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             References
             [1] Tariq Z, Elkatatny S, Mahmoud M, Abdulraheem A. A new artificial intelligence based
                empirical correlation to predict sonic travel time. In: International petroleum technology
                conference; Bangkok, Thailand. IPTC: International petroleum technology conference; 2016.
             [2] Li H, Misra S. Prediction of subsurface NMR T2 distribution from formation-mineral
                composition using variational autoencoder. In: SEG technical program expanded abstracts;
                2017. p. 3350–4.
             [3] Li H, Misra S. Prediction of subsurface NMR T2 distributions in a shale petroleum system using
                variational autoencoder-based neural networks. IEEE Geosci Remote Sens Lett 2017;(99):1–3.
             [4] Rezaee MR, Ilkhchi AK, Barabadi A. Prediction of shear wave velocity from petrophysical
                data utilizing intelligent systems: an example from a sandstone reservoir of Carnarvon
                Basin, Australia. J Petrol Sci Eng 2007;55(3–4):201–12.
             [5] Asoodeh M, Bagheripour P. Prediction of compressional, shear, and stoneley wave velocities
                from conventional well log data using a committee machine with intelligent systems. Rock
                Mech Rock Eng 2012;45(1):45–63.
             [6] Iverson WP, Walker JN. Shear and compressional logs derived from nuclear logs. In: SEG
                technical program expanded abstracts. Society of Exploration Geophysicists; 1988. p. 111–3.
             [7] Greenberg M, Castagna J. Shear-wave velocity estimation in porous rocks: theoretical
                formulation, preliminary verification and applications. Geophys Prospect 1992;40(2):195–209.
             [8] Maleki S, Moradzadeh A, Riabi RG, Gholami R, Sadeghzadeh F. Prediction of shear wave
                velocity using empirical correlations and artificial intelligence methods. NRIAG J Astron
                Geophys 2014;3(1):70–81.
             [9] Keys RG, Xu S. An approximation for the Xu-White velocity model. Geophysics 2002;67(5):
                1406–14.
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