References
- Akinnikawe, O., Lyne, S., & Roberts, J. (2018). Synthetic Well Log Generation Using Machine Learning Techniques. Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA.
- Bhatt, A. (2002). Reservoir properties from well logs using neural networks. PhD. Dissertation, Department of Petroleum Engineering and Applied Geophysics. Ph.D. thesis, Norwegian University of Science and Technology.
- Dubois, M. K., Bohling, G. C., & Chakrabarti, S. (2007). Comparison of four approaches to a rock facies classification problem. Computers & Geosciences, 33(5), 599-617. doi:https://doi.org/10.1016/j.cageo.2006.08.011
- Eshkalak, M. O., Mohaghegh, S. D., & Esmaili, S. (2013). Synthetic, Geomechanical Logs For Marcellus Shale. Paper presented at the SPE Digital Energy Conference, The Woodlands, Texas, USA. https://doi.org/10.2118/163690-MS
- Eshkalak, M. O., Mohaghegh, S. D., & Esmaili, S. (2014). Geomechanical Properties of Unconventional Shale Reservoirs. Journal of Petroleum Engineering, 2014, 10. doi:10.1155/2014/961641
- Hall, B. (2016). Facies classification using machine learning. The Leading Edge, 35(10), 906-909. doi:10.1190/tle35100906.1
- Johnson, L. M., Rezaee, R., Kadkhodaie, A., Smith, G., & Yu, H. (2018). Geochemical property modelling of a potential shale reservoir in the Canning Basin (Western Australia), using Artificial Neural Networks and geostatistical tools. Computers & Geosciences, 120, 73-81. doi:https://doi.org/10.1016/j.cageo.2018.08.004
- Lopes, R., & Jorge, A. (2017). Mind the Gap: A Well Log Data Analysis.
- Saggaf M., M., & Nebrija L., E. (2000). Estimation Of Lithologies And Depositional Facies From Wireline Logs (Vol. 84).