Applying AI-assisted image processing to detect and measure CO2 microbubbles for carbon storage

• Autorzy: Le Dang Nguyen Hai , Le Nam Nguyen Hai

Identyfikatory

DOI

10.29227/IM-2025-01-02-060

• Konferencja: CESD 2024 : Conference on Earth Sciences : November 11th, 2024, Ho Chi Minh City, Vietnam
• Języki publikacji: EN

Abstrakty

EN

To address the challenges in the CO2 injection process, CO2 microbubble dispersion has been proposed as an alternative to traditional methods, such as miscible injection and water-alternating-gas (WAG) injection. This study presents an AI-assisted model for detecting CO2 microbubbles, powered by the YOLOv8 algorithm, renowned for its high-accuracy predictions. Conventional image processing techniques often struggle with detecting microbubbles, particularly in cases involving overlapping bubbles, variations in size, and low-contrast images, which can lead to inaccuracies in bubble identification and measurement. In contrast, YOLOv8’s advanced detection capabilities offer a more robust solution by precisely localizing and classifying microbubbles, even in challenging scenarios. The model’s performance was rigorously evaluated, demonstrating its effectiveness as a valuable tool for microbubble analysis. The detection images processed using YOLOv8 illustrate its ability to accurately detect and classify bubbles of varying sizes, generating precise bounding boxes around each identified bubble. This combination of data visualization and advanced detection techniques underscores the efficacy of YOLOv8 in microbubble analysis, enabling accurate measurement and detailed characterization of bubble size distributions—an essential factor in optimizing chemical engineering processes.

Słowa kluczowe

EN

𝐶𝑂2 microbubble; computer vision; object detection; keywords

PL

𝐶𝑂2 mikropęcherzyki; widzenie komputerowe; wykrywanie obiektów; słowa kluczowe

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2025
• Tom: Vol. 2, no. 1
• Strony: art. no. 60
• Opis fizyczny: Bibliogr. 21 poz., rys., wykr., zdj.

Twórcy

autor

Le Dang Nguyen Hai

• Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet, Dien Hong Ward, Ho Chi Minh City, Vietnam
• Vietnam National University Ho Chi Minh City, Linh Xuan Ward, Ho Chi Minh City, Vietnam

autor

Le Nam Nguyen Hai

• Faculty of Geology and Petroleum Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet, Dien Hong Ward, Ho Chi Minh City, Vietnam
• Vietnam National University Ho Chi Minh City, Linh Xuan Ward, Ho Chi Minh City, Vietnam

Bibliografia

• 1. Akai, T., Xue, Z., Yamashita, Y., and Yoshizawa, M. “Application of CO2 micro bubble for the innovative CO2-EOR.” Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIPEC 2015, No. November, (2015).
• 2. Le, N. N. H., Sugai, Y., Vo-Thanh, H., Nguele, R., Ssebadduka, R., and Wei, N. “Experimental investigation on plugging performance of CO2 microbubbles in porous media.” Journal of Petroleum Science and Engineering, Vol. 211, No. November 2021, (2022), 110187. https://doi.org/10.1016/j.petrol.2022.110187
• 3. Nguyen Hai Le, N., Sugai, Y., Nguele, R., and Sreu, T. “Bubble size distribution and stability of CO2 microbubbles for enhanced oil recovery: effect of polymer, surfactant and salt concentrations.” Journal of Dispersion Science and Technology, (2021). https://doi.org/10.1080/01932691.2021.1974873
• 4. Nguyen Hai Le, N. N. H., Sugai, Y., and Sasaki, K. “Investigation of Stability of CO2 Microbubbles—Colloidal Gas Aphrons for Enhanced Oil Recovery Using Definitive Screening Design.” Colloids and Interfaces, Vol. 4, No. 2, (2020), 26. https://doi.org/10.3390/colloids4020026
• Telmadarreie, A., Doda, A., Trivedi, J. J., Kuru, E., and Choi, P. “CO2 microbubbles - A potential fluid for enhanced oil recovery: Bulk and porous media studies.” Journal of Petroleum Science and Engineering, Vol. 138, , (2016), 160–173. https://doi.org/10.1016/j.petrol.2015.10.035
• 6. Ruan, J., Zhou, H., Ding, Z., Zhang, Y., Zhao, L., Zhang, J., and Tang, Z. “Machine learning-aided characterization of microbubbles for venturi bubble generator.” Chemical Engineering Journal, Vol. 465, No. March, (2023), 142763. https://doi.org/10.1016/j.cej.2023.142763
• 7. Jin, S., Ye, G., Guo, Y., Zhao, Z., Lu, L., and Liu, Z. “A real-time monitoring and measurement method for microbubble morphology based on image processing technology.” Microchemical Journal, Vol. 203, No. May, (2024), 110881. https://doi.org/10.1016/j.microc.2024.110881
• 8. Dey, A., and Biswas, S. “Shot-ViT: Cricket Batting Shots Classification with Vision Transformer Network.” International Journal of Engineering Transactions C: Aspects, Vol. 37, No. 12, (2024), 2463–2472. https://doi.org/10.5829/ije.2024.37.12c.04
• 9. Varghese, R., and M., S. “YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness.” In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) (pp. 1–6). https://doi.org/10.1109/ADICS58448.2024.10533619
• 10. Rami, V., and Ch, R. “A Review on YOLOv8 and Its Advancements A Review on YOLOv8 and its Advancements,” No. January, (2024). https://doi.org/10.1007/978-981-99-7962-2
• 11. Reis, D., Kupec, J., Hong, J., and Daoudi, A. “Real-Time Flying Object Detection with YOLOv8,” (2023). Retrieved from http://arxiv.org/abs/2305.09972
• 12. Chen, Y., Xu, H., Chang, P., Huang, Y., Zhong, F., Jia, Q., Chen, L., Zhong, H., and Liu, S. “CESYOLOv8 : Strawberry Maturity Detection Based on the Improved YOLOv8,” (2024).
• 13. Amaral, D. De, Diego, E., Freitas, G. De, Abud, H. F., and Gomes, D. G. “Applying YOLOv8 and Xray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv . Xaraés Seeds,” (2024), 869– 880.
• 14. He, K., Zhang, X., Ren, S., and Sun, J. “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,” (n.d.), 1–14.
• 15. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. “Feature Pyramid Networks for Object Detection.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 936–944). https://doi.org/10.1109/CVPR.2017.106
• 16. Terven, J., Córdova-Esparza, D. M., and Romero-González, J. A. “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS.” Machine Learning and Knowledge Extraction, Vol. 5, No. 4, (2023), 1680–1716. https://doi.org/10.3390/make5040083
• 17. Sun, X., Liu, T., Yu, X., and Pang, B. “Unmanned Surface Vessel Visual Object Detection Under AllWeather Conditions with Optimized Feature Fusion Network in YOLOv4.” Journal of Intelligent and Robotic Systems: Theory and Applications, Vol. 103, No. 3, (2021). https://doi.org/10.1007/s10846-021-01499-8
• 18. Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., and Tang, J. “Generalized Focal Loss : Learning Qualified and Distributed Bounding Boxes for Dense Object Detection,” No. NeurIPS, (2020), 1–11.
• 19. Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. “UnitBox: An Advanced Object Detection Network.” In Proceedings of the 24th ACM International Conference on Multimedia (pp. 516–520). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2964284.2967274
• 20. Girshick, R., Donahue, J., Darrell, T., and Malik, J. “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.” In 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587). https://doi.org/10.1109/CVPR.2014.81
• 21. D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” CoRR, vol. abs/1412.6980, 2014, [Online]. Available: https://api.semanticscholar.org/CorpusID:6628106

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2026).