Study of Chemical Sand Consolidation Transition from Polymers to Nanoparticles

Authors

  • I.J. Karimov Azerbaijan State Oil and Industry University

DOI:

https://doi.org/10.52171/herald.393

Keywords:

chemical sand consolidation, polymer resins, nanoparticles, permeability retention, uniaxial compressive strength, wellbore stability

Abstract

The article presents a study on chemical sand consolidation to address sand production issues in hydrocarbon extraction. The study aims to transform sand grains in formations with low geomechanical stability into a strong yet permeable matrix, thereby protecting equipment from erosion and maintaining well productivity. Both conventional polymer resins (epoxy, furan, phenol-formaldehyde) and nanoparticle-based systems were used in the study, and their mechanical strength, permeability loss, and thermal durability (up to 195°C) were comparatively evaluated. Results indicate that nanoparticles preserve reservoir connectivity and limit permeability loss to approximately 11.8%, whereas polymer resins provide high uniaxial compressive strength but reduce permeability by about 52.5%. The study also analyzes the applicability of these methods under various reservoir conditions, including deepwater and mature fields.

References

1. Tran H.D., Le N., Nguyen V.H. Customer churn prediction in the banking sector using machine learning-based classification models. Interdisciplinary Journal of Information, Knowledge & Management, 18, 87–105, 2023. https://doi.org/10.28945/5086

2. Ashraf R. Bank customer churn prediction using machine learning framework. Journal of Applied Finance & Banking, 14(4), 1–5, 2024.

3. Badalova A.N., Guliyeva S.H. Application of Machine Learning Methods for Classification of Agricultural Crops. Herald of the Azerbaijan Engineering Academy, 14(2), 106–116, 2022. https://doi.org/10.52171/2076-0515_2022_14_02_106_116

4. Imani M., Joudaki M., Beikmohammadi A., Arabnia H.R. Customer churn prediction: A systematic review of recent advances, trends, and challenges in machine learning and deep learning. Machine Learning and Knowledge Extraction, 7(3), 105, 2025.

https://doi.org/10.3390/make7030105

5. Xu X., Kou G., Ergu D. Profit-based uncertainty estimation with application to credit scoring. European Journal of Operational Research, 325(2), 303–316, 2025. https://doi.org/10.1016/j.ejor.2025.03.007

6. Li Y., Yan K. Prediction of bank credit customers churn based on machine learning and interpretability analysis. Data Science in Finance and Economics, 5(1), 19–34, 2025.

https://doi.org/10.3934/DSFE.2025002

7. Peng K., Peng Y., Li W. Research on customer churn prediction and model interpretability analysis. PLoS ONE, 18(12), e0289724, 2023.

https://doi.org/10.1371/journal.pone.0289724

8. Breiman, L. Random forests. Machine Learning, 45(1), 5–32, 2001. https://doi.org/10.1023/A:1010933404324

9. Chen, T., & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794, 2016. https://doi.org/10.1145/2939672.2939785

10. Gal, Y., & Ghahramani, Z. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In Proceedings of the 33rd International Conference on Machine Learning (ICML), 1050–1059, 2016

11. Fawcett, T. An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874, 2006. DOI: https://doi.org/10.1016/j.patrec.2005.10.010

12. Powers, D.M.W. Evaluation: From precision, recall and F-measure to ROC. Journal of Machine Learning Technologies, 2(1), 37–63, 2011

13. Brier, G.W. Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1–3, 1950

14. Guo, C., Pleiss, G., Sun, Y., & Weinberger, K.Q. On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning (ICML), 1321–1330, 2017

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Published

2026-01-29

How to Cite

Karimov, I. (2026). Study of Chemical Sand Consolidation Transition from Polymers to Nanoparticles. Herald of Azerbaijan Engineering Academy, 17(4), 1–9. https://doi.org/10.52171/herald.393

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