Open Access Peer-reviewed Research Article

Smart Polymeric Liners for Internal Corrosion Control in Wet Sour Gas Pipelines: Integrating Nanostructured Materials and AI-Driven Predictive Modelling

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Submitted   Jan 7, 2026
Published   Apr 29, 2026
Issue    Vol 5 No 1 (2026)
DOI   10.25082/RIMA.2026.01.005

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Mavis Sika Okyere corresponding author
Department of Pipelines and Stations, Ghana National Gas Limited Company, Accra, Ghana

Abstract

Sour gas pipelines suffer severe integrity challenges due to internal corrosion driven by hydrogen sulfide (H₂S) and carbon dioxide (CO₂). This study introduces a novel smart polymeric liner enhanced with nanostructured graphene and nanosilica fillers, integrated into a CFD→AI→digital twin workflow for predictive corrosion control. Unlike conventional inhibitors and coatings, the liner combines nanostructural impermeability, mechanical reinforcement, and tailored interfacial adhesion strategies to suppress corrosion in API 5L X65 steel pipelines. CFD simulations demonstrate that bare pipelines exhibit extreme corrosion rates, while lined systems reduce rates to near-negligible levels (< 0.002 mm/year). An AI regression model trained on CFD outputs and experimental data achieves an R² ≈ 0.99, enabling accurate forecasts of corrosion rates and remaining service life. Integration into a digital twin framework allows real-time monitoring, predictive maintenance scheduling, and dynamic risk assessment. This work establishes a next-generation material–digital solution for extending the service life of sour gas pipelines.

Keywords
smart materials, polymeric liners, sour gas corrosion, Hydrogen Sulfide (H₂S), Computational Fluid Dynamics (CFD), Artificial Intelligence (AI), machine learning

Article Details

How to Cite
Okyere, M. S. (2026). Smart Polymeric Liners for Internal Corrosion Control in Wet Sour Gas Pipelines: Integrating Nanostructured Materials and AI-Driven Predictive Modelling. Research on Intelligent Manufacturing and Assembly, 5(1), 361-377. https://doi.org/10.25082/RIMA.2026.01.005
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. de Freitas D S, da Silva C A M, Gonçalves I L M. Mechanisms of corrosion of pipelines. Handbook of Pipeline Engineering. Cham: Springer International Publishing. 2024: 1149-1188. https://doi.org/10.1007/978-3-031-33328-6_29
  2. Skilbred ES, Palencsar S, Dugstad A, et al. Hydrogen uptake during active CO$_2$-H$_2$S corrosion of carbon steel wires in simulated annulus fluid.Corrosion Science. 2022, 199: 110172. https://doi.org/10.1016/j.corsci.2022.110172
  3. Al Madan A, Hussein A, Akhtar SS. A review on internal corrosion of pipelines in the oil and gas industry due to hydrogen sulfide and the role of coatings as a solution. Corrosion Reviews. 2025, 43(2): 189-208. https://doi.org/10.1515/corrrev-2024-0114
  4. Yang H, Wu X, Du S, et al. Synergistic effect of “double pores” texture on tribological properties of AZ31 Mg alloy micro-arc oxide ceramic coatings. Surface and Coatings Technology. 2023, 470: 129875. https://doi.org/10.1016/j.surfcoat.2023.129875
  5. Al-Mosawi AI, Abbas Abdulsada S. Efficacy of polymeric liners in preventing internal corrosion of oil pipelines: A review. Journal of Thermoplastic Composite Materials. 2025, 38(11): 3967-3992. https://doi.org/10.1177/08927057251334471
  6. Khalid HU, Ismail MC, Nosbi N. Permeation Damage of Polymer Liner in Oil and Gas Pipelines: A Review. Polymers. 2020, 12(10): 2307. https://doi.org/10.3390/polym12102307
  7. Chen G, Fu W, Liu Z, et al. Self-sealing polyurethane coatings containing high oil-absorption resin for storage facility and fuel pipelines. Progress in Organic Coatings. 2022, 166: 106789. https://doi.org/10.1016/j.porgcoat.2022.106789
  8. Efremenko Y, Laroussi A, Sengül A, et al. Deposition of Polymers on Titanium Nitride Electrodes. Coatings. 2024, 14(2): 215. https://doi.org/10.3390/coatings14020215
  9. Okyere MS. Assessment of internal corrosion risk in wet sour gas pipeline systems utilizing computational fluid dynamics: a predictive framework for evaluating pipeline and structural integrity, with a case study of the Kashagan 28-inch offshore pipeline. Journal of Engineering and Applied Science. 2025, 72(1). https://doi.org/10.1186/s44147-025-00737-2
  10. Li X, Cao J, Zhang R, et al. Predicting corrosion failure locations in gathering pipelines based on multiphase flow characteristics. Petroleum Science and Technology. 2026: 1-20. https://doi.org/10.1080/10916466.2026.2636673
  11. Wang J, Li Z. Wind speed interval prediction based on multidimensional time series of Convolutional Neural Networks. Engineering Applications of Artificial Intelligence. 2023, 121: 105987. https://doi.org/10.1016/j.engappai.2023.105987
  12. Sun TJ, Bhowmik S. CO$_2$ pipeline integrity management: a digital twin approach. Offshore Technology Conference. OTC. 2023: D011S010R004.
  13. Kausar A. N-Doped Graphene and Polymer Sequent Nanocomposite—Nitty-Gritties and Scoping Insights. Polymer-Plastics Technology and Materials. 2023, 62(11): 1347-1363. https://doi.org/10.1080/25740881.2023.2207112
  14. Zhang J, Kong G, Li S, et al. Graphene-reinforced epoxy powder coating to achieve high performance wear and corrosion resistance. Journal of Materials Research and Technology. 2022, 20: 4148-4160. https://doi.org/10.1016/j.jmrt.2022.08.156
  15. Kulyk B, Freitas MA, Santos NF, et al. A critical review on the production and application of graphene and graphene-based materials in anti-corrosion coatings. Critical Reviews in Solid State and Materials Sciences. 2022, 47(3): 309-355. https://doi.org/10.1080/10408436.2021.1886046
  16. Zhang S, Zhang Y, Wang ZY, et al. Synthesis and characterizations of polystyrene materials with low dielectric constant and low dielectric loss at high frequency. , ed. Journal of Applied Polymer Science. 2023, 140(27). https://doi.org/10.1002/app.54012
  17. Zhao X, Jiang D, Ma L, et al. Corrosion effects and smart coatings of corrosion protection. Coatings. 2022, 12(10): 1378. https://doi.org/10.3390/coatings12101378
  18. Zhang A, Zhu J, Han S, et al. Finely regulated polyamide membranes with rapid water transport for low-pressure precise nanofiltration. , ed. Journal of Membrane Science. 2022, 662: 120987. https://doi.org/10.1016/j.memsci.2022.120987
  19. Hu H, Kang C, Xiong Z, et al. Tunable electronic structure and magnetic characteristics of ZnO monolayer via vacancy defects, and domain/atomic doping. , ed. Materials Today Communications. 2023, 36: 106789. https://doi.org/10.1016/j.mtcomm.2023.106789
  20. Amin KF, Nahin AM, Hoque ME. Polymer nanocomposites for adhesives and coatings. Advanced polymer nanocomposites. Woodhead Publishing. 2022: 235-265. https://doi.org/10.1016/B978-0-12-824492-0.00014-3
  21. Kong L, Li H, Qi G, et al. Stability of Polymer Materials in Reinforced Thermoplastic Pipe after Actual Service in Oil Transportation System. , ed. Journal of Materials Engineering and Performance. 2025, 35(1): 626-636. https://doi.org/10.1007/s11665-025-11423-y
  22. Nomura Y, Fujishiro T, Yoshimura N. Development of Heavy-Gauge Plate for Linepipe with Excellent Sour Resistance and Low-Temperature Toughness. ISOPE International Ocean and Polar Engineering Conference. ISOPE. 2024: ISOPE-I-24-456.
  23. Laycock N. Key Challenges for Internal Corrosion Modeling of Wet Gas Pipelines. Corrosion. 2024, 80(12): 1146-1163. https://doi.org/https://doi.org/10.5006/4532
  24. Liu EB, Huang S, Tian DC, et al. Experimental and numerical simulation study on the erosion behavior of the elbow of gathering pipeline in shale gas field. Petroleum Science. 2024, 21(2): 1257-1274. https://doi.org/10.1016/j.petsci.2023.08.034
  25. Wang X, Yang X, Duan J, et al. A digital twin integrated smart-liner for visualization monitoring of oil and gas pipeline infrastructure. Applied Energy. 2025, 400: 126558. https://doi.org/10.1016/j.apenergy.2025.126558
  26. Zhu Y, Cui J, Ma Y, et al. Tuning the interfacial compatibility of poly(vinylidene difluoride) and poly(ethylene oxide) blends for improved solid-state polymer electrolytes. Science China Chemistry. 2025, 68(12): 6669-6681. https://doi.org/10.1007/s11426-025-3128-2
  27. Bilisik K, Akter M. Polymer nanocomposites based on graphite nanoplatelets (GNPs): a review on thermal-electrical conductivity, mechanical and barrier properties. Journal of Materials Science. 2022, 57(15): 7425-7480. https://doi.org/10.1007/s10853-022-07092-0
  28. Alturki YA, Al Munif EH, Alarawi A, et al. Chemical Thermal Performance of Nonmetallic Polymers, PEEK, PVDF, PPS and SMPs in Extreme Downhole Conditions. Middle East Oil, Gas and Geosciences Show (MEOS GEO). Published online September 16, 2025. https://doi.org/10.2118/227273-ms
  29. Dagdag O, Kim H. Recent Advances in the Hydrogen Gas Barrier Performance of Polymer Liners and Composites for Type IV Hydrogen Storage Tanks: Fabrication, Properties, and Molecular Modeling. Polymers. 2025, 17(9): 1231. https://doi.org/10.3390/polym17091231
  30. Frias-Cacho X, Castro M, Nguyen D-D, et al. A Review of In-Service Coating Health Monitoring Technologies: Towards “Smart” Neural-Like Networks for Condition-Based Preventive Maintenance. Coatings. 2022, 12(5): 565. https://doi.org/10.3390/coatings12050565
  31. Oumaima J, Zakaria M, Laidi Z. A brief review on AI-driven approaches for predictive maintenance of pipelines. 2025 11th International Conference on Optimization and Applications (ICOA). IEEE. 2025: 1-7. https://doi.org/10.1109/ICOA66896.2025.11236851
  32. Tu J, Yeoh GH, Liu C, et al. Computational fluid dynamics: a practical approach. Elsevier, 2023.
  33. Ferziger JH, Perić M. Computational Methods for Fluid Dynamics. (, ed.). Springer Berlin Heidelberg, 2002. https://doi.org/10.1007/978-3-642-56026-2
  34. Shah BA, Muthalif AGA. A comprehensive review on corrosion management in oil and gas pipeline: methods and technologies for corrosion prevention, inspection and monitoring. Anti-Corrosion Methods and Materials. 2025, 72(5): 681-701. https://doi.org/10.1108/ACMM-09-2024-3085
  35. Nešić S. Key issues related to modelling of internal corrosion of oil and gas pipelines – A review. , ed. Corrosion Science. 2007, 49(12): 4308-4338. https://doi.org/10.1016/j.corsci.2007.06.006
  36. Reddy YD, Goud BS, Nisar KS, et al. Heat absorption/generation effect on MHD heat transfer fluid flow along a stretching cylinder with a porous medium. Alexandria Engineering Journal. 2023, 64: 659-666. https://doi.org/10.1016/j.aej.2022.08.049
  37. Natsui S, Tonya K, Hirai A, et al. Comprehensive numerical assessment of molten iron–slag trickle flow and gas countercurrent in complex coke bed by Eulerian–Lagrangian approach. , ed. Chemical Engineering Journal. 2021, 414: 128606. https://doi.org/10.1016/j.cej.2021.128606
  38. Tran MK, Panchal S, Chauhan V, et al. Python-based scikit-learn machine learning models for thermal and electrical performance prediction of high-capacity lithiumion battery. International Journal of Energy Research. 2022, 46(2): 786-794. https://doi.org/10.1002/er.7202
  39. Rasheed A, San O, Kvamsdal T. Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. , ed. IEEE Access. 2020, 8: 21980-22012. https://doi.org/10.1109/access.2020.2970143
  40. Han J, Pei J, Tong H. Data mining: concepts and techniques. Morgan kaufmann, 2022.
  41. Iranzad R, Liu X. A review of random forest-based feature selection methods for data science education and applications. International Journal of Data Science and Analytics. 2025, 20(2): 197-211. https://doi.org/10.1007/s41060-024-00509-w
  42. Arkes J. Regression analysis: a practical introduction. Routledge, 2025.
  43. Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. , ed. Geoscientific Model Development. 2014, 7(3): 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
  44. Sajedian A, Mohaghegh S, Kenoth SA, et al. AI-Based Smart Proxy Models for Accurate Oil Rate Prediction and Efficient Pipeline Monitoring. Annals of Marine Science. 2024, 8(1): 042-054. https://doi.org/10.17352/ams.000048
  45. Kasireddy V, Akinci B. Assessing the impact of 3D point neighborhood size selection on unsupervised spall classification with 3D bridge point clouds. , ed. Advanced Engineering Informatics. 2022, 52: 101624. https://doi.org/10.1016/j.aei.2022.101624
  46. Bhave A, van Delden J, Gloor PA, et al. Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions. , ed. Sensors. 2023, 23(15): 6789. https://doi.org/10.3390/s23156789
  47. Ferziger JH, Perić M, Street RL. Computational Methods for Fluid Dynamics. (, ed.). Springer International Publishing, 2020. https://doi.org/10.1007/978-3-319-99693-6
  48. Stroup WW, Ptukhina M, Garai J. Generalized linear mixed models: modern concepts, methods and applications. Chapman and Hall, CRC, 2024.
  49. Roustaei N. Application and interpretation of linear-regression analysis. Medical Hypothesis, Discovery and Innovation in Ophthalmology. 2024, 13(3): 151. https://doi.org/10.51329/mehdiophthal1506
  50. Wilk MB, Gnanadesikan R. Probability Plotting Methods for the Analysis of Data. , ed. Biometrika. 1968, 55(1): 1. https://doi.org/10.2307/2334448
  51. Hadi AS, Chatterjee S. Regression analysis by example using R. John Wiley & Sons, 2023.