With the deepening of oil and gas exploration, the exploration targets have gradually shifted from structural oil and gas reservoirs to lithological oil and gas reservoirs. The fluvial sandstone reservoir is an important type of lithological oil and gas reservoir. The fluvial sandstone presents characteristics in seismic data, such as the uneven thickness that is difficult to distinguish, the poor lateral continuity, and the strong subjectivity in tracking and interpretation, which pose great challenges to seismic interpretation work. To solve this problem, this study proposes a method for identifying channel sand bodies based on convolutional neural networks using the image semantic segmentation idea. This method mainly adopts the U-Net network structure, which can effectively combine low-resolution information and high-resolution information. The actual application effect shows that compared with traditional manual interpretation, this method has higher efficiency and accuracy.
References
[1]
Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the Dimensionality of Data with Neural Networks. Science, 313, 504-507. https://doi.org/10.1126/science.1127647
[2]
Si, X., Yuan, Y., Si, T. and Gao, S. (2019) Attenuation of Random Noise Using Denoising Convolutional Neural Networks. Interpretation, 7, SE269-SE280. https://doi.org/10.1190/int-2018-0220.1
[3]
Saad, O.M. and Chen, Y. (2020) Deep Denoising Autoencoder for Seismic Random Noise Attenuation. Geophysics, 85, V367-V376. https://doi.org/10.1190/geo2019-0468.1
[4]
Song, H., Gao, Y., Chen, W., Xue, Y., Zhang, H. and Zhang, X. (2020) Seismic Random Noise Suppression Using Deep Convolutional Autoencoder Neural Network. Journal of Applied Geophysics, 178, Article ID: 104071. https://doi.org/10.1016/j.jappgeo.2020.104071
[5]
Yuan, S., Liu, J., Wang, S., Wang, T. and Shi, P. (2018) Seismic Waveform Classification and First-Break Picking Using Convolution Neural Networks. IEEE Geoscience and Remote Sensing Letters, 15, 272-276. https://doi.org/10.1109/lgrs.2017.2785834
[6]
Yuan, P., Wang, S., Hu, W., Wu, X., Chen, J. and Van Nguyen, H. (2020) A Robust First-Arrival Picking Workflow Using Convolutional and Recurrent Neural Networks. Geophysics, 85, U109-U119. https://doi.org/10.1190/geo2019-0437.1
[7]
Zheng, H. and Zhang, B. (2020) Intelligent Seismic Data Interpolation via Convolutional Neural Network. Progress in Geophysics, 35, 721-727.
[8]
Wu, X., Liang, L., Shi, Y. and Fomel, S. (2019) FaultSeg3D: Using Synthetic Data Sets to Train an End-to-End Convolutional Neural Network for 3D Seismic Fault Segmentation. Geophysics, 84, IM35-IM45. https://doi.org/10.1190/geo2018-0646.1
[9]
Tschannen, V., Delescluse, M., Ettrich, N. and Keuper, J. (2020) Extracting Horizon Surfaces from 3D Seismic Data Using Deep Learning. Geophysics, 85, N17-N26. https://doi.org/10.1190/geo2019-0569.1
[10]
Qian, F., Yin, M., Liu, X., Wang, Y., Lu, C. and Hu, G. (2018) Unsupervised Seismic Facies Analysis via Deep Convolutional Autoencoders. Geophysics, 83, A39-A43. https://doi.org/10.1190/geo2017-0524.1
[11]
Duan, Y., Zheng, X., Hu, L. and Sun, L. (2019) Seismic Facies Analysis Based on Deep Convolutional Embedded Clustering. Geophysics, 84, IM87-IM97. https://doi.org/10.1190/geo2018-0789.1
[12]
Pham, N., Fomel, S. and Dunlap, D. (2019) Automatic Channel Detection Using Deep Learning. Interpretation, 7, SE43-SE50. https://doi.org/10.1190/int-2018-0202.1
[13]
Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W. and Frangi, A., Eds., Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Springer, 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
