Reservoir Characterization Using Multi-component Seismic Data in a Novel Hybrid Model Based on Clustering and Deep Neural Network
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s11053-021-09863-z.pdf
Reference57 articles.
1. Abdel-Fattah, M. I., Pigott, J. D., & El-Sadek, M. S. (2020). Integrated seismic attributes and stochastic inversion for reservoir characterization: Insights from Wadi field (NE Abu-Gharadig Basin, Egypt). Journal of African Earth Sciences, 161, 103661.
2. Abdideh, M., & Ameri, A. (2019). Cluster analysis of petrophysical and geological parameters for separating the electrofacies of a gas carbonate reservoir sequence. Natural Resources Research, 29(3), 1843–1856.
3. Abdulaziz, A. M., & Hawary, S. S. (2020). Prediction of carbonate diagenesis from well logs using artificial neural network: An innovative technique to understand complex carbonate systems. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2020.01.010
4. Abdulaziz, A. M., Mahdi, H. A., & Sayyouh, M. H. (2018). Prediction of reservoir quality using well logs and seismic attributes analysis with artificial neural network: A case study from farrud reservoir, al-ghani field, Libya. Journal of Applied Geophysics, 161, 239–254.
5. Babasafari, A. A., Ghosh, D., Salim, A. M. A., & Kordi, M. (2020). Lithology-dependent seismic anisotropic amplitude variation with offset correction in transversely isotropic media. Geophysical Prospecting, 68(8), 2471–2493.
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