Author:
Shi Su-Zhen,Shi Gui-Fei,Pei Jin-Bo,Li-Li ,Zhao Kang,He Ya-Zhou
Publisher
Springer Science and Business Media LLC
Reference29 articles.
1. Al-Anazi, A. F. and Gates, I. D., 2012, Support vector regression to predict porosity and permeability: Effect of sample size: Computers & Geosciences, 39(1), 64–76.
2. Agbadze, O. K., Qiang, C., and Jiaren, Y., 2022, Acoustic impedance and lithology-based reservoir porosity analysis using predictive machine learning algorithms: Journal of Petroleum Science & Engineering, 208, 109656.
3. Al Moqbel, A. and Wang, Y., 2011, Carbonate reservoir characterization with lithofacies clustering and porosity prediction: Journal of Geophysics and Engineering, 8(4), 592–598.
4. An, P., Cao, D., Zhao, B., et al., 2019, Reservoir physical parameters prediction based on LSTM recurrent neural network: Progress in Geophysics, 34(5), 10.
5. Anh, D. T., Tanim, A. H., Kushwaha, D. P., et al., 2023, Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers: International Journal of Sediment Research, 38(1), 128–140.