Reservoir Characterization

Determining petrophysical properties of the subsurface such as porosity, permeability, or shale fraction that could be an indication of potential hydrocarbon-enriched zones, is an important task in exploration seismology and reservoir characterization.

Used ML Approaches:

• Artificial Neural Networks

Used Neural Networks:

• FC

• CNN

• RNN

• GAN

• U-Net

• PINN

• Autoencoder

• VGG

• DenseNet

• LSTM

Used Learning Procedures:

• Unsupervised Learning

• Supervised Learning

• Transfer Learning

• Semi-Supervised Learning

References:

1. Saggaf, M. M., Toksöz, M. N., & Mustafa, H. M. (2003). Estimation of reservoir properties from seismic data by smooth neural networks. Geophysics, 68(6), 1969-1983.

2. Iturrarán-Viveros, U., & Parra, J. O. (2014). Artificial neural networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data. Journal of Applied Geophysics, 107, 45-54

3. Cao, J., & Roy, B. (2017). Time-lapse reservoir property change estimation from seismic using machine learning. The Leading Edge, 36(3), 234-238.

4. Zhang, G., Wang, Z., & Chen, Y. (2018). Deep learning for seismic lithology prediction. Geophysical Journal International, 215(2), 1368-1387.

5. Zhang, J., Li, J., Chen, X., & Li, Y. (2020). Seismic lithology/fluid prediction via a hybrid ISD-CNN. IEEE Geoscience and Remote Sensing Letters, 18(1), 13-17.

6. Zhang, Z. D., & Alkhalifah, T. (2020). High-resolution reservoir characterization using deep learning-aided elastic full-waveform inversion: the North Sea field data example. Geophysics, 85(4), WA137-WA146.

7. Das, V., & Mukerji, T. (2020). Petrophysical properties prediction from prestack seismic data using convolutional neural networks. Geophysics, 85(5), N41-N55.

8. Priezzhev, I. I., Veeken, P. C. H., Egorov, S. V., & Strecker, U. (2019). Direct prediction of petrophysical and petroelastic reservoir properties from seismic and well-log data using nonlinear machine learning algorithms. The Leading Edge, 38(12), 949-958.

9. Li, H., Lin, J., Wu, B., Gao, J., & Liu, N. (2021). Elastic Properties Estimation From Prestack Seismic Data Using GGCNNs and Application on Tight Sandstone Reservoir Characterization. IEEE Transactions on Geoscience and Remote Sensing.

10. Weinzierl, W., & Wiese, B. (2021). Deep learning a poroelastic rock-physics model for pressure and saturation discrimination. Geophysics, 86(1), MR53-MR66.

11. Li, G., Qiao, Y., Zheng, Y., Li, Y., & Wu, W. (2019). Semi-supervised learning based on generative adversarial network and its applied to lithology recognition. IEEE Access, 7, 67428-67437.

12. Feng, R., Mejer Hansen, T., Grana, D., & Balling, N. (2020). An unsupervised deep-learning method for porosity estimation based on poststack seismic data. Geophysics, 85(6), M97-M105.

13. Feng, R. (2020). Unsupervised learning elastic rock properties from pre-stack seismic data. Journal of Petroleum Science and Engineering, 192, 107237.

14. Zhong, Z., Sun, A. Y., & Wu, X. (2020). Inversion of time‐lapse seismic reservoir monitoring data using cycleGAN: a deep learning‐based approach for estimating dynamic reservoir property changes. Journal of Geophysical Research: Solid Earth, 125(3), e2019JB018408.

15. Liu, M., & Grana, D. (2020). Time-lapse seismic history matching with an iterative ensemble smoother and deep convolutional autoencoder. Geophysics, 85(1), M15-M31.

16. Zhou, Z., Lin, Y., Zhang, Z., Wu, Y., Wang, Z., Dilmore, R., & Guthrie, G. (2019). A data-driven CO2 leakage detection using seismic data and spatial–temporal densely connected convolutional neural networks. International Journal of Greenhouse Gas Control, 90, 102790.

17. Li, D., Peng, S., Guo, Y., Lu, Y., & Cui, X. (2021). CO2 storage monitoring based on time-lapse seismic data via deep learning. International Journal of Greenhouse Gas Control, 108, 103336.