Estimating shear wave velocities in oil fields: a neural network approach
- 213 Downloads
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 wordsANN back-propagation well-log data shear wave velocity oil field
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- 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
- 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
- Serra, O., 1984, Fundamentals of Well-Log Interpretation. Elsevier, Amsterdam, 229 p.Google Scholar
- 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. http://www.rocksolidimages.com/pdf/neural_network.pdfGoogle Scholar