Estimation of shear and Stoneley wave velocities from conventional well data using different intelligent systems and the concept of committee machine: an example from South Pars gas field, Persian Gulf

References

• Anemangely, M., A. Ramezanzadeh, and B. Tokhmechi. 2017. Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour oilfield. Journal of Natural Gas Science and Engineering 38:373–87. doi:10.1016/j.jngse.2017.01.003.
   Web of Science ®Google Scholar
• Anemangely, M., A. Ramezanzadeh, H. Amiri, and S. A. Hoseinpour. 2019. Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering 174:306–27. doi:10.1016/j.petrol.2018.11.032.
   Web of Science ®Google Scholar
• Bhatt, A., and H. B. Helle. 2002. Committee neural networks for porosity and permeability prediction from well logs. Geophysical Prospecting 50:645–60. doi:10.1046/j.1365-2478.2002.00346.x.
   Web of Science ®Google Scholar
• Castagna, J. P., M. L. Batzle, and R. L. Eastwood. 1985. Relationship between compressional and shear wave velocities in silicate rocks. Geophysics 50:571–81. doi:10.1190/1.1441933.
   Web of Science ®Google Scholar
• Chiu, S. 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems 2:267–78.
   Web of Science ®Google Scholar
• Du, Q., Q. Yasin, A. Ismail, and G. Sohail. 2019. Combining classification and regression for improving shear wave velocity estimation from well logs data. Journal of Petroleum Science and Engineering 182:106260. doi:10.1016/j.petrol.2019.106260.
   Web of Science ®Google Scholar
• Gholami, R., A. Moradzadeh, V. Rasouli, and J. Hanachi. 2014. Shear wave velocity prediction using seismic attributes and well log data. Acta Geophysica 62:818–48. doi:10.2478/s11600-013-0200-7.
   Web of Science ®Google Scholar
• Labani, M. M., A. Kadkhodaie-Ilkhchi, and K. Salahshoor. 2010. Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: A case study from the Iranian part of the South Pars gas field, Persian Gulf Basin. Journal of Petroleum Science and Engineering 72:175–85. doi:10.1016/j.petrol.2010.03.015.
   Web of Science ®Google Scholar
• Mehrgini, M., H. Izadi, and H. Memarian. 2017. Shear wave velocity prediction using Elman artificial neural network. Carbonates and Evaporites 1–11. doi: 10.1007/s13146-017-0406-x.
   PubMed Web of Science ®Google Scholar
• Nikravesh, M., F. Aminzadeh, and L. A. Zadeh (Eds.). 2003. Soft computing and intelligent data analysis in oil exploration. In Part 1: Introduction: Fundamentals of soft computing, 744. Berkeley, USA: Elsevier.
   Google Scholar
• Noori, R., G. Hoshyaripour, K. Ashrafi, and B. Araabi. 2010. Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment 44:476–82. doi:10.1016/j.atmosenv.2009.11.005.
   Web of Science ®Google Scholar
• Rajabi, M., B. Bohloli, and E. Gholampour Ahangar. 2010. Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: A case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran). Computers & Geosciences 36:647–64. doi: 10.1016/j.cageo.2009.09.008.
   Web of Science ®Google Scholar
• Rezaee, M. R., A. Kadkhodaie Ilkhchi, and P. M. Alizadeh. 2008. Intelligent approaches for the synthesis of petrophysical logs. Journal of Geophysics and Engineering 5:12–26. doi:10.1088/1742-2132/5/1/002.
   Web of Science ®Google Scholar
• Rezaee, M. R., and J. K. Applegate. 1997. Shear velocity prediction from wireline logs, an example from Carnarvon Basin, NW Shelf, Australia. SEG (society of Exploration Geophysicists) Expanded Abstracts 16:945–47.
   Google Scholar
• Rolon, L., S. D. Mohaghegh, S. Ameri, R. Gaskari, and B. McDaniel. 2009. Using artificial neural networks to generate synthetic well logs. Journal of Natural Gas Science and Engineering 1:118–33. doi:10.1016/j.jngse.2009.08.003.
   Web of Science ®Google Scholar
• Shiroodi, S., M. Ghafoori, H. Ansari, G. Lashkaripour, and M. Ghanadian. 2017. Shear wave prediction using committee fuzzy model constrained by lithofacies, Zagros basin, SW Iran. Journal of African Earth Sciences 126:123–35. doi:10.1016/j.jafrearsci.2016.11.016.
   Web of Science ®Google Scholar
• Singh, S., and A. Kanli. 2016. Estimating shear wave velocities in oil fields: A neural network approach. Geosciences Journal 20:221–28. doi:10.1007/s12303-015-0036-z.
   Web of Science ®Google Scholar
• Takagi, T., and M. Sugeno. 1985. Identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 15:116–32. doi:10.1109/TSMC.1985.6313399.
   Web of Science ®Google Scholar
• Vapnik, V., S. Golowich, and A. J. Smola. 1996. Support vector method for function approximation. Regression Estimation, and Signal Processing, Advances in Neural Information Processing Systems 9:281–87.
   Google Scholar
• Wang, P., and S. Peng. 2019. On a new method of estimating shear wave velocity from conventional well logs. Journal of Petroleum Science and Engineering 180:105–23. doi:10.1016/j.petrol.2019.05.033.
   Web of Science ®Google Scholar