ICANN ’93 pp 875-881 | Cite as

Lithofacies Identification from Wireline Logs

Bringing Neural Networks to Application
  • Willem J. M. Epping
  • Sandra M. Oudshoff
  • Frances V. Abbots
Conference paper

Abstract

Neural-network classifiers, of the multi-layered perceptron type, were trained to identify lithofacies (rock types) from wireline logs in two ways: (1) by a semi-autonomous method based on pre-segmented log intervals and (2) by an autonomous method based on non-segmented data. Good results were achieved in reservoirs from two geologically different environments (siliciclastics and carbonates) without having to fine-tune the network parameters. The performance was substantially better than that of linear discriminant and Gaussian classifiers. The standard neural-network algorithm has been modified to increase its robustness e. g. to over-training. A user-friendly neural-network module has been embedded in an existing computing environment to facilitate the use of neural networks by formation analysts.

Keywords

Permeability Entropy Porosity Petroleum Hydrocarbon 

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References

  1. [1]
    Wolff, M. and Pelissier-Combescure, J.: “Faciolog — automatic electrofacies determination”, SPWLA 23rd Annual Logging Symposium (1982) July 6–9, paper FF.Google Scholar
  2. [2]
    Delfiner, P., Peyret, O. and Serra, O.: “Automatic determination of lithology from well logs”, SPE Formation Evaluation (1987) September, 303–310.Google Scholar
  3. [3]
    Bush, J.M., Fortney, W.G. and Berry, L.N.: “Determination of lithology from well logs by statistical analysis”, SPE Formation Evaluation (1987) December, 412–418.Google Scholar
  4. [4]
    Baldwin, J.L., Bateman, R.M. and Wheatley, C.L.: “Application of a neural network to the problem of mineral identification from well logs”, The Log Analyst (1990) September-October, 279–293.Google Scholar
  5. [5]
    Cardon, H.R.A., Van Hoogstraten, R. and Davies, P.: “A neural network application in geology: identification of genetic facies”, Proceedings of the International Conference on Artificial Neural Networks, Espoo Finland, (1991) June 24–28, p. 809–813.Google Scholar
  6. [6]
    Liu, R.L., Zhou, C.D. and Jin, Z.W.: “Lithofacies sequence recognition from well logs using time-delay neural networks”, SPWLA 33rd Annual Logging Symposium (1992) June 14–17, paper L.Google Scholar
  7. [7]
    Rumelhart, D.E., McClelland, J.L. and the PDP Research Group. Parallel distributed processing, explorations in the microstructure of cognition, Vol. 1: Foundations. MIT Press, Cambridge MA, 1986.Google Scholar
  8. [8]
    Kaiman, B.L. and Kwasny, S.C.: “Why tanh: choosing a sigmoidal function”, Proceedings of the International Joint Conference on Neural Networks, Baltimore MD, (1992) June 7–11, Vol. 4, p. 578–581.Google Scholar
  9. [9]
    Solla, S.A., Levin, E. and Fleisher, M.: “Accelerated learning in layered neural networks”, Complex Systems (1988) 2, 625–640.MathSciNetMATHGoogle Scholar
  10. [10]
    Weigend, A.S., Rumelhart, D.E. and Huberman, B.A.: “Generalisation by weight-elimination with application to forecasting”, Advances of Neural Information Processing Systems 3, Morgan Kaufmann, San Mateo CA, (1991), p. 875–882.Google Scholar
  11. [11]
    Heskes, T.M., Slijpen, E.T.P. and Kappen, B.: “Cooling schedules for learning in neural networks”, Physical Review E (accepted).Google Scholar
  12. [12]
    Holmström, L. and Koistinen, P.: “Using additive noise in back-propagation training”, IEEE trans. Neural Networks (1992) 3, 24–38.CrossRefGoogle Scholar
  13. [13]
    Richard, M.D. and Lippmann, R.P.: “Neural network classifiers estimate Bayesian a posteriori probabilities, Neural Computation (1991) 3, 461–483.CrossRefGoogle Scholar
  14. [14]
    Garcia, G. and Whitman, W.W.: “Inversion of a lateral log by using neural networks”, SPE 7th Petroleum Computer Conference, Houston TX, (1992) July 19–22, paper 24454.Google Scholar
  15. [15]
    Wiener, J.M., Rogers, J.A., Rogers, J.R. and Moll, R.F.: “Predicting carbonate permeabilities from wireline logs using a back-propagation neural network”, SEG 61st Annual International Meeting, Houston TX, (1991) November 10–14, p. 285–288.Google Scholar

Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • Willem J. M. Epping
    • 1
  • Sandra M. Oudshoff
    • 1
  • Frances V. Abbots
    • 1
  1. 1.Shell Research B.V.Koninklijke/Shell Exploratie en Produktie LaboratoriumAB RijswijkThe Netherlands

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