Corrosion Monitoring and Predictive Modelling of Gas Processing Pipelines in Ghana and Sub-Saharan Africa: Evidence Gaps and a Research Agenda

Gas processing pipelines are central to gas monetisation and power stability in Ghana, yet peer reviewed evidence on their corrosion behaviour remains limited compared with global literature. The regional operating environment combines internal wet gas CO2 and H2S attack with marine atmospheric corrosion, corrosion under insulation and buried coating failure, creating multi mechanism degradation that cannot be represented by single gas intuition or average rate models alone. At the same time, monitoring and predictive modelling have moved toward layered sensing architectures and probabilistic, machine learning and hybrid approaches, although many studies rely on simulated data, weak validation and incomplete uncertainty treatment, which reduces confidence for safety critical deployment. Using a structured narrative review with scoping features and conservative inclusion criteria, this article examines corrosion mechanisms, monitoring techniques, predictive models and the limited Ghana and Sub Saharan specific evidence base. The review finds that model transfer without regional calibration is unsafe and that monitoring must be designed around deployability as much as technical capability. It proposes a research programme focused on long term regional datasets, systematic recalibration of transferable oil-sector and analogue marine evidence, hybrid mechanistic-data driven models with explicit uncertainty quantification, field validation under tropical coastal and onshore gas conditions, and open yet secure data structures and capacity building to support defensible integrity decisions in Ghanaian and Sub Saharan gas processing networks.

pipeline integrity management, pipeline corrosion, predictive modelling, corrosion monitoring, uncertainty quantification.

1. Abd, A. A., Naji, S. Z., & Hashim, A. S. (2019). Failure analysis of carbon dioxide corrosion through wet natural gas gathering pipelines. Engineering Failure Analysis, 105, 638–646. https://doi.org/10.1016/J.ENGFAILANAL.2019.07.026 [Google Scholar] [Crossref]

2. Ablo, A. D. (2018). Scale, local content and the challenges of Ghanaians employment in the oil and gas industry. Geoforum, 96, 181-189. [Google Scholar] [Crossref]

3. Ablo, A. D., & Otchere-Darko, W. (2022). Local content and local participation in the oil and gas industry: has Ghana gotten it right?. In Petroleum Resource Management in Africa: Lessons from Ten Years of Oil and Gas Production in Ghana(pp. 291-313). Cham: Springer International Publishing. [Google Scholar] [Crossref]

4. Adjei, A. N., & Ackah, I. (2023). External analysis of local content dynamics in the upstream petroleum sector of Ghana and Ivory Coast. The Extractive Industries and Society, 15, 101304. https://doi.org/10.1016/J.EXIS.2023.101304 [Google Scholar] [Crossref]

5. Afrifa-Yamoah, E. (2024). Towards sustainable resource management: Graph modelling insights from Ghana’s oil and gas local content ecosystem. The Extractive Industries and Society, 20, 101570. https://doi.org/10.1016/J.EXIS.2024.101570 [Google Scholar] [Crossref]

6. Ahmad, E. A., Chang, H. Y., Al-Kindi, M., Joshi, G. R., Cooper, K., Lindsay, R., & Harrison, N. M. (2019). Corrosion Protection through Naturally Occurring Films: New Insights from Iron Carbonate. ACS Applied Materials & Interfaces, 11(36), 33435–33441. https://doi.org/10.1021/ACSAMI.9B10221 [Google Scholar] [Crossref]

7. Akhlaghi, B., Mesghali, H., Ehteshami, M., Mohammadpour, J., Salehi, F., & Abbassi, R. (2023). Predictive deep learning for pitting corrosion modeling in buried transmission pipelines. Process Safety and Environmental Protection, 174, 320–327. https://doi.org/10.1016/J.PSEP.2023.04.010 [Google Scholar] [Crossref]

8. Alcántara, J., de la Fuente, D., Chico, B., Simancas, J., Díaz, I., & Morcillo, M. (2017). Marine Atmospheric Corrosion of Carbon Steel: A Review. Materials 2017, Vol. 10, Page 406, 10(4), 406. https://doi.org/10.3390/MA10040406 [Google Scholar] [Crossref]

9. Alsalem, M. M., Ryan, M. P., Campbell, A. N., & Campbell, K. S. (2023). Modelling of CO2 corrosion and FeCO3 formation in NaCl solutions. Chemical Engineering Journal, 451, 138966. https://doi.org/10.1016/J.CEJ.2022.138966 [Google Scholar] [Crossref]

10. Amoah, P., Owusu-Poku, S., & Ajubala, G. A. (2023). Investigation of Wall Thickness, Corrosion, and Deposits in Industrial Pipelines Using Radiographic Technique. International Journal of Corrosion, 2023(1), 4924399. https://doi.org/10.1155/2023/4924399 [Google Scholar] [Crossref]

11. Askari, M., Aliofkhazraei, M., & Afroukhteh, S. (2019). A comprehensive review on internal corrosion and cracking of oil and gas pipelines. Journal of Natural Gas Science and Engineering, 71, 102971. https://doi.org/10.1016/J.JNGSE.2019.102971 [Google Scholar] [Crossref]

12. Baethge, C., Goldbeck-Wood, S., & Mertens, S. (2019). SANRA—a scale for the quality assessment of narrative review articles. Research Integrity and Peer Review 2019 4:1, 4(1), 5-. https://doi.org/10.1186/S41073-019-0064-8 [Google Scholar] [Crossref]

13. Bai, P. peng, Zhou, J., Luo, B. wei, Zheng, S. qi, Wang, P. yan, & Tian, Y. (2020). Hydrogen embrittlement of X80 pipeline steel in H2S environment: Effect of hydrogen charging time, hydrogen-trapped state and hydrogen charging–releasing–recharging cycles. International Journal of Minerals, Metallurgy and Materials, 27(1), 63–73. https://doi.org/10.1007/S12613-019-1870-1/METRICS [Google Scholar] [Crossref]

14. Balaskó, M., Sváb, E., Kuba, A., Kiss, Z., Rodek, L., & Nagy, A. (2005). Pipe corrosion and deposit study using neutron- and gamma- radiation sources. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 542(1–3), 302–308. https://doi.org/10.1016/J.NIMA.2005.01.153 [Google Scholar] [Crossref]

15. Barker, R., Burkle, D., Charpentier, T., Thompson, H., & Neville, A. (2018). A review of iron carbonate (FeCO3) formation in the oil and gas industry. Corrosion Science, 142, 312–341. https://doi.org/10.1016/J.CORSCI.2018.07.021 [Google Scholar] [Crossref]

16. Caines, S., Khan, F., Shirokoff, J., & Qiu, W. (2015). Experimental design to study corrosion under insulation in harsh marine environments. Journal of Loss Prevention in the Process Industries, 33, 39–51. https://doi.org/10.1016/J.JLP.2014.10.014 [Google Scholar] [Crossref]

17. Cao, Q., Pojtanabuntoeng, T., Esmaily, M., Thomas, S., Brameld, M., Amer, A., & Birbilis, N. (2022). A Review of Corrosion under Insulation: A Critical Issue in the Oil and Gas Industry. Metals 2022, Vol. 12, Page 561, 12(4), 561. https://doi.org/10.3390/MET12040561 [Google Scholar] [Crossref]

18. Carlson, L. C., Rogers, T. T., Kamara, T. B., Rybarczyk, M. M., Leow, J. J., Kirsch, T. D., & Kushner, A. L. (2015). Petroleum pipeline explosions in sub-Saharan Africa: A comprehensive systematic review of the academic and lay literature. Burns, 41(3), 497–501. https://doi.org/10.1016/J.BURNS.2014.08.013 [Google Scholar] [Crossref]

19. Cawley, P. (2024). Guided waves in long range nondestructive testing and structural health monitoring: Principles, history of applications and prospects. NDT & E International, 142, 103026. https://doi.org/10.1016/J.NDTEINT.2023.103026 [Google Scholar] [Crossref]

20. Chen, D., Chen, X., Chen, S., & Chen, Y. (2018). Rust Evolution and Electrochemical Properties of Field-exposed Carbon Steel in a Tropical Marine Environment. International Journal of Electrochemical Science, 13(8), 7505–7516. https://doi.org/10.20964/2018.08.02 [Google Scholar] [Crossref]

21. Chen, H., Cui, H., He, Z., Lu, L., & Huang, Y. (2021). Influence of chloride deposition rate on rust layer protectiveness and corrosion severity of mild steel in tropical coastal atmosphere. Materials Chemistry and Physics, 259, 123971. https://doi.org/10.1016/J.MATCHEMPHYS.2020.123971 [Google Scholar] [Crossref]

22. Chen, Z., Xu, M., Hu, T., Xue, G., Chen, F., Zhao, H., Zhou, H., Lei, Y., & Zhu, K. (2024). Effects of H2S content on the corrosion behavior of gas storage reservoir injection and production pipeline steel in CO2-H2S environment. Materials Today Communications, 41, 110364. https://doi.org/10.1016/J.MTCOMM.2024.110364 [Google Scholar] [Crossref]

23. Coelho, L. B., Zhang, D., Van Ingelgem, Y., Steckelmacher, D., Nowé, A., & Terryn, H. (2022). Reviewing machine learning of corrosion prediction in a data-oriented perspective. Npj Materials Degradation, 6(1), 8-. https://doi.org/10.1038/s41529-022-00218-4 [Google Scholar] [Crossref]

24. Corvo, F., Perez, T., Dzib, L. R., Martin, Y., Castañeda, A., Gonzalez, E., & Perez, J. (2008). Outdoor–indoor corrosion of metals in tropical coastal atmospheres. Corrosion Science, 50(1), 220–230. https://doi.org/10.1016/J.CORSCI.2007.06.011 [Google Scholar] [Crossref]

25. Dai, M. (2021). In situ mathematically simulation for CO2 internal corrosion in wet natural gas gathering pipelines system by HYSYS. Engineering Failure Analysis, 122, 105265. https://doi.org/10.1016/J.ENGFAILANAL.2021.105265 [Google Scholar] [Crossref]

26. Dhutti, A., Dhutti, A., Malo, S., Marques, H., Balachandran, W., & Gan, T. H. (2021). Numerical Modelling of Ultrasonic Guided Wave Propagation and Defect Detection in Offshore Steel Sheet Piles. Applied Sciences 2021, Vol. 11, Page 4076, 11(9), 4076. https://doi.org/10.3390/APP11094076 [Google Scholar] [Crossref]

27. Dong, Z., Ding, L., Meng, Z., Xu, K., Mao, Y., Chen, X., Ye, H., & Poursaee, A. (2024). Machine learning-based corrosion rate prediction of steel embedded in soil. Scientific Reports 2024 14:1, 14(1), 18194-. https://doi.org/10.1038/s41598-024-68562-w [Google Scholar] [Crossref]

28. Edalati, K., Rastkhah, N., Kermani, A., Seiedi, M., & Movafeghi, A. (2006). The use of radiography for thickness measurement and corrosion monitoring in pipes. International Journal of Pressure Vessels and Piping, 83(10), 736–741. https://doi.org/10.1016/J.IJPVP.2006.07.010 [Google Scholar] [Crossref]

29. Ezeh, C. C., & Oriaku, C. C. (2020). Corrosion In multiphase flow systems: The impact of high CO2 and low water conditions. International Journal of Science and Research Archive, 1(1), 184–200. https://doi.org/10.30574/IJSRA.2020.1.1.0043 [Google Scholar] [Crossref]

30. Fang, J., Cheng, X., Gai, H., Lin, S., & Lou, H. (2023). Development of machine learning algorithms for predicting internal corrosion of crude oil and natural gas pipelines. Computers & Chemical Engineering, 177, 108358. https://doi.org/10.1016/J.COMPCHEMENG.2023.108358 [Google Scholar] [Crossref]

31. Farh, H. M. H., Ben Seghier, M. E. A., & Zayed, T. (2023). A comprehensive review of corrosion protection and control techniques for metallic pipelines. Engineering Failure Analysis, 143, 106885. https://doi.org/10.1016/J.ENGFAILANAL.2022.106885 [Google Scholar] [Crossref]

32. Fonseca, D., Tagliari, M. R., Guaglianoni, W. C., Tamborim, S. M., & Borges, M. F. (2024). Carbon Dioxide Corrosion Mechanisms: Historical Development and Key Parameters of CO2-H2O Systems. International Journal of Corrosion, 2024(1), 5537767. https://doi.org/10.1155/2024/5537767 [Google Scholar] [Crossref]

33. Gbadago, F. Y., Enu-Kwesi, F., Agyei, S. K., & Gatsi, J. G. (2023). Key local factors, supplier and linkage effectiveness: Evidence from the upstream petroleum sector of Ghana. Resources Policy, 85, 103929. https://doi.org/10.1016/J.RESOURPOL.2023.103929 [Google Scholar] [Crossref]

34. Ghavamian, A., Mustapha, F., Baharudin, B. T. H. T., & Yidris, N. (2018). Detection, Localisation and Assessment of Defects in Pipes Using Guided Wave Techniques: A Review. Sensors 2018, Vol. 18, Page 4470, 18(12), 4470. https://doi.org/10.3390/S18124470 [Google Scholar] [Crossref]

35. Gou, J., Nie, R., Xing, X., Li, Z., Cui, G., Liu, J., Deng, X., & Cheng, Y. F. (2023). Hydrogen-induced cracking of welded X80 steel studies by experimental testing and molecular dynamics modeling. Corrosion Science, 214, 111027. https://doi.org/10.1016/J.CORSCI.2023.111027 [Google Scholar] [Crossref]

36. Hassan, S., Wang, J., Kontovas, C., & Bashir, M. (2022). An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks. Reliability Engineering & System Safety, 218, 108171. https://doi.org/10.1016/J.RESS.2021.108171 [Google Scholar] [Crossref]

37. Huang, X., Zhou, L., Li, Y., Du, Z., Zhu, Q., & Han, Z. (2023). The synergistic effect of temperature, H2S/CO2 partial pressure and stress toward corrosion of X80 pipeline steel. Engineering Failure Analysis, 146, 107079. https://doi.org/10.1016/J.ENGFAILANAL.2023.107079 [Google Scholar] [Crossref]

38. Jacklin, R., Owen, J., Sykes, A., Burkle, D., Woollam, R. C., & Barker, R. (2024). An electrochemical study of iron carbonate layers formed on carbon steel during corrosion in elevated pressure CO2 environments. Corrosion Science, 235, 112202. https://doi.org/10.1016/J.CORSCI.2024.112202 [Google Scholar] [Crossref]

39. Kamali, N., Javidi, M., Pakravan, H. A., & Keshavarz, P. (2026). Simulation of multiphase flow in wet natural gas offshore pipelines to assess top-of-the-line corrosion risks: A case study. Corrosion Science, 258, 113371. https://doi.org/10.1016/J.CORSCI.2025.113371 [Google Scholar] [Crossref]

40. Khalili, P., & Cawley, P. (2018). The choice of ultrasonic inspection method for the detection of corrosion at inaccessible locations. NDT & E International, 99, 80–92. https://doi.org/10.1016/J.NDTEINT.2018.06.003 [Google Scholar] [Crossref]

41. Komary, M., Komarizadehasl, S., Tošić, N., Segura, I., Lozano-Galant, J. A., & Turmo, J. (2023). Low-Cost Technologies Used in Corrosion Monitoring. Sensors 2023, Vol. 23, Page 1309, 23(3), 1309. https://doi.org/10.3390/S23031309 [Google Scholar] [Crossref]

42. Lazareva, A., Owen, J., Vargas, S., Barker, R., & Neville, A. (2021). Investigation of the evolution of an iron carbonate layer and its effect on localized corrosion of X65 carbon steel in CO2 corrosion environments. Corrosion Science, 192, 109849. https://doi.org/10.1016/J.CORSCI.2021.109849 [Google Scholar] [Crossref]

43. Li, K., Zeng, Y., & Luo, J. L. (2021). Influence of H2S on the general corrosion and sulfide stress cracking of pipelines steels for supercritical CO2 transportation. Corrosion Science, 190, 109639. https://doi.org/10.1016/J.CORSCI.2021.109639 [Google Scholar] [Crossref]

44. Li, Q., Wang, D., Deng, C., Wu, S., Gao, Z., Zhao, H., & Liang, H. (2023). Role of microstructure heterogeneity on hydrogen-assisted fracture toughness degradation of X80 weld metal in H2S-saturated solution. Corrosion Science, 224, 111467. https://doi.org/10.1016/J.CORSCI.2023.111467 [Google Scholar] [Crossref]

45. Li, Y. T., He, X. N., & Shuai, J. (2022). Risk analysis and maintenance decision making of natural gas pipelines with external corrosion based on Bayesian network. Petroleum Science, 19(3), 1250–1261. https://doi.org/10.1016/J.PETSCI.2021.09.016 [Google Scholar] [Crossref]

46. Liao, K., Leng, J., Cheng, Y. F., He, T., He, G., Zhao, S., Liu, X., & Huang, Q. (2022). Effect of H2S concentrations on corrosion failure of L245NS steel in CO2-O2-H2S system. Process Safety and Environmental Protection, 168, 224–238. https://doi.org/10.1016/J.PSEP.2022.09.038 [Google Scholar] [Crossref]

47. Liu, L., & Case, R. (2022). The influence of H2S on hydrogen absorption and sulfide stress cracking resistance of high strength low alloy carbon steel C110. Journal of Natural Gas Science and Engineering, 99, 104418. https://doi.org/10.1016/J.JNGSE.2022.104418 [Google Scholar] [Crossref]

48. Lotz, H., Neff, D., Mercier-Bion, F., Bataillon, C., Nuns, N., & Dillmann, P. (2024). 13C isotopic labeling to decipher the iron corrosion mechanisms in a carbonated anoxic environment. Materials and Corrosion, 75(6), 786–795. https://doi.org/10.1002/MACO.202314203;JOURNAL:JOURNAL:15214176;WGROUP:STRING:PUBLICATION [Google Scholar] [Crossref]

49. Lyu, F., Zhou, X., Ding, Z., Qiao, X., & Song, D. (2024). Application Research of Ultrasonic-Guided Wave Technology in Pipeline Corrosion Defect Detection: A Review. Coatings 2024, Vol. 14, Page 358, 14(3), 358. https://doi.org/10.3390/COATINGS14030358 [Google Scholar] [Crossref]

50. Ma, H., Zhang, W., Wang, Y., Ai, Y., & Zheng, W. (2023). Advances in corrosion growth modeling for oil and gas pipelines: A review. Process Safety and Environmental Protection, 171, 71–86. https://doi.org/10.1016/J.PSEP.2022.12.054 [Google Scholar] [Crossref]

51. Ma, Q., Liang, W., & Zhou, P. (2025). A Review on Pipeline In-Line Inspection Technologies. Sensors 2025, Vol. 25, Page 4873, 25(15), 4873. https://doi.org/10.3390/S25154873 [Google Scholar] [Crossref]

52. Ma, W. L., Wang, H. X., Barker, R., Kapur, N., Hua, Y., & Neville, A. (2021). Corrosion behaviour of X65 carbon steel under the intermittent oil/water wetting: A synergic effect of flow velocity and alternate immersion period. Corrosion Science, 187, 109507. https://doi.org/10.1016/J.CORSCI.2021.109507 [Google Scholar] [Crossref]

53. Ma, Y., Li, Y., & Wang, F. (2010). The atmospheric corrosion kinetics of low carbon steel in a tropical marine environment. Corrosion Science, 52(5), 1796–1800. https://doi.org/10.1016/J.CORSCI.2010.01.022 [Google Scholar] [Crossref]

54. Martinelli-Orlando, F., & Angst, U. (2022). Monitoring corrosion rates with ER-probes – a critical assessment based on experiments and numerical modelling. Corrosion Engineering, Science and Technology, 57(3), 254–268. https://doi.org/10.1080/1478422X.2022.2053036 [Google Scholar] [Crossref]

55. May, Z., Alam, M. K., & Nayan, N. A. (2022). Recent Advances in Nondestructive Method and Assessment of Corrosion Undercoating in Carbon–Steel Pipelines. Sensors 2022, Vol. 22, Page 6654, 22(17), 6654. https://doi.org/10.3390/S22176654 [Google Scholar] [Crossref]

56. Mgbemere, H. E., Agunsoye, J. O., Ojemeni, K. I., & Izuegbu, O. V. (2025). Corrosion Mitigation with Chemical Injection in Welded Carbon Steel Pipe for Offshore Water Treatment Plant. ABUAD Journal of Engineering Research and Development (AJERD), 8(1), 113-121. [Google Scholar] [Crossref]

57. Nešić, S. (2007). Key issues related to modelling of internal corrosion of oil and gas pipelines – A review. Corrosion Science, 49(12), 4308–4338. https://doi.org/10.1016/J.CORSCI.2007.06.006 [Google Scholar] [Crossref]

58. Nguyen, T. C., So, Y. S., Yoo, J. S., & Kim, J. G. (2022). Machine learning modeling of predictive external corrosion rates of spent nuclear fuel carbon steel canister in soil. Scientific Reports 2022 12:1, 12(1), 20281-. https://doi.org/10.1038/s41598-022-24783-5 [Google Scholar] [Crossref]

59. Obaseki, M., Nwankwojike, B. N., & Abam, F. I. (2022). Diagnostic and prognostic development of a mechanistic model for multiphase flow in oil-gas pipelines. Journal of King Saud University - Engineering Sciences, 34(8), 562–570. https://doi.org/10.1016/J.JKSUES.2020.12.010 [Google Scholar] [Crossref]

60. Obike, A. I., Uwakwe, K. J., Abraham, E. K., Ikeuba, A. I., & Emori, W. (2020). Review of the losses and devastation caused by corrosion in the Nigeria oil industry for over 30 years. International Journal of corrosion and scale inhibition, 9(1), 74-91. [Google Scholar] [Crossref]

61. Odumugbo, C. A. (2010). Natural gas utilisation in Nigeria: Challenges and opportunities. Journal of Natural Gas Science and Engineering, 2(6), 310–316. https://doi.org/10.1016/J.JNGSE.2010.08.004 [Google Scholar] [Crossref]

62. Okyere, M. S. (2025). 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, 72(1), 250. [Google Scholar] [Crossref]

63. Orlikowski, J., Jażdżewska, A., Łuksa, I., Szociński, M., & Darowicki, K. (2024). Corrosion Monitoring in Petroleum Installations—Practical Analysis of the Methods. Materials 2024, Vol. 17, Page 2663, 17(11), 2663. https://doi.org/10.3390/MA17112663 [Google Scholar] [Crossref]

64. Ossai, C. I. (2012). Advances in Asset Management Techniques: An Overview of Corrosion Mechanisms and Mitigation Strategies for Oil and Gas Pipelines. International Scholarly Research Notices, 2012(1), 570143. https://doi.org/10.5402/2012/570143 [Google Scholar] [Crossref]

65. Ossai, C. I., Boswell, B., & Davies, I. J. (2015). Pipeline failures in corrosive environments–A conceptual analysis of trends and effects. Engineering Failure Analysis, 53, 36-58. [Google Scholar] [Crossref]

66. Özkan, C., Sahlmann, L., Feiler, C., Zheludkevich, M., Lamaka, S., Sewlikar, P., Kooijman, A., Taheri, P., & Mol, A. (2024). Laying the experimental foundation for corrosion inhibitor discovery through machine learning. Npj Materials Degradation 2024 8:1, 8(1), 21-. https://doi.org/10.1038/s41529-024-00435-z [Google Scholar] [Crossref]

67. Pan, L., Shi, K., Han, L., Qu, D., Zhang, Y., & Chen, W. (2024). Research on Wireless Passive Ultrasonic Thickness Measurement Technology Based on Pulse Compression Method. Sensors 2024, Vol. 24, Page 8023, 24(24), 8023. https://doi.org/10.3390/S24248023 [Google Scholar] [Crossref]

68. Parlak, B. O., & Yavasoglu, H. A. (2023). A Comprehensive Analysis of In-Line Inspection Tools and Technologies for Steel Oil and Gas Pipelines. Sustainability 2023, Vol. 15, Page 2783, 15(3), 2783. https://doi.org/10.3390/SU15032783 [Google Scholar] [Crossref]

69. Pessu, F., Barker, R., & Neville, A. (2015). The Influence of pH on Localized Corrosion Behavior of X65 Carbon Steel in CO2-Saturated Brines. Corrosion, 71(12), 1452–1466. https://doi.org/10.5006/1770 [Google Scholar] [Crossref]

70. Pollock, D., Evans, C., Menghao Jia, R., Alexander, L., Pieper, D., Brandão de Moraes, É., Peters, M. D. J., Tricco, A. C., Khalil, H., Godfrey, C. M., Saran, A., Campbell, F., & Munn, Z. (2024). “How-to”: scoping review? Journal of Clinical Epidemiology, 176, 111572. https://doi.org/10.1016/J.JCLINEPI.2024.111572 [Google Scholar] [Crossref]

71. Qin, M., Liao, K., He, G., Huang, Y., Wang, M., & Zhang, S. (2022). Main control factors and prediction model of flow-accelerated CO2/H2S synergistic corrosion for X65 steel. Process Safety and Environmental Protection, 160, 749–762. https://doi.org/10.1016/J.PSEP.2022.02.062 [Google Scholar] [Crossref]

72. Qin, M., Liao, K., He, G., Zou, Q., Zhao, S., & Zhang, S. (2022). Corrosion mechanism of X65 steel exposed to H2S/CO2 brine and H2S/CO2 vapor corrosion environments. Journal of Natural Gas Science and Engineering, 106, 104774. https://doi.org/10.1016/J.JNGSE.2022.104774 [Google Scholar] [Crossref]

73. Ren, L., Jiang, T., Jia, Z. guang, Li, D. sheng, Yuan, C. lin, & Li, H. nan. (2018). Pipeline corrosion and leakage monitoring based on the distributed optical fiber sensing technology. Measurement, 122, 57–65. https://doi.org/10.1016/J.MEASUREMENT.2018.03.018 [Google Scholar] [Crossref]

74. Salgueiro, F., Ribeiro, M., Carvalho, A., Covas, G., Baltzersen, Ø., & Proença, C. S. (2024). Monitoring of Wall Thickness to Predict Corrosion in Marine Environments Using Ultrasonic Transducers. NDT 2024, Vol. 2, Pages 255-269, 2(3), 255–269. https://doi.org/10.3390/NDT2030016 [Google Scholar] [Crossref]

75. Santa, A. C., Tamayo, J. A., Correa, C. D., Gómez, M. A., Castaño, J. G., & Baena, L. M. (2022). Atmospheric corrosion maps as a tool for designing and maintaining building materials: A review. Heliyon, 8(9), e10438. https://doi.org/10.1016/J.HELIYON.2022.E10438 [Google Scholar] [Crossref]

76. Seechurn, Y., Surnam, B. Y. R., & Wharton, J. A. (2022). Marine atmospheric corrosion of carbon steel in the tropical microclimate of Port Louis. Materials and Corrosion, 73(9), 1474–1489. https://doi.org/10.1002/MACO.202112871 [Google Scholar] [Crossref]

77. Seechurn, Y., Surnam, B. Y. R., & Wharton, J. A. (2023). Marine atmospheric corrosion of carbon steel in Mauritius. Materials Today: Proceedings. https://doi.org/10.1016/J.MATPR.2023.07.140 [Google Scholar] [Crossref]

78. Shabarchin, O., & Tesfamariam, S. (2016). Internal corrosion hazard assessment of oil & gas pipelines using Bayesian belief network model. Journal of Loss Prevention in the Process Industries, 40, 479–495. https://doi.org/10.1016/J.JLP.2016.02.001 [Google Scholar] [Crossref]

79. Shin, D. H., Hwang, H. K., Kim, H. H., & Lee, J. H. (2022). Evaluation of Commercial Corrosion Sensors for Real-Time Monitoring of Pipe Wall Thickness under Various Operational Conditions. Sensors, 22(19), 7562. https://doi.org/10.3390/S22197562/S1 [Google Scholar] [Crossref]

80. Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836. https://doi.org/10.1016/J.YMSSP.2010.11.018 [Google Scholar] [Crossref]

81. Song, Y., Wang, Q., Zhang, X., Dong, L., Bai, S., Zeng, D., Zhang, Z., Zhang, H., & Xi, Y. (2023). Interpretable machine learning for maximum corrosion depth and influence factor analysis. Npj Materials Degradation 2023 7:1, 7(1), 9-. https://doi.org/10.1038/s41529-023-00324-x [Google Scholar] [Crossref]

82. Soomro, A. A., Mokhtar, A. A., Kurnia, J. C., Lashari, N., Sarwar, U., Jameel, S. M., Inayat, M., & Oladosu, T. L. (2022). A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion. International Journal of Pressure Vessels and Piping, 200, 104841. https://doi.org/10.1016/J.IJPVP.2022.104841 [Google Scholar] [Crossref]

83. Sutojo, T., Rustad, S., Akrom, M., Syukur, A., Shidik, G. F., & Dipojono, H. K. (2023). A machine learning approach for corrosion small datasets. Npj Materials Degradation 2023 7:1, 7(1), 18-. https://doi.org/10.1038/s41529-023-00336-7 [Google Scholar] [Crossref]

84. Tan, L., Yang, Y., Zhang, K., Liao, K., He, G., Tian, J., & Lu, X. (2025). Prediction of internal corrosion rate for gas pipeline: A new method based on transformer architecture. Computers & Chemical Engineering, 198, 109084. https://doi.org/10.1016/J.COMPCHEMENG.2025.109084 [Google Scholar] [Crossref]

85. Thibbotuwa, U. C., Cortés, A., & Irizar, A. (2022). Small Ultrasound-Based Corrosion Sensor for Intraday Corrosion Rate Estimation. Sensors 2022, Vol. 22, Page 8451, 22(21), 8451. https://doi.org/10.3390/S22218451 [Google Scholar] [Crossref]

86. Tricco, A. C., Robinson, E., Straus, S. E., Colquhoun, H., Godfrey, C. M., Moher, D., Munn, Z., Pollock, D., Logan, S., Elsman, E. B. M., Hasanoff, S., Lai, Y., McMahon, J., Dourka, J., Neupane, D., Khalil, H., Peters, M. D. J., Alexander, L., Jia, R. M., … Veroniki, A. A. (2026). Updating the PRISMA reporting guideline for scoping reviews: a scoping review. Journal of Clinical Epidemiology, 196, 112314. https://doi.org/10.1016/J.JCLINEPI.2026.112314 [Google Scholar] [Crossref]

87. Turnbull, D., Chugh, R., & Luck, J. (2023). Systematic-narrative hybrid literature review: A strategy for integrating a concise methodology into a manuscript. Social Sciences & Humanities Open, 7(1), 100381. https://doi.org/10.1016/J.SSAHO.2022.100381 [Google Scholar] [Crossref]

88. Wang, C., Hua, Y., Nadimi, S., Hu, Q., Taleb, W., Zhang, J., Liu, X., Zhang, R., Chen, X., Neville, A., & Li, Y. (2022). Anti-corrosion characteristics of FeCO3 and FexCayMgzCO3 scales on carbon steel in high-PT CO2 environments. Chemical Engineering Journal, 431, 133484. https://doi.org/10.1016/J.CEJ.2021.133484 [Google Scholar] [Crossref]

89. Wang, Q., Song, Y., Zhang, X., Dong, L., Xi, Y., Zeng, D., Liu, Q., Zhang, H., Zhang, Z., Yan, R., & Luo, H. (2023). Evolution of corrosion prediction models for oil and gas pipelines: From empirical-driven to data-driven. Engineering Failure Analysis, 146, 107097. https://doi.org/10.1016/J.ENGFAILANAL.2023.107097 [Google Scholar] [Crossref]

90. Wright, R. F., Lu, P., Devkota, J., Lu, F., Ziomek-Moroz, M., & Ohodnicki, P. R. (2019). Corrosion Sensors for Structural Health Monitoring of Oil and Natural Gas Infrastructure: A Review. Sensors 2019, Vol. 19, Page 3964, 19(18), 3964. https://doi.org/10.3390/S19183964 [Google Scholar] [Crossref]

91. Xie, M., Li, Z., Zhao, J., & Pei, X. (2021). A Prognostics Method Based on Back Propagation Neural Network for Corroded Pipelines. Micromachines 2021, Vol. 12, Page 1568, 12(12), 1568. https://doi.org/10.3390/MI12121568 [Google Scholar] [Crossref]

92. Xu, L., Wang, Y., Mo, L., Tang, Y., Wang, F., & Li, C. (2023). The research progress and prospect of data mining methods on corrosion prediction of oil and gas pipelines. Engineering Failure Analysis, 144, 106951. https://doi.org/10.1016/J.ENGFAILANAL.2022.106951 [Google Scholar] [Crossref]

93. Yang, D., Zhang, X., Zhou, T., Wang, T., & Li, J. (2023). A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN. Sensors 2023, Vol. 23, Page 855, 23(2), 855. https://doi.org/10.3390/S23020855 [Google Scholar] [Crossref]

94. Zhao, W., Zhang, T., Wang, Y., Qiao, J., & Wang, Z. (2018). Corrosion Failure Mechanism of Associated Gas Transmission Pipeline. Materials 2018, Vol. 11, Page 1935, 11(10), 1935. https://doi.org/10.3390/MA11101935 [Google Scholar] [Crossref]

95. Zhou, J., Wu, M., Qiu, J., Cheng, R., & Tian, M. (2025). Influence of H2S on the Hydrogen Embrittlement Susceptibility of X80 Pipeline Steel in Hydrogen-Doped Natural Gas. ACS Omega, 10(26), 28233–28242. https://doi.org/10.1021/ACSOMEGA.5C03111 [Google Scholar] [Crossref]

• Assessing Environmental Support as a Predictor of Occupational Engagement among Women Entrepreneurs in Delta Districts
• Permeability Characteristics of Lower Niger Floodplain Sediments and their Implications for Drainage and Flood Mitigation
• Investigating Atmospheric Metal Pollution in Khartoum North (Bahri), Sudan Using Laser-Induced Breakdown Spectroscopy