Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization

Geophysical Prospecting

Volumn 61, Issue 3, 2013, Pages 582-598

  Ali Ahmadi, Mohammad ^a   Zendehboudi, Sohrab ^b   Lohi, Ali ^c   Elkamel, Ali ^b   Chatzis, Ioannis ^b  

^a PETROLEUM UNIVERSITY OF TECHNOLOGY   (Iran)

^b UNIVERSITY OF WATERLOO   (Canada)

^c RYERSON UNIVERSITY   (Canada)

Author keywords

Hybrid genetic algorithm; Neural network; Particle swarm optimization; Permeability; Well log data

Indexed keywords

FEED-FORWARD ARTIFICIAL NEURAL NETWORKS; FUNDAMENTAL PROPERTIES; HYBRID GENETIC ALGORITHMS; PETROPHYSICAL PROPERTIES; PREDICTION OF RESERVOIR; RESERVOIR CHARACTERIZATION; RESERVOIR PERMEABILITY; WELL LOG DATA;

ENHANCED RECOVERY; FORECASTING; GENETIC ALGORITHMS; MECHANICAL PERMEABILITY; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); RESERVOIR MANAGEMENT; SENSORS;

PETROLEUM RESERVOIR ENGINEERING;

ARTIFICIAL NEURAL NETWORK; ENHANCED OIL RECOVERY; GENETIC ALGORITHM; OPTIMIZATION; PERMEABILITY; RESERVOIR CHARACTERIZATION; SPATIAL DISTRIBUTION; SPATIAL VARIATION; WELL LOGGING;

EID: 84876168452     PISSN: 00168025     EISSN: 13652478     Source Type: Journal    
DOI: 10.1111/j.1365-2478.2012.01080.x     Document Type: Article

References (49)

1. st edn. Eage Publications, p.161.
2. van der Baan M. and Jutten C. 2000. Neural networks in geophysical applications. Geophysics 65, 1032-1047.
3. Balan B., Mohaghegh S. and Ameri S. 1995. State of the art in permeability determination from well log data: Part 1 A Comparative study, model development. SPE 30978, SPE Eastern Regional Conference and Exhibition, Morgantown, West Virginia, 17-21 September 1995.
4. Bogdanov I.I., Mourzenko V.V. and Thovert J.F. 2007. Effective permeability of fractured porous media with power-law distribution of fracture sizes. Physics Review E 76(3), p.15.
5. Boschetti F. and Moresi L. 2001. Interactive inversion in geosciences. Geophysics 66(4), 1226-1234.
6. nd edn. Boston : McGraw-Hill.
7. nd edn. Prentice-Hall, Englewood Cliffs, NJ .
8. Byrnes A.P. 1994. Measurements of independent and dependent variables for petrophysical properties prediction. Reservoir Quality Assessment and Prediction in Clastic Rocks . M.D., Wilson edition, SEPM short course #30, 231-311.
9. rd edn. Butterworth's Scientific Publications, London, p.128.
10. Coker D.A., Torquato S. and Dunsmuir J.H. 1966. Morphology and physical properties of Fontainebleau sandstone via a topographic analysis. Journal of Geophysical Research 101(B8), 17497-17506.
11. Coulibaly P. and Baldwin C.K. 2005. Nonstationary hydrological time series forecasting using nonlinear dynamic methods. Journal of Hydrology 174-307.
12. Doveton J.H. and Prensky S.E. 1992. Geological applications of wireline logs: A Synopsis of developments and trends. The Log Analyst 33(3), 286-303.
13. nd edn. Academic, San Diego, California, p.574.
14. Eberhart R.C. and Kennedy J. 1995. A new optimizer using particle swarm theory. Proceedings of the 6th Symposium on Micro Machine and Human Science, 39-43.
15. st edn. Academic Press Professional, Boston, p.464.
16. García-Pedrajas N., Hervás-Martínez C. and Muñoz-Perez J. 2003. A cooperative coevolutionary model for evolving artificial neural networks. IEEE Transactions on Neural Networks 14 (3), 575-596.
17. van Golf-Racht T.D. 1982. Fundamentals of Fractured Reservoir Engineering, 1st edn. Elsevier publisher, p.710.
18. ed edn. PWS Publishing, Boston, p.736.
19. Hassan R., Cohanim B. and Weck O. 2005. A comparison of particle swarm optimization and genetic algorithm. Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, Austin, Texas, 18-21.
20. ed edn. MIT Press, Cambridge, MA, p. 183.
21. Hornick K., Stinchcombe M. and White H. 1989. Multilayer feed forward networks are universal approximators. Neural networks 2(5), 359-366.
22. 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, 422-436.
23. Huang Z. and Williamson M.A. 1997. Determination of porosity and permeability in reservoir intervals by artificial neural network modelling, offshore eastern Canada. Petroleum Geoscience 3, 245-258.
24. Jennings J.B. 1987. Capillary pressure techniques: Application to exploration and development geology. AAPG Bulletin 71, 1196-1209.
25. Juang C.F. 2004. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 997-1006.
26. Kozeny J. 1927. Ober kapillare Leitung des Wassers in Boden: Sitzungsbericht der Wiener Akademie. Abteilung Πa 136, p. 271.
27. nd Annual Technical Conference of SPE, Denver, Colorado, 9-12 October 1977.
28. Pape H., Clauser C. and Iffland J. 1999. Permeability prediction based on fractal pore space geometry. Geophysics 64, 1447-1460.
29. Parker P.B. 1999. Genetic Algorithms and Their Use in Geophysical Problems . Ph.D. Thesis, Ernest Orlando, Lawrence Berkeley National Laboratory, Earth Sciences Division, April 1999.
30. Poulton M.M. 2002. Neural networks as an intelligence amplification tool: A review of applications. Geophysics 67, 979-993.
31. Pryor W.A. 1973. Permeability-porosity patterns and variations in some Holocene sand bodies. American Association of Petroleum Geologists Bulletin 57 (1), 162-191.
32. Qu1 X., Feng J. and Sun W. 2008. Parallel genetic algorithm model based on AHP and neural networks for Enterprise Comprehensive Business. IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 897-900.
33. Rallo R., Ferre-Gin J., Arenas A. and Giralt F. 2002. Neural virtual sensor for the inferential prediction of product quality from process variables. Computers and Chemical Engineering 26, 1735-1754.
34. Rechenberg I. 1994. Evolution strategy, Computational Intelligence-Imitating Life . Edited by J.M. Zurada, R.J. Marks and C.J. Robinson . IEEE Press, Piscataway, NJ, 147-159.
35. Reed R. 1993. Pruning algorithms-A Survey. IEEE Trans. Neural Networks 4, 740-747.
36. Ripley B.D. 1996. Pattern Recognition and Neural Networks . Cambridge : Cambridge University Press, p.415.
37. Rosenblatt F. 1958. The perceptron: A probabilistic model for information storage and organisation in the brain. Psychological review 65, 386-408.
38. st edn. Total Edition Press. Paris, p.458.
39. Sambridge M. and Mosegaard K. 2002. Monte Carlo methods in geophysical inverse problems. Reviews of Geophysics 40(3), 1-29.
40. nd edn. John Wiley & Sons Inc., New York, p.456.
41. nd edn. Elsevier Science Publications, Netherlands, p.281.
42. Sharma S.K., Nohwar U.V. S., Raj H., Koithara J. and Chilingar G.V. 1980. Interrelationships among various petrophysical parameters of carbonate reservoir rocks in the Bombay High Oil Field, India. Energy Sources 5(1), 53-69.
43. Shi Y. and Eberhart R.C. 1988. A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation, 69-73.
44. Soupios P., Akca I., Mpogiatzis P., Basokur A.T. and Papazachos C. 2011. Applications of hybrid genetic algorithms in seismic tomography. Journal of Applied Geophysics 75, 479-489.
45. De Souto M.C.P., Yamazaki A. and Ludernir T.B. 2002. Optimization of neural network weights and architecture for odor recognition using simulated annealing. Proceedings of the 2002 International Joint Conference on Neural Networks 1, 547-552.
46. Tang P. and Xi Z. 2008. The research on BP neural network model based on guaranteed convergence particle swarm optimization. Second International Symposium on Intelligent Information Technology Application 2, 13-16.
47. Thomas D.C. and Pugh V.J. 1987. A statistical analysis of accuracy and reproducibility of standard core analysis. Proceedings of Society of Core Analysis, Paper no. 8701.
48. Tronicke J., Paasche H. and Böniger U. 2012. Crosshole traveltime tomography using particle swarm optimization: A near-surface field example. Geophysics 77(1), February 2012.
49. Wu T. 2004. Permeability Prediction and Drainage Capillary Pressure Simulation in Sandstone Reservoirs . Ph.D. Dissertation, Department of Geology and Geophysics, The Texas A&M University, College Station, TX, Dec 2004, p.184.