MicroGraphNets: Automated characterization of the micro-scale wettability of porous media using graph neural networks
Abstract view|63|times PDF download|23|times
Abstract
This study introduces MicroGraphNets, a deep learning framework for automating the microscopic characterization of wettability in porous media using graph neural networks. The framework predicts rock surface roughness, fluid/fluid interfacial curvatures, and contact angles at 3-phase contact lines from segmented multiphase micro-computed tomography images. This is achieved by converting these images into sets of surface and interfacial points, with their intersection defining the 3-phase contact line points. Specialized geometrical training graphs are constructed from these points to predict each property, leveraging surface and interfacial normal vectors as input features for constructing surface and interfacial graphs. To address the unique challenge that arises from the coexistence of all phases around 3-phase contact lines, distinct node types assigned to each phase were embedded as node features for constructing contact angle graphs. To predict the properties, the framework employs a message-passing graph neural network with three modules: an encoder for initial feature embeddings, a processor for aggregating neighboring embeddings and propagating messages, and a decoder for final property prediction. This approach effectively captures node and edge relationships, facilitating accurate regression of surface and interfacial properties. Validation includes testing on unseen samples and a synthetic droplet test against analytical solutions. Time-resolved analysis was performed to demonstrate the scalability and efficiency of the framework on large datasets. MicroGraphNets demonstrates superior accuracy and efficiency compared to traditional deep learning methods, showcasing its potential for predicting microscopic surface and interfacial properties of porous media.
Document Type: Original article
Cite as: Alzahrani, M. K., Shapoval, A., Chen, Z., Rahman, S. S. MicroGraphNets: Automated characterization of the micro-scale wettability of porous media using graph neural networks. Capillarity, 2024, 12(3): 57-71. https://doi.org/10.46690/capi.2024.09.01
Keywords
Full Text:
PDFReferences
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. J., Chung, T., Wang, Y. D., et al. Flow-based characterization of digital rock images using deep learning. SPE Journal, 2021, 26(4): 1800-1811.
Alqahtani, N. J., Niu, Y., Wang, Y. D., et al. Super-resolved segmentation of X-ray images of carbonate rocks using deep learning. Transport in Porous Media, 2022, 143(2): 497-525.
AlRatrout, A., Blunt, M. J., Bijeljic, B. Wettability in complex porous materials, the mixed-wet state, and its relationship to surface roughness. Proceedings of the National Academy of Sciences, 2018, 115(36): 8901-8906.
AlRatrout, A., Raeini, A. Q., Bijeljic, B., et al. Automatic measurement of contact angle in pore-space images. Advances in Water Resources, 2017, 109: 158-169.
Alzahrani, M. K., Shapoval, A., Chen, Z., et al. Pore-GNN: A graph neural network-based framework for predicting flow properties of porous media from micro-CT images. Advances in Geo-Energy Research, 2023, 10(1): 39-55.
Amari, S. I. Backpropagation and stochastic gradient descent method. Neurocomputing, 1993, 5(4-5): 185-196.
Armstrong, R. T., Sun, C., Mostaghimi, P., et al. Multiscale characterization of wettability in porous media. Transport in Porous Media, 2021, 140(1): 215-240.
Ar Rushood, I., Alqahtani, N., Wang, Y. D., et al. Segmentation of X-Ray images of rocks using deep learning. Paper SPE 201282 Presented at the SPE Annual Technical Conference and Exhibition, Virtual, 26-29 October, 2020.
Balaban, S. Deep learning and face recognition: The state of the art. Biometric and Surveillance Technology for Human and Activity Identification XII, 2015, 9457: 68- 75.
Boampong, L. O., Rafati, R., Haddad, A. S. Analysis of wettability alteration in low salinity water flooding using a zeta potential-based model. Capillarity, 2023, 7(2): 32- 40.
Cai, C., Vlassis, N., Magee, L., et al. Equivariant geometric learning for digital rock physics: Estimating formation factor and effective permeability tensors from Morse graph. International Journal for Multiscale Computational Engineering, 2023, 21(5): 1-24.
Cao, Y., Tang, M., Zhang, Q., et al. Dynamic capillary pressure analysis of tight sandstone based on digital rock model. Capillarity, 2020, 3(2): 28-35.
Charles, R. Q., Su, H., Kaichun, M., et al. PointNet: Deep learning on point sets for 3D classification and segmentation. Paper Presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 21-26 July, 2017.
Deng, L., Liu, Y. Deep Learning in Natural Language Pro cessing. Singapore, Springer, 2018.
Chung, J., Ahmad, R., Sun, W., et al. Prediction of effective elastic moduli of rocks using graph neural networks. Computer Methods in Applied Mechanics and Engineering, 2024, 421: 116780.
Diao, Z., Li, S., Liu, W., et al. Numerical study of the effect of tortuosity and mixed wettability on spontaneous imbibition in heterogeneous porous media. Capillarity, 2021, 4(3): 50-62.
Ding, H., Wang, Y., Shapoval, A., et al. Macro-and microscopic studies of “smart water” flooding in carbonate rocks: An image-based wettability examination. Energy & Fuels, 2019, 33(8): 6961-6970.
Fey, M., Lenssen, J. E. Fast graph representation learning with PyTorch geometric. ArXiv Preprint ArXiv: 1903.02428, 2019.
Garfi, G., John, C. M., Berg, S., et al. The sensitivity of estimates of multiphase fluid and solid properties of porous rocks to image processing. Transport in Porous Media, 2020, 131(3): 985-1005.
Geistlinger, H., Golmohammadi, S., Zulfiqar, B., et al. The interplay between pore-scale heterogeneity, surface roughness, and wettability controls trapping in two-phase fluid displacement in porous media. Geophysical Research Letters, 2024, 51(1): e2023GL106197.
Goldberger, J., Hinton, G. E., Roweis, S., et al. Neighbourhood components analysis. Paper Presented at Advances in Neural Information Processing Systems, Vancouver, British Columbia, 13-18 December, 2004.
Haines, W. B. Studies in the physical properties of soil. V. The hysteresis effect in capillary properties, and the modes of moisture distribution associated therewith. The Journal of Agricultural Science, 1930, 20(1): 97-116.
Iglauer, S., Pentland, C. H., Busch, A. CO2 wettability of seal and reservoir rocks and the implications for carbon geo-sequestration. Water Resource Research, 2015, 51(1): 729-774.
Kashefi, A., Mukerji, T. Point-cloud deep learning of porous media for permeability prediction. Physics of Fluids, 2021, 33: 097109.
Kingma, D. P., Ba, J. Adam: A method for stochastic optimization. ArXiv Preprint ArXiv: 1412.6980, 2017.
Klise, K. A., Moriarty, D., Yoon, H., et al. Automated contact angle estimation for three-dimensional X-ray microto-mography data. Advances in Water Resources, 2016, 95: 152-160.
Lourenc¸o, S. D. N., Saulick, Y., Zheng, S., et al. Soil wettability in ground engineering: Fundamentals, methods, and applications. Acta Geotechnica, 2018, 13: 1-14.
Meyer, F., Beucher, S. Morphological segmentation. Journal of Visual Communication and Image Representation, 1990, 1: 21-46.
Otter, D. W., Medina, J. R., Kalita, J. K. A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(2): 604-624.
Paszke, A., Gross, S., Massa, F., et al. PyTorch: An imperative style, high-performance deep learning library. ArXiv Preprint ArXiv: 1912.01703, 2019.
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.
Rabbani, A., Sun, C., Babaei, M., et al. DeepAngle: Fast calculation of contact angles in tomography images using deep learning. Geoenergy Science and Engineering, 2023, 227: 211807.
Rücker, M., Bartels, W. B., Singh, K., et al. The effect of mixed wettability on pore-scale flow regimes based on a flooding experiment in ketton limestone. Geophysical Research Letters, 2019, 46(6): 3225-3234.
Santos, J. E., Xu, D., Jo, H., et al. PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media. Advances in Water Resources, 2020, 138: 103539.
Shapoval, A., Alzahrani, M., Xue, W., et al. Oil-water interactions in porous media during fluid displacement: Effect of potential determining ions (PDI) on the formation of in-situ emulsions and oil recovery. Journal of Petroleum Science and Engineering, 2022, 210: 110079.
Shapoval, A., Zhuravljov, A., Lanetc, Z., et al. Pore-scale evaluation of physicochemical interactions by engineered water injections. Transport in Porous Media, 2023, 148(3): 605-625.
Singh, N. S., Hariharan, S., Gupta, M. Facial recognition using deep learning. Paper Presented at International Conference on Data Sciences, Security and Applications, New Delhi, India, 7-8 March, 2019.
Sun, C., McClure, J. E., Mostaghimi, P., et al. Probing effective wetting in subsurface systems. Geophysical Research Letters, 2020, 47(5): e2019GL086151.
Wang, W., Gang, J. Application of convolutional neural network in natural language processing. Paper Presented at 2018 International Conference on Information Systems and Computer Aided Education (ICISCAE), Changchun, Jilin, 6-8 July, 2018.
Wang, Y., Arns, C. H., Rahman, S. S., et al. Porous structure reconstruction using convolutional neural networks. Mathematical Geosciences, 2018, 50(7): 781-799.
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, 2021, 104: 107185.
Yu, Z., Wang, T., Guo, T., et al. Robust point cloud normal estimation via neighborhood reconstruction. Advances in Mechanical Engineering, 2019, 11(4): 1687814019836043.
Yun, W., Liu, Y., Kovscek, A. R. Deep learning for automated characterization of pore-scale wettability. Advances in Water Resources, 2020, 144: 103708.
Zou, S., Sun, C. X-ray microcomputed imaging of wettability characterization for multiphase flow in porous media: A review. Capillarity, 2020, 3(3): 36-44.
Zulfiqar, B., Vogel, H., Ding, Y., et al. The impact of wettability and surface roughness on fluid displacement and capillary trapping in 2-D and 3-D porous media: 2. Combined effect of wettability, surface roughness, and pore space structure on trapping efficiency in sand packs and micromodels. Water Resources Research, 2020, 56(10): e2020WR027965.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.