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  • 1 Exploration and Production Division, MOL Hungarian Oil and Gas Plc. H-1039 Budapest, Batthyány u. 45, Hungary
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Seismic data integration in reservoir modeling workflows is the one of the fastest-growing fields in the Earth Sciences. The actual geostatistical methods (co-kriging, stochastic simulation) can use seismic data as a secondary variable if there is a well-determined linear correlation between well log data and seismic attribute. Seismic interpreters must often increase this correlation. The application of multi-attributes via neural network may help in this case. A neural network type, called multi-layer perceptron, and its application in 3D porosity distribution prediction in a Hungarian natural gas reservoir, are described in this paper.

  • Hagan, M., T., H. B. Demuth, M. H. Beale 1996: Neural Network Design. - Boston, MA: PWS Publishing, 637 p.

    Neural Network Design , () 637.

  • Hampson, D., J. S. Schuelke, J. A. Quirein 2001: Use of multiattribute transforms to predict log properties from seismic data. - Geophysics, 66, pp. 220 - 231 .

    'Use of multiattribute transforms to predict log properties from seismic data. - ' () 66 Geophysics : 220 -231.

    • Search Google Scholar
  • Haykin, S., 1999: Neural Networks - a Comprehensive Foundation. - Prentice Hall, New Jersey, 2nd edition, 842 p.

    Neural Networks - a Comprehensive Foundation , () 842.

  • Sinvhal, A., H. Sinvhal 1992: Seismic Modelling and Pattern Recognition in Oil Exploration. - Kluwer Academic Publishers, 178 p.

    Seismic Modelling and Pattern Recognition in Oil Exploration , () 178.

  • Metropolis, N., A. Rosenbluth, M. Rosenbluth, A. Teller, E. Teller 1953: Equation of State Calculations by Fast Computing Machines. - J. Chem. Phys., 21/6, pp. 1087 - 1092 .

    'Equation of State Calculations by Fast Computing Machines. - ' () 21 J. Chem. Phys. : 1087 -1092.

    • Search Google Scholar
  • Rosenblatt, F., 1962: Principles of NeurodyWintershallics: Perceptrons and the Theory of Brain Mechanisms. - Washington D.C., Spartan Books, 24 p.

    Principles of NeurodyWintershallics: Perceptrons and the Theory of Brain Mechanisms , () 24.

    • Search Google Scholar
  • Rumelhart, D., E., G. E. Hinton, R. Williams 1986: Learning internal representations by error propagation. - In: D. Rumelhart E., J. L. McClelland (Eds): Parallel Data Processing, vol. 1, Cambridge, MA: The M. I. T. Press, pp. 318 - 362 .

    The Learning internal representations by error propagation , () 318 -362.

  • Lippmann, R., P. 1989: Pattern Classification Using Neural Networks. - IEEE Communications Magazine, 27/11, pp. 47 - 50, 59-64.

    'Pattern Classification Using Neural Networks. - ' () 27 IEEE Communications Magazine : 47 -50.

    • Search Google Scholar
  • Schultz, P., S. S. Ronen, M. Hattori, C. Corbett 1994: Seismic-guided estimation of log properties, Part 1: A data-driven interpretation methodology. The Leading Edge, Vol. 13/5, pp. 305-315; Part 2: Using artificial neural networks for nonlinear attribute calibration. The Leading Edge, Vol. 13/6, pp. 674 - 678; Part 3: A controlled study. The Leading Edge, 13/7, pp. 770 - 777 .

  • Taner, M., T., F. Koehler, R. E. Sheriff 1979: Complex seismic trace analysis. - Geophysics, 44, pp. 1041 - 1063 .

    'Complex seismic trace analysis. - ' () 44 Geophysics : 1041 -1063.

  • Taner, M., T. 1997: Seismic trace attributes and their projected use in prediction of rock properties and seismic facies. - Rock Solid Images technical publication, http://www.rocksolidimages.com

    Seismic trace attributes and their projected use in prediction of rock properties and seismic facies , ().

    • Search Google Scholar
  • Todorov, T., R. Stewart, D. Hampson, B. Russell 1998: Well Log Prediction Using Attributes from 3C-3D Seismic Data. - CREWES Research Report - Vol. 10 52 1 -52-13.

    'Well Log Prediction Using Attributes from 3C-3D Seismic Data ' () Vol. 10 CREWES Research Report : 52 -1.

    • Search Google Scholar