Committee neural network model for rock permeability prediction

Journal of Applied Geophysics

Volumn 104, Issue , 2014, Pages 142-148

  Bagheripour, Parisa ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Committee neural network (CNN); Generalized regression neural network (GRNN); Genetic algorithm (GA); Multi layer perceptron (MLP); Radial basis function (RBF); Rock permeability

Indexed keywords

FORECASTING; GAS INDUSTRY; GENETIC ALGORITHMS; INTELLIGENT SYSTEMS; NEURAL NETWORKS; PETROLEUM RESERVOIRS; PRINCIPAL COMPONENT ANALYSIS; RADIAL BASIS FUNCTION NETWORKS; ROCKS; WELL LOGGING;

COMMITTEE NEURAL NETWORK (CNN); GENERALIZED REGRESSION NEURAL NETWORK(GRNN); MULTI LAYER PERCEPTRON; RADIAL BASIS FUNCTION(RBF); ROCK PERMEABILITY;

PETROLEUM RESERVOIR ENGINEERING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CARBONATE; ESTIMATION METHOD; HYDROCARBON RESERVOIR; PERMEABILITY; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; QUANTITATIVE ANALYSIS; RELIABILITY ANALYSIS; WELL LOGGING;

ARABIAN SEA; INDIAN OCEAN; PERSIAN GULF; SOUTH PARS GAS FIELD;

EID: 84896523766     PISSN: 09269851     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jappgeo.2014.03.001     Document Type: Article

References (49)

1. Ahmed T. Reservoir Engineering Handbook 2006, Gulf Professional Publishing, Elsevier Inc.
2. Ahmed U., Crary S., Coates G. Permeability estimation: the various sources and their interrelationship. J. Pet. Technol. 1991, 43:578-587.
3. Aminian K., Ameri S., Oyerokun A., Thomas B. Prediction of flow units and permeability using artificial neural network. SPE western regional meeting (California, U.S.A., 19-24 may), SPE 83586 2003.
4. Archie G.E. The electrical resistivity log as an aid in determining some reservoir characteristics. Trans. AIME 1942, 146:54-62.
5. Artun E., Mohaghegh S.D., Toro J., Wilson T., Sanchez A. Reservoir characterization: using intelligent seismic inversion. SPE Eastern Regional Meeting (Morgantown, West Virginia, 14-16 September), SPE 98012 2005.
6. Asoodeh M. Intelligent isochronal test analysis. J. Pet. Sci. Technol. 2013, 31(9). 10.1080/10916466.2010.545783.
7. Asoodeh M. Prediction of Poisson's ratio from conventional well log data: a committee machine with intelligent systems approach. Energy Sources Part A 2013, 35(10). 10.1080/15567036.2011.557693.
8. Asoodeh M., Bagheripour P. Estimation of bubble point pressure from PVT data using a power-law committee with intelligent systems. J. Pet. Sci. Eng. 2012, 90(91):1-11.
9. Asoodeh M., Bagheripour P. Core porosity estimation through different training approaches for neural network: back-propagation learning vs. genetic algorithm. Int. J. Comput. Appl. 2013, 63(5):11-15. (0975 - 8887).
10. Asoodeh M., Kazemi K. Estimation of bubble point pressure: using a genetic integration of empirical formulas. Energy Sources Part A 2013, 35(12). 10.1080/15567036.2011.574195.
11. Atkinson G.D., Bloch S., Scheihing M.H. Sedimentology and depositional environments of the Oligo-Miocene gas-bearing succession in the Yacheng 13-1 field, South China Sea. People's Republic of China. AAPG Bull. 1990, 74:601.
12. Beard D.C., Weyl P.K. Influence of texture on porosity and permeability of unconsolidated sand. AAPG Bull. 1973, 57:349-369.
13. Berg R. Method of determining permeability from reservoir rock properties. Gulf Coast Assoc. Geol. Soc. Trans. 1970, 20:303-317.
14. Bishop C.M. Neural Networks for Pattern Recognition 1994, 670. Clarendon Press, Oxford.
15. Bloch S. Empirical prediction of porosity and permeability in sandstones. AAPG Bull. 1991, 75:1145-1160.
16. Carman P. Fluid flow through granular beds. Inst. Chem. Eng. Trans. 1937, 15:150-166.
17. Chilinger G. Relationship between porosity, permeability and grain size distribution of sands and sandstones. Deltaic and Shallow Marine Deposits 1964, 71-75. Elsevier, New York. L. van Straaten (Ed.).
18. Cuddy S.J. Litho-facies and permeability prediction from electrical logs using fuzzy logic. Eighth Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, UAE, Society of Petroleum Engineers (SPE) 65411 (49470) 1998, 1-12.
19. Doyen P. Permeability, conductivity and pore geometry of sandstone. J. Geophys. Res. 1988, 93:7729-7740.
20. Ehrlich R., Crabtree S.J., Horkowitz K.O., Horkowitz J.P. Petrography and reservoir physics I: Objective classification of reservoir porosity. AAPG Bull. 1991, 75:1562-1574.
21. Gassmann F. Über Die Elastizität poröser median (Elasticity of porous media). Veir. der Natur. Ges. Zürich 1951, 96:1-23.
22. Hagan M.T., Menhaj M. Training feed-forward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 1999, 5(6):989-993.
23. Haykin S. Neural Networks: A Comprehensive Foundation 1991, 842. Prentice-Hall, Englewood Cliffs, NJ.
24. Holland J.H. Adaptation in Natural And Artificial Systems 1975, 183. University of Michigan Press, Ann Arbor, USA.
25. Huang Z., Shimeld J., Williamson M., Katsube J. Permeability prediction with artificial neural network modeling in the Venture gas field offshore eastern. Can. Geophys. 1996, 61:422-436.
26. Hurst A., Rosvoll K.J. Permeability variations in sandstones and their relationship to sedimentary structures. Reservoir Characterization II 1991, 166-196. L.W. Lake, H.B. Carroll, T.C. Wesson (Eds.).
27. Jamialahmadi M., Javadpour F.G. Relationship of permeability, porosity and depth using an artificial neural network. J. Pet. Sci. Eng. 2000, 26:235-239.
28. Johnson D., Koplik J., Dashen R. Theory of dynamic permeability and tortuosity in fluid-saturated porous media. J. Fluid Mech. 1987, 176:379-402.
29. Kadkhodaei-Illkchi A., Rezaee M.R., Moallemi S.A. A new approach for prediction of porosity, permeability and rock types using soft computing techniques. An Example from Southern Persian Gulf, Petroleum Geoscience Collaboration Conference, London 30 November-1 December 375 2005.
30. Kadkhodaei-Illkchi A., Rezaee M.R., Rahimpour-Bonab H., Chehrazi A. Petrophysical data prediction from seismic attributes using committee fuzzy inference systems. J. Comput. Geosci. 2009, 35:2314-3330.
31. Lim J. Reservoir properties determination using fuzzy logic and neural network from well log data in offshore Korea. J. Pet. Sci. Eng. 2005, 49:182-192.
32. Marion D., Nur A., Albert F. Modeling the relationship between sonic velocity, porosity, permeability, and shaliness in sand, shale and shaly sand. SPWLA Thirtieth Annual Logging Symposium, Proceeding paper G 1989, 1-22.
33. Mc Gowen J.H., Bloch S. Depositional facies, diagenesis and reservoir quality of Ivishak Sandstone, Prudhoe Bay field (abs). AAPG Bull. 1985, 69:276.
34. Mohaghegh S. Virtual-intelligence applications in petroleum engineering: part 2-evolutionary computing. J. Pet. Technol. 2000, 52:40-46.
35. Mohaghegh S., Balan B., Ameri S. State-of-the-art in permeability determination from well log data: part 2-verifiable, accurate permeability predictions, the touch-stone of all models. SPE Eastern Regional Conference and Exhibition Sept 17-21, West Virginia, SPE 30979 1995.
36. Mohaghegh S.D., Goddard C., Popa A., Ameri S., Bhuiyan M. Reservoir characterization through synthetic logs. SPE Eastern Regional Meeting (Morgantown, West Virginia, 17-19 October), SPE 65675 2000.
37. Pittman E.D., Larese R.E. Experimental compaction of lithic sands. SEPM Annual Midyear Meeting 1987, 466.
38. Rajabi M., Bohloli B., Ahangar E. Intelligent approach for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: a case study from the Sarvak carbonate reservoir in the Abadan plain (Southern Iran). J. Comput. Geosci. 2010, 36:647-664.
39. Rezaee M.R., Griffiths C.M. Pore geometry controls on porosity and permeability in the Tirrawarra Sandstone Reservoir, Cooper Basin, South Australia. AAPG Annual Convention, San Diego, May 19-22 1996, 119.
40. Rezaee M.R., Lemon N. Permeability estimation from mercury injection capillary pressure data, a case study in the Tirrawarra Sandstone, Cooper Basin Australian Petroleum. Prod. Explor. Assoc. J. 1997, 37:824.
41. Saggaf M.M., Nebrija Ed.L. A fuzzy approach for the estimation of facies from wireline logs. AAPG Bull. 2003, 87:1223-1240.
42. Scherer M. Parameters influencing porosity in sandstones: a model for sandstone porosity prediction. AAPG Bull. 1987, 71:485-491.
43. Schwartz L., Banavar J. Transport properties of disordered continuum systems. Phys. Rev. B 1989, 39:11965-11970.
44. Seeman U., Scherer M. Volcanoclastics as potential hydrocarbon reservoirs. Clay Mineral. 1984, 9:457-470.
45. Serra O. Fundamentals of Well-Log Interpretation 1984, Elsevier, New York.
46. Sharkey A.J.C. On combining artificial neural networks. Connect. Sci. 1996, 8:299-314.
47. Smosna R. Compaction law for Cretaceous sandstones of Alaska's North Slope. J. Sediment. Petrol. 1989, 59:572-584.
48. Zargar G., Bagheripour P., Asoodeh M. Fuzzy modeling of volume reduction of oil due to dissolved gas run off and pressure release. J. Pet. Explor. Prod. Technol. 2014, 10.1007/s13202-014-0099-9.
49. Zhang J., Li L., Ping S. Neural approach for calculating permeability of porous medium Chinese. Phys. Lett. 2006, 23:1009-1011.