Abstract
This article investigates bypassing the inversion steps involved in a standard litho-type classification pipeline and performing the litho-type classification directly from imaged seismic data. We consider a set of deep learning methods that map the seismic data directly into litho-type classes, trained on two variants of synthetic seismic data: (i) one in which we image the seismic data using a local Radon transform to obtain angle gathers, (ii) and another in which we start from the subsurface-offset gathers, based on correlations over the seismic data. Our results indicate that this single-step approach provides a faster alternative to the established pipeline while being convincingly accurate. We observe that adding the background model as input to the deep network optimization is essential in correctly categorizing litho-types. Also, starting from the angle gathers obtained by imaging in the Radon domain is more informative than using the subsurface offset gathers as input.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Code Availability
We build on open-source pytorch (https://pytorch.org/) model implementations.
References
Bachrach, R., Perdomo, J., Mallick, S., et al: Propagating seismic data quality into rock physics analysis and reservoir property estimation: Case study of lithology prediction using full waveform inversion in clastic basins. In: 2003 SEG Annual Meeting, OnePetro (2003)
Backus, G.E.: ,Long-wave elastic anisotropy produced by horizontal layering. J. Geophys. Res. 67(11), 4427–4440 (1962)
Bhatt, A., Helle, H.B.: Determination of facies from well logs using modular neural networks. Pet. Geosci. 8(3), 217–228 (2002)
Bosch, M., Mukerji, T., Gonzalez, E.F.: Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review. GEOPHYSICS TA - TT - 75(5), 165–175 (2010). https://doi.org/10.1190/1.3478209
Cuddy, S., et al: Litho-facies and permeability prediction from electrical logs using fuzzy logic. SPE Reserv. Evaluation Eng. 3(04), 319–324 (2000)
Dafni, R., Symes, W.W.: Scattering and dip angle decomposition based on subsurface offset extended wave-equation migration. Geophysics 81(3), S119–S138 (2016)
Das, V., Mukerji, T.: Petrophysical properties prediction from prestack seismic data using convolutional neural networks. Geophysics 85(5), N41–N55 (2020)
Das, V., Pollack, A., Wollner, U., et al: Convolutional neural network for seismic impedance inversion. In: SEG Technical Program Expanded Abstracts 2018. Society of Exploration Geophysicists, p. 2071–2075 (2018)
De Bruin, C., Wapenaar, C., Berkhout, A.: Angle-dependent reflectivity by means of prestack migration. Geophysics 55(9), 1223–1234 (1990)
Feng, R., Luthi, S., Gisolf, A.: Reservoir lithology classification by the hidden markov model. In: Fourth EAGE Exploration Workshop, European Association of Geoscientists & Engineers, vol. 1, pp 1–5 (2017)
Feng, R., Luthi, S.M., Gisolf, D., et al.: Obtaining a high-resolution geological and petrophysical model from the results of reservoir-orientated elastic wave-equation-based seismic inversion. Pet. Geosci. 23 (3), 376–385 (2017)
Feng, R., Luthi, S.M., Gisolf, D., et al.: Obtaining a high-resolution geological and petrophysical model from the results of reservoir-orientated elastic wave-equation-based seismic inversion. Pet. Geosci. 23 (3), 376–385 (2017). https://doi.org/10.1144/petgeo2015-076
Feng, R., Luthi, S.M., Gisolf, D., et al.: Reservoir lithology classification based on seismic inversion results by hidden markov models: Applying prior geological information. Mar. Pet. Geol. 93, 218–229 (2018). https://doi.org/10.1016/j.marpetgeo.2018.03.004
Ganssle, G.: Neural networks. Lead. Edge 37(8), 616–619 (2018)
Gisolf, A., van den Berg, P.: Target oriented non-linear inversion of seismic data. In: 72nd EAGE Conference and Exhibition-Workshops and Fieldtrips, European Association of Geoscientists & Engineers, pp. cp–161 (2010)
Gisolf, A., van den Berg, P.: Target-oriented non-linear inversion of time-lapse seismic data. In: SEG Technical Program Expanded Abstracts 2010. Society of Exploration Geophysicists, pp. 2860–2864 (2010)
Gisolf, D., Haffinger, P.R., Doulgeris, P.: Reservoir-oriented wave-equation-based seismic amplitude variation with offset inversion, vol. 5. https://doi.org/10.1190/int-2016-0157.1 (2017)
Goodfellow, I., Bengio, Y., Courville, A., et al: Deep learning. 2, MIT press Cambridge (2016)
Kennett, B.: Seismic wave propagation in stratified media. ANU Press (2009)
LeCun, Y., Bottou, L., Bengio, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Li, Y., Anderson-Sprecher, R.: Facies identification from well logs: A comparison of discriminant analysis and naïve bayes classifier. J. Pet. Sci. Eng. 53(3), 149–157 (2006)
Loog, M.: Supervised classification: Quite a brief overview. In: Machine Learning Techniques for Space Weather, pp 113–145. Elsevier (2018)
Ronneberger O, Fischer P, Brox T: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, pp 234–241. Springer (2015)
Sakurai, S., Melvin, J., et al: Facies discrimination and permeability estimation from well logs for the endicott field. In: SPWLA 29th Annual Logging Symposium, Society of Petrophysicists and Well-Log Analysts (1988)
Sava, P.C., Fomel, S.: Angle-domain common-image gathers by wavefield continuation methods. Geophysics 68(3), 1065–1074 (2003)
Sharma, S., Gisolf, D., Luthi, S., et al.: Strategies to include geological knowledge in full waveform inversion. In: International Conference and Exhibition, Barcelona, Spain, 3-6, April, 2016. Society of Exploration Geophysicists and American Association of Petroleum Geologists. https://doi.org/10.1190/ice2016-6518022.1 (2016)
Sharma, S., Gisolf, D., Luthi, S.: Bayesian update of wave equation based seismic inversion using geological prior information and scenario testing. In: 80th EAGE Conference and Exhibition 2018, European Association of Geoscientists & Engineers, vol. 1, pp 1–5 (2018)
Siripitayananon, P., Chen, H.C., Hart, B.S.: A new technique for lithofacies prediction: back-propagation neural network. In: Proceedings of ACMSE: The 39th Association of Computing and Machinery South Eastern Conference, Citeseer, pp. 31–38 (2001)
Symes, W.W.: Migration velocity analysis and waveform inversion. Geophys. Prospect. 56(6), 765–790 (2008)
Tang, H., White, C.D.: Multivariate statistical log log-facies classification on a shallow marine reservoir. J. Pet. Sci. Eng. 61(2-4), 88–93 (2008)
Vossepoel, F., Darnet, M., Gesbert, S., et al.: Detecting hydrocarbons in carbonates: Joint interpretation of CSEM and seismic. In: Society of Exploration Geophysicists International Exposition and 80th Annual Meeting 2010, SEG (2010)
White, R., Simm, R.: Tutorial: Good practice in well ties. First Break. 21(10) (2003)
van Wijngaarden, A.: Imaging and characterization of angle-dependent seismic reflection data. PhD thesis, Delft University of Technology (1998)
Zhao, T., Li, F., Marfurt, K.J.: Seismic attribute selection for unsupervised seismic facies analysis using user-guided data-adaptive weights. Geophys. 83(2), O31–O44 (2018)
Zheng, Y.: Elastic pre-stack seismic inversion in stratified media using machine learning. In: 81st EAGE Conference and Exhibition 2019 (2019)
Acknowledgements
The authors would like to thank the sponsoring companies in the Delphi Consortium for their support.
Funding
The authors would like to thank the sponsoring companies in the Delphi Consortium for their support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
TUDelft.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Silvia L. Pintea and Siddharth Sharma contributed equally to this work.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Pintea, S.L., Sharma, S., Vossepoel, F.C. et al. Seismic inversion with deep learning. Comput Geosci 26, 351–364 (2022). https://doi.org/10.1007/s10596-021-10118-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10596-021-10118-2