution of logging data to sedimentology and stratigraphy,” presented at the 55th Ann. Tech. Conf. Exhibition Soc. Petroleum Eng., 1980.
24. P. Slade and T. D. Gedeon, “Bimodal distribution removal,” in J. Mira, J. Cabestany and A. Prieto, Ed., New Trends in Neural Computation, vol. 686. Berlin: Springer Verlag, 1993, pp. 249–254.
25. J. H. Stiles, Jr. and J. M. Huthilz, “The use of routine and special core analysis in characterizing brent group reservoirs, U. K. North Sea,” J. Petroleum Technol., vol. 44, pp. 704–713, 1992.
26. W. A. Wendt, S. Sakurai, and P. H. Nelson, “Permeability prediction from well logs using multiple regression,” in L. W. Lake and H. B. Carroll Eds., Reservoir Characterization. New York: Academic, 1986, pp. 181–222.
27. B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: Perceptron, madaline and backpropagation,” Proc. IEEE, vol. 78, pp. 1415–1442, 1990.
28. M. Wolff and J. Pelissier-Combescure, “FACIOLOG: Automatic electrofacies determination,” Soc. Professional Well Log Analysts 23rd Ann. Logging Symp., Paper FF, 1982.
29. X. H. Yu, “Can backpropagation error surface not have local minima,” IEEE Trans. Neural Networks, vol. 3, pp. 1019–1021, 1992.
30. Y. X. Zhang and D. X. Wang, “Fast learning in a backpropagation algorithm with a sine-type thresholding function,” Appl. Opt., vol. 31, pp. 2414–2416, 1992.