Petrophysical rock properties prediction from elastic properties using artificial neural network (ANN).

References

1. Abdulraheem, A., Ahmed, M., Vantala, A., & Parvez, T. (2009). Prediction of rock mechanical parameters for hydrocarbon reservoirs using different artificial intelligence techniques. Society of Petroleum Engineers - SPE Saudi Arabia Section Technical Symposium 2009. https://doi.org/10.2118/126094-ms
   [Google Scholar]
2. Avseth, P., Mukerji, T., & Mavko, G. (2005). Quantitative Seismic Interpretation. Cambridge University Press. https://doi.org/10.1017/CBO9780511600074
   [Google Scholar]
3. Das, V., & Mukerji, T. (2020). Petrophysical properties prediction from prestack seismic data using convolutional neural networks. Geophysics, 85(5), N41–N55. https://doi.org/10.1190/geo2019-0650.1
   [Google Scholar]
4. Das, V., Pollack, A., Wollner, U., & Mukerji, T. (2019). Convolutional neural network for seismic impedance inversion. Https://Doi.Org/10.1190/Geo2018-0838.1, 84(6), R869–R880. https://doi.org/10.1190/GEO2018-0838.1
   [Google Scholar]
5. Dvorkin, J., Gutierrez, M. A., & Grana, D. (2014). Seismic Reflections of Rock Properties. Cambridge University Press. https://doi.org/10.1017/CBO9780511843655
   [Google Scholar]
6. Elkatatny, S., Tariq, Z., Mahmoud, M., & Abdulraheem, A. (2018). New insights into porosity determination using artificial intelligence techniques for carbonate reservoirs. Petroleum, 4(4), 408–418. https://doi.org/10.1016/j.petlm.2018.04.002
   [Google Scholar]
7. Gowida, A., Moussa, T., Elkatatny, S., & Ali, A. (2019). A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks. Sustainability 2019, Vol. 11, Page 5283, 11(19), 5283. https://doi.org/10.3390/SU11195283
   [Google Scholar]
8. Grana, D., & Della Rossa, E. (2010). Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion. Geophysics, 75(3). https://doi.org/10.1190/1.3386676/ASSET/IMAGES/LARGE/17.JPEG
   [Google Scholar]
9. Mavko, G., Mukerji, T., & Dvorkin, J. (2020). The Rock Physics Handbook. The Rock Physics Handbook. https://doi.org/10.1017/9781108333016
   [Google Scholar]
10. Spikes, K., Mukerji, T., Dvorkin, J., & Mavko, G. (2007). Probabilistic seismic inversion based on rock-physics models. Geophysics, 72(5). https://doi.org/10.1190/1.2760162/ASSET/IMAGES/LARGE/057705GPY10.JPEG
    [Google Scholar]
11. Tariq, Z., Mahmoud, M., & Abdulraheem, A. (2019). An artificial intelligence approach to predict the water saturation in carbonate reservoir rocks. Proceedings - SPE Annual Technical Conference and Exhibition, 2019-Septe(1), 1–15. https://doi.org/10.2118/195804-ms
    [Google Scholar]
12. Wollner, U., Arevalo-Lopez, H., & Dvorkin, J. (2017). Seismic-scale petrophysical interpretation and gas-volume estimation from simultaneous impedance inversion. Leading Edge, 36(11), 910–915. https://doi.org/10.1190/tle36110910.1
    [Google Scholar]
13. Wollner, U., Yang, Y., & Dvorkin, J. P. (2017). Rock-physics diagnostics of an offshore gas field. Geophysics, 82(4), MR121–MR132. https://doi.org/10.1190/GEO2016-0390.1
    [Google Scholar]