Generalized regression and feed-forward back propagation neural networks in modelling porosity from geophysical well logs

Journal of Petroleum Exploration and Production Technology

Volumn 5, Issue 2, 2015, Pages 157-166

  Konaté, Ahmed Amara ^a   Pan, Heping ^a   Khan, Nasir ^a   Yang, Jie Huai ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

Computational geophysics; Generalized regression neural network Feed forward back propagation; Geophysical well logs; Porosity prediction; Reservoir evaluation; Reservoir properties

Indexed keywords

BACKPROPAGATION; DATA FLOW ANALYSIS; FEEDFORWARD NEURAL NETWORKS; FORECASTING; GASOLINE; GEOPHYSICS; OIL FIELDS; PETROLEUM GEOLOGY; PETROLEUM INDUSTRY; PETROLEUM PROSPECTING; PETROLEUM RESERVOIR EVALUATION; POROSITY; WELL LOGGING;

FEED-FORWARD BACK PROPAGATION; FEED-FORWARD BACK-PROPAGATION NEURAL NETWORKS; GENERALIZED REGRESSION NEURAL NETWORK(GRNN); HYDROCARBON EXPLORATION; NON-LINEAR RELATIONSHIPS; POROSITY PREDICTIONS; RESERVOIR PROPERTY; WELL LOGS;

OIL WELL LOGGING;

EID: 84946826295     PISSN: 21900558     EISSN: 21900566     Source Type: Journal    
DOI: 10.1007/s13202-014-0137-7     Document Type: Article

References (18)

1. Abraham A (2005) Artificial neural networks, handbook of measuring system design. In: Sydenham PH, Thorn R (eds) ISBN: 0-470- 02143-8, Wiley, Hoboken
2. Bishop C (1995) Neural networks for pattern recognition. Oxford Press, Oxford
3. Celikoglu HB (2006) Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling. Math Comput Model 44:640-658
4. Cigizoglua HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37:63-68
5. Hagan MT, Menhaj MB (1994) Training feed forward techniques with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989-993
6. Haldorsen HH, Damsleth E (1993) Challenges in reservoir characterization. AAPG Bull 77:541-551
7. Handhel AM (2009) Prediction of reservoir permeability from wire logs data using artificial neural networks. Iraqi J Sci 50(1):67-74
8. Haykin S (2007) Neural networks: a comprehensive foundation, Third edn. Prentice Hall, Inc., Upper Saddle River
9. Hecht-Nielsen R (1989) Theory of backpropagation neural networks. In: Presented at IEEE Proceedings, international conference on neural network, Washington DC
10. Hornik K, Stinchcombe MB, White H (1989) Multilayer feed forward networks are universal approximators. Neural Netw 2(5):359-366
11. Mohaghegh S, Arefi R, Ameri S, Aminian K, Nutter R (1996) Petroleum reservoir characterization with the aid of artificial neural networks. J Petrol Sci Eng 16(4):263-274
12. Poulton MM (2002) Neural networks as an intelligence amplification tool: a review of applications. Geophysics 67(3):979-993
13. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568-576
14. Sun G, Hoff SJ, Zelle BC, Nelson MA (2008) Nelson Development and comparaison of backpropagation and generalized neural network models to predict diurnal and seasonal gas and PM10 concentrations and emissions from buildings. Trans ASABE 51(2):685-694
15. Van der Baan M, Jutten C (1992) Neural networks in geophysical applications. Geophysics 65(4):1032-1047
16. White H (1992) Artificial neural networks. Approximation and learning theory. Blackwell, Cambridge
17. Wong PM, Gedeon TD, Taggart J (1995) An improved technique in porosity prediction: a neural network approach. IEEE-TGRS 33(4):971-980
18. Yan J (2002) Reservoir parameters estimation from well log and core data: a case study from the North Sea. Pet Geosci 8:63-69