Advances in the application of deep learning methods to digital rock technology
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Abstract
Digital rock technology is becoming essential in reservoir engineering and petrophysics. Three-dimensional digital rock reconstruction, image resolution enhancement, image segmentation, and rock parameters prediction are all crucial steps in enabling the overall analysis of digital rocks to overcome the shortcomings and limitations of traditional methods. Artificial intelligence technology, which has started to play a significant role in many different fields, may provide a new direction for the development of digital rock technology. This work presents a systematic review of the deep learning methods that are being applied to tasks within digital rock analysis, including the reconstruction of digital rocks, high-resolution image acquisition, grayscale image segmentation, and parameter prediction. The results of these applications prove that state-of-the-art deep learning methods can help advance and provide a new approach to scientific knowledge in the field of digital rocks. This work also discusses future research and developments on the application of deep learning methods to digital rock technology.
Document Type: Invited review
Cited as: Li, X., Li, B., Liu, F., Li, T., Nie, X. Advances in the application of deep learning methods to digital rock technology. Advances in Geo-Energy Research, 2023, 8(1): 5-18. https://doi.org/10.46690/ager.2023.04.02
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Akai, T., Alhammadi, A. M., Blunt, M. J., et al. Modeling oil recovery in mixed-wet rocks: Pore-scale comparison between experiment and simulation. Transport in Porous Media, 2019, 127: 393-414.
Alqahtani, N., Alzubaidi, F., Armstrong, R. T., et al. Machine learning for predicting properties of porous media from 2d X-ray images. Journal of Petroleum Science and Engineering, 2020, 184: 106514.
Alqahtani, N., Armstrong, R. T., Mostaghimi, P. Deep learning convolutional neural networks to predict porous media properties. Paper SPE 191906 Presented at SPE Asia Pacific Oil and Gas Conference and Exhibition, Brisbane, Australia, 23-25 October, 2018.
Andhumoudine, A. B., Nie, X., Zhou, Q., et al. Investigation of coal elastic properties based on digital core technology and finite element method. Advances in Geo-Energy Research, 2021, 5: 53-63.
Andrä, H., Combaret, N., Dvorkin, J., et al. Digital rock physics benchmarks-Part I: Imaging and segmentation. Computers and Geosciences, 2013, 50: 25-32.
Arjovsky, M., Chintala, S., Bottou, L. Wasserstein generative adversarial networks. Paper Presented at Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 6-11 Augest, 2017.
Badrinarayanan, V., Kendall, A., Cipolla, R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39: 2481-2495.
Blunt, M. J., Bijeljic, B., Dong, H., et al. Pore-scale imaging and modelling. Advances in Water Resources, 2013, 51: 197-216.
Brock, A., Donahue, J., Simonyan, K. Large scale GAN training for high fidelity natural image synthesis. ArXiv preprint, 2018, 1809: 11096.
Cai, J., Xu, K., Zhu, Y., et al. Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Applied Energy, 2020, 262: 114566.
Cang, R., Li, H., Yao, H., et al. Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model. Computational Materials Science, 2018, 150: 212-221.
Cao, D., Hou, Z., Liu, Q., et al. Reconstruction of three-dimension digital rock guided by prior information with a combination of InfoGAN and style-based GAN. Journal of Petroleum Science and Engineering, 2022, 208: 109590.
Chen, H., He, X., Teng, Q., et al. Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks. Physical Review E, 2020a, 101: 023305.
Chen, Z., Liu, X., Yang, J., et al. Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin. Computers and Geosciences, 2020b, 138: 104450.
Chen, L. C., Papandreou, G., Kokkinos, I., et al. Semantic image segmentation with deep convolutional nets and fully connected crfs. ArXiv preprint, 2014, 1412: 7062.
Cireşan, D. C., Giusti, A., Gambardella, L. M., et al. Deep neural networks segment neuronal membranes in electron microscopy images. Paper Presented at Advances in Neural Information Processing Systems, Lake Tahoe, U.S.A, 3-8 December, 2012.
Coenen, J., Tchouparova, E., Jing, X., et al. Measurement parameters and resolution aspects of micro X-ray tomography for advanced core analysis. Paper Presented at proceedings of International Symposium of the Society of Core Analysts, Abu Dhabi, The United Arab Emirates, 5-9 October, 2004.
Dong, C., Loy, C. C., He, K., et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38: 295-307.
Farabet, C., Couprie, C., Najman, L., et al. Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35: 1915-1929.
Feng, J., Teng, Q., Li, B., et al. An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning. Computer Methods in Applied Mechanics and Engineering, 2020, 368: 113043.
Flannery, B. P., Deckman, H. W., Roberge, W. G., et al. Three-dimensional X-ray microtomography. Science, 1987, 237: 1439-1444.
Ganin, Y., Lempitsky, V. N4-fields: Neural network nearest neighbor fields for image transforms. Paper Presented at 12nd Asian Conference on Computer Vision, Singapore, 1-5 November, 2014.
Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., et al. A review on deep learning techniques applied to semantic segmentation. ArXiv preprint, 2017, 1704: 06857.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., et al. Generative adversarial nets. Paper Presented at Advances in Neural Information Processing Systems, Montreal, Canada, 8-13 December, 2014.
Gulrajani, I., Ahmed, F., Arjovsky, M., et al. Improved training of wasserstein gans. Paper Presented at Advances in Neural Information Processing Systems, Long Beach, U.S.A, 4-9 December, 2017.
Gupta, S., Girshick, R., Arbeláez, P., et al. Learning rich features from RGB-D images for object detection and segmentation. Paper Presented at 13rd European Conference on Computer Vision (ECCV), Zurich, Switzerland, 6-12 September, 2014.
Haagsma, A., Scharenberg, M., Keister, L., et al. Secondary porosity prediction in complex carbonate reefs using 3D CT scan image analysis and machine learning. Journal of Petroleum Science and Engineering, 2021, 207: 109087.
Hu, M. F., Zuo, X., Liu, J. W. Survey on deep generative model. Acta Automatica Sinica, 2022, 48: 40-74. (in Chinese)
Ishola, O., Vilcáez, J. Machine learning modeling of permeability in 3D heterogeneous porous media using a novel stochastic pore-scale simulation approach. Fuel, 2022, 321: 124044.
Kamrava, S., Tahmasebi, P., Sahimi, M. Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm. Neural Networks, 2019, 118: 310-320.
Karimpouli, S., Tahmasebi, P. Segmentation of digital rock images using deep convolutional autoencoder networks. Computers and Geosciences, 2019a, 126: 142-150.
Karimpouli, S., Tahmasebi, P. Image-based velocity estimation of rock using convolutional neural networks. Neural Networks, 2019b, 111: 89-97.
Kendall, A., Badrinarayanan, V., Cipolla, R. Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. ArXiv preprint, 2015, 1511: 02680.
Ledig, C., Theis, L., Huszár, F., et al. Photo-realistic single image super-resolution using a generative adversarial network. Paper Presented at Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, U.S.A, 21-26 July, 2017.
Li, B., Nie, X., Cai, J., et al. U-Net model for multicomponent digital rock modeling of shales based on CT and QEMSCAN images. Journal of Petroleum Science and Engineering, 2022, 216: 110734.
Li, Z., Teng, Q., He, X., et al. Sparse representation-based volumetric super-resolution algorithm for 3D CT images of reservoir rocks. Journal of Applied Geophysics, 2017, 144: 69-77.
Li, C., Wang, D., Kong, L. Application of machine learning techniques in mineral classification for scanning electron microscopy-energy dispersive x-ray spectroscopy (SEMEDS) images. Journal of Petroleum Science and Engineering, 2021, 200: 108178.
Lin, C., Wu, Y., Ren, L., et al. Review of digital core modeling methods. Progress in Geophysics, 2018, 33: 679-689. (in Chinese)
Liu, M., Shabaninejad, M., Mostaghimi, P. Predictions of permeability, surface area and average dissolution rate during reactive transport in multi-mineral rocks. Journal of Petroleum Science and Engineering, 2018, 170: 130-138.
Liu, C., Zhang, L., Li, Y., et al. Effects of microfractures on permeability in carbonate rocks based on digital core technology. Advances in Geo-Energy Research, 2022, 6: 86-90.
Long, J., Shelhamer, E., Darrell, T. Fully convolutional networks for semantic segmentation. Paper Presented at Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, U.S.A, 8-10 June, 2015.
Mao, X., Li, Q., Xie, H., et al. Least squares generative adversarial networks. Paper Presented at Proceedings of the IEEE International Conference on Computer Vision, Venice (ICCV), Italy, 22-29 October, 2017.
Misbahuddin, M. Estimating petrophysical properties of shale rock using conventional neural networks CNN. Paper SPE 204272 Presented at SPE Annual Technical Conference and Exhibition, Virtual, 26-29 October, 2020.
Mosser, L., Dubrule, O., Blunt, M. J. Reconstruction of three-dimensional porous media using generative adversarial neural networks. Physical Review E, 2017, 96: 043309.
Mosser, L., Dubrule, O., Blunt, M. J. Stochastic reconstruction of an Oolitic limestone by generative adversarial networks. Transport in Porous Media, 2018, 125: 81-103.
Nanfack, G., Elhassouny, A., Thami, R. O. H. Squeeze- SegNet: A new fast deep convolutional neural network for semantic segmentation. Paper SPIE 10696 Presented at Tenth International Conference on Machine Vision (ICMV 2017), Vienna, Austria, 13-15 November, 2017.
Nie, X., Zou, C., Li, Z., et al. Numerical simulation of the electrical properties of shale gas reservoir rock based on digital core. Journal of Geophysics and Engineering, 2016a, 13: 481-490.
Nie, X., Zou, C. C., Meng, X. H., et al. 3D digital core modeling of shale gas reservoir rocks: A case study of conductivity model. Natural Gas Geoscience, 2016b, 27: 706-715. (in Chinese)
Ning, F., Delhomme, D., LeCun, Y., et al. Toward automatic phenotyping of developing embryos from videos. IEEE Transactions on Image Processing, 2005, 14: 1360-1371.
Okabe, H., Blunt, M. J. Prediction of permeability for porous media reconstructed using multiple-point statistics. Physical Review E, 2004, 70: 066135.
Pinheiro, P. O., Collobert, R. Recurrent convolutional neural networks for scene labeling. Paper Presented at Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 21-26 June, 2014.
Rabbani, A., Babaei, M., Shams, R., et al. DeePore: A deep learning workflow for rapid and comprehensive characterization of porous materials. Advances in Water Resources, 2020, 146: 103787.
Radford, A., Metz, L., Chintala, S. Unsupervised representation learning with deep convolutional generative adversarial networks. ArXiv preprint, 2015, 1511: 06434.
Ronneberger, O., Fischer, P., Brox, T. U-net: Convolutional networks for biomedical image segmentation. Paper Presented at International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5-9 October, 2015.
Seo, H., Badiei Khuzani, M., Vasudevan, V., et al. Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications. Medical Physics, 2020, 47: e148-e167.
Shan, L., Bai, X., Liu, C., et al. Super-resolution reconstruction of digital rock CT images based on residual attention mechanism. Advances in Geo-Energy Research, 2022, 6: 157-168.
Song, R., Liu, J., Li, G. Researches on the pore permeability of core sample based on 3D micro-CT images and pore-scale structured element models. Journal of Southwest Petroleum University (Science & Technology Edition), 2015, 37: 138-145. (in Chinese)
Srisutthiyakorn, N. Deep learning methods for predicting permeability from 2-D/3-D binary segmented images. SEG Technical Program Expanded Abstracts, 2016, 35: 3042-3046.
Tembely, M., AlSumaiti, A. M., Alameri, W. S. Machine and deep learning for estimating the permeability of complex carbonate rock from X-ray micro-computed tomography. Energy Reports, 2021, 7: 1460-1472.
Valsecchi, A., Damas, S., Tubilleja, C., et al. Stochastic reconstruction of 3D porous media from 2D images using generative adversarial networks. Neurocomputing, 2020, 399: 227-236.
Wang, Y. D., Armstrong, R. T., Mostaghimi, P. Enhancing resolution of digital rock images with super resolution convolutional neural networks. Journal of Petroleum Science and Engineering, 2019a, 182: 106261.
Wang, J., Cao, J., You, J., et al. Prediction of reservoir porosity, permeability, and saturation based on a gated recurrent unit neural network. Geophysical Prospecting for Petroleum, 2020, 59(4): 616-627. (in Chinese)
Wang, H., Dalton, L., Fan, M., et al. Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM. Journal of Petroleum Science and Engineering, 2022, 215: 110596.
Wang, Y., Jiang, H., Yu, F., et al. Researches on the pore permeability prediction method of 3D digital cores based on machine learning. Petroleum Science Bulletin, 2019b, 4: 354-363. (in Chinese)
Wang, S., Qin, C., Feng, Q., et al. A framework for predicting the production performance of unconventional resources using deep learning. Applied Energy, 2021a, 295: 117016.
Wang, Y. D., Shabaninejad, M., Armstrong, R. T., et al. Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images. Applied Soft Computing, 2021b, 104: 107185.
Wang, S., Yao, X., Feng, Q., et al. Molecular insights into carbon dioxide enhanced multi-component shale gas recovery and its sequestration in realistic kerogen. Chemical Engineering Journal, 2021c, 425: 130292.
Wang, C. C., Yao, J., Yang, Y. F., et al. Structure characteristics analysis of carbonate dual pore digital rock. Journal of China University of Petroleum (Edition of Natural Science), 2013, 37: 71-74. (in Chinese)
Wu, K., Van Dijke, M. I. J., Couples, G. D., et al. 3D stochastic modelling of heterogeneous porous media-applications to reservoir rocks. Transport in Porous Media, 2006, 65: 443-467.
Xia, Y., Cai, J., Perfect, E., et al. Fractal dimension, lacunarity and succolarity analyses on CT images of reservoir rocks for permeability prediction. Journal of Hydrology, 2019, 579: 124198.
Xiao, L. The fusion of data-driven machine learning with mechanism models and interpretability issues. Geophysical Prospecting for Petroleum, 2022, 61: 205-212. (in Chinese)
Yang, Y. Application progress of big data & AI technologies in exploration and development of Shengli Oilfield. Petroleum Geology and Recovery Efficiency, 2022, 29(1): 1-10. (in Chinese)
Yang, Y., Liu, F., Yao, J., et al. Reconstruction of 3D shale digital rock based on generative adversarial network. Journal of Southwest Petroleum University (Science & Technology Edition), 2021, 43: 73-83. (in Chinese)
Yang, J., Wright, J., Huang, T., et al. Image super-resolution as sparse representation of raw image patches. Paper Presented at 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, U.S.A, 23-28 June, 2008.
Yang, Z., Yabansu, Y. C., Al-Bahrani, R., et al. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Computational Materials Science, 2018, 151: 278-287.
Yang, Y., Yao, J., Wang, C., et al. New pore space characterization method of shale matrix formation by considering organic and inorganic pores. Journal of Natural Gas Science and Engineering, 2015, 27: 496-503.
Yao, J., Sun, H., Li, A., et al. Modern system of multiphase flow in porous media and its development trend. Chinese Science Bulletin, 2018, 63: 425-451. (in Chinese)
Yao, J., Wang, C., Yang, Y., et al. The construction of carbonate digital rock with hybrid superposition method. Journal of Petroleum Science and Engineering, 2013, 110: 263-267.
You, N., Li, Y. E., Cheng, A. 3D carbonate digital rock reconstruction using progressive growing GAN. Journal of Geophysical Research: Solid Earth, 2021, 126: e2021JB021687.
Zha, W., Li, X., Xing, Y., et al. Reconstruction of shale image based on wasserstein generative adversarial networks with gradient penalty. Advances in Geo-Energy Research, 2020, 4: 107-114.
Zhang, W., Li, H., Li, Y., et al. Application of deep learning algorithms in geotechnical engineering: a short critical review. Artificial Intelligence Review, 2021a, 54: 5633-5673.
Zhang, T., Li, D., Lu, F. A pore space reconstruction method of shale based on autoencoders and generative adversarial networks. Computational Geosciences, 2021b, 25: 2149-2165.
Zhang, H. L., Yu, H., Yuan, X. H., et al. Permeability prediction of low-resolution porous media images using autoencoder-based convolutional neural network. Journal of Petroleum Science and Engineering, 2022, 208: 109589.
Zhao, J., Chen, H., Li, N., et al. Research advance of petrophysical application based on digital core technology. Progress in Geophysics, 2020, 35: 1099-1108. (in Chinese)
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., et al. Unet++: A nested u-net architecture for medical image segmentation. Paper Presented at Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain, 20 September, 2018.
Zhu, J. Y., Park, T., Isola, P., et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. Paper Presented at Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22-29 October 2017a.
Zhu, W., Shan, R. Digital core based transmitted ultrasonic wave simulation and velocity accuracy analysis. Applied Geophysics, 2016, 13: 375-381.
Zhu, Y., Tao, G., Fang, W. Lattice boltzmann simulation of permeability in 3D porous medium. Well Logging Technology, 2008, 32: 25-28. (in Chinese)
Zhu, J. Y., Zhang, R., Pathak, D., et al. Toward multimodal image-to-image translation. Paper Presented at 31st Annual Conference on Neural Information Processing Systems, NIPS 2017, Long Beach, U.S.A, 4-9 December, 2017b.
Zhu, L., Zhang, C., Zhang, C., et al. Challenges and prospects of digital core-reconstruction research. Geofluids, 2019, 2019: 7814180.
Zhu, L., Zheng, Y. Applications of self-attention SRGAN in super resolution reconstruction of rock CT image. Journal of Xi’an Shiyou University (Natural Science Edition), 2022, 37: 131-137. (in Chinese)
DOI: https://doi.org/10.46690/ager.2023.04.02
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