Geosciences Journal

, Volume 20, Issue 2, pp 221–228 | Cite as

Estimating shear wave velocities in oil fields: a neural network approach

  • Sagar Singh
  • Ali Ismet Kanli


In this study, we applied the back-propagation Artificial Neural Network (ANN) technique to test the shear-velocity for the two wells from an oil field in southeastern region of Turkey estimated from an empirical relationship. The input to the neural network includes neutron porosity, density, true resistivity, P-wave velocity and gamma-ray logs which are known to affect the shearwave velocity. The correlation between the shear-wave velocity from the empirical relationship and that from the neural network is close to one in both the training and testing stages. Thus, the ANN technique can be used to predict shear-wave velocity from other well log data.

Key words

ANN back-propagation well-log data shear wave velocity oil field 


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  1. Akhundi, H., Ghafoori, M., and Lashkaripour, G.R., 2014, Prediction of shear wave velocity using artificial neural network technique, multiple regression and petrophysical data: A case study in Asmari reservoir (SW Iran). Open Journal of Geology, 4, 303–313.CrossRefGoogle Scholar
  2. Avseth, P., Mukerji, T., and Mavko, G., 2005, Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk, Cambridge University Press, Cambridge, 359 p.CrossRefGoogle Scholar
  3. Baan, M. and Jutten, C., 2000, Neural networks in geophysical applications. Geophysics, 65, 1032–1047.CrossRefGoogle Scholar
  4. Castagna, J.P., Batzle, M.L., and Eastwood, R.L., 1985, Relationships between compressional-wave and shear wave velocities in clastic silicate rocks. Geophysics, 50, 571–581.CrossRefGoogle Scholar
  5. Castagna, J.P., Batzle, M.L., and Kan, T.K., 1993, Rock physics: The link between rock properties and amplitude-versus offset response. In: Castagna, J.P. and Backus, M. (eds.), Offset-Dependent Reflectivity-Theory and practice of AVO analysis. Investigations in Geophysics, 8, 135–177.Google Scholar
  6. Demaison, G.J. and Moore, G.T., 1980, Anoxic environments and oil source bed genesis. American Association of Petroleum Geologists Bulletin, 64, 1179–1209.Google Scholar
  7. Demirel, I.H. and Guneri, S., 2000, Cretaceous Carbonates in the Adiyaman Region, SE Turkey: an Assessment of Burial History and Source-Rock Potential. Journal of Petroleum Geology, 23, 91–106.CrossRefGoogle Scholar
  8. Eskandari, H., Rezaee, M.R., and Mohammadnia, M., 2004, Application of Multiple regression and Artificial Neural Network Techniques to Predict Shear Wave Velocity from Wireline Log Data for a Carbonate Reservoir, South-West Iran. Canadian Society of Exploration Geophysicists Recorder, 29, 42–48.Google Scholar
  9. Greenberg, M.L. and Castagna, J.P., 1992, Shear-wave velocity estimation in porous rocks: Theoretical formulation, preliminary verification and applications. Geophysical Prospecting, 40, 195–209.CrossRefGoogle Scholar
  10. Krief, M., Garat, J., Stellingwerff, J., and Ventre, J., 1990, A petrophysical interpretation using the velocities of P and S waves (full-waveform sonic). The Log Analyst, 31, 355–369.Google Scholar
  11. Okay, O., 1996, the determination of volume and porosity in the reservoir of the oil-wells. Ph.D. Thesis, Graduate School of Science and Engineering, Istanbul University, Istanbul, 88 p.Google Scholar
  12. Serra, O., 1984, Fundamentals of Well-Log Interpretation. Elsevier, Amsterdam, 229 p.Google Scholar
  13. Taner, M., 1995, Neural networks and computation of neural network weights and biases by the generalized delta rule and back-propagation of errors. Rock solid images, 1–11. Scholar
  14. Wagner, C. and Pehlivan, M., 1987, Geological control in the distribution of source rocks and reservoir in upper cretaceous carbonates of Southeast Turkey. Journal of Petroleum Science and Engineering, 1, 105–114.CrossRefGoogle Scholar
  15. Wong, P.M. and Nikravesh M., 2001, Introduction: Field Applications of Intelligent Computing Techniques. Journal of Petroleum Geology, 24, 381–387.CrossRefGoogle Scholar

Copyright information

© The Association of Korean Geoscience Societies and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Earth SciencesIndian Institute of Technology RoorkeeRoorkeeIndia
  2. 2.Department of Geophysical Engineering, Faculty of EngineeringIstanbul University, Avcilar CampusIstanbulTurkey

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