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Integrated electrochemical and machine learning framework for SiO2/CaCO3 under-deposits driven welded X65 carbon steel corrosion mitigation in sour service conditions
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Adewale K. Ipadeolaa, *, Mostafa H. Sliema, Dana Abdeenb, Nicholas Laycockb, Ashwin RajKumarc, Phaneendra K. Yalavarthyc, **, Aboubakr M. Abdullaha, ***
Petroleum Research | 2026, 11(2) : 613 - 632
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Petroleum Research | 2026, 11(2): 613-632
• Full Length Article •
Integrated electrochemical and machine learning framework for SiO2/CaCO3 under-deposits driven welded X65 carbon steel corrosion mitigation in sour service conditions
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Adewale K. Ipadeolaa, *, Mostafa H. Sliema, Dana Abdeenb, Nicholas Laycockb, Ashwin RajKumarc, Phaneendra K. Yalavarthyc, **, Aboubakr M. Abdullaha, ***
Affiliations
  • aCenter for Advanced Materials, Qatar University, Doha 2713, Qatar
  • bQatar Shell Research and Technology Centre, P.O. Box 3747, Doha, Qatar
  • cDepartment of Computational and Data Sciences (CDS), Indian Institute of Science (IISc), Bangalore, India
Published: 2026-06-10 doi: 10.1016/j.ptlrs.2025.09.006
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The integrity of welded X65 carbon steel (CS) pipelines in oil and gas systems is significantly compromised by preferential weldment corrosion (PWC) and pitting in sour conditions. This study clarifies that the effect of pre-corrosion conditioning and inorganic deposits (SiO2 or CaCO3) on welded CS pipelines, with SiO2 deposits leading to the most substantial damage at weld metal (WM) and heat-affected zone (HAZ) relative to parent metal (PM). Due to localized acidification and differences in potential, corrosion rates (CRs) hierarchies are SiO2-deposited > CaCO3-deposited > non-deposited. Microstructural analysis reveals that deposited CS-WM has carbide redistribution and increase residual stress from the welding thermal process, with increased aggressive species and stabilized iron oxides, leading to intensified PWC. However, amine-based inhibitor CRW11 suppresses the CRs below 0.1 mmpy through chemisorbed films that disrupt microgalvanic coupling and protective corrosion product layers with inhibition efficiency (IE > 85%). CS-PM-CaCO3 has a high pit depth (68.3 ± 2.7 μm) and propagation rate (1.2 ± 0.2 mmpy), making it the most vulnerable region to pitting relative to CS-WM-CaCO3 (52.5 ± 1.1 μm; 0.9 ± 0.1 mmpy) and CS-HAZ-CaCO3 (29.3 ± 0.6 μm; 0.5 ± 0.1 mmpy). This proves that CaCO3 deposit on welded CS creates a physical barrier for the surface nucleation effect and improves anodic dissolution. Machine learning models (Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost)) accurately predict IE (R2 = 0.99). However, RF performs best for data without deposits (RMSE = 0.9), while DT is most appropriate model for those with SiO2 and CaCO3 deposits (lowest RMSE = 0.5 and 0.2). These findings provide coupled electrochemical, microstructural, and machine learning frameworks for PWC mitigation, allowing for adaptive corrosion management methods in sour service pipes and material designs optimized for topology.

Under-deposited welded carbon steel  /  Sour media corrosion tests  /  Microstructural alterations  /  Pit depth/Aspect ratio  /  Random forest/Decision tree
Adewale K. Ipadeola, Mostafa H. Sliem, Dana Abdeen, Nicholas Laycock, Ashwin RajKumar, Phaneendra K. Yalavarthy, Aboubakr M. Abdullah. Integrated electrochemical and machine learning framework for SiO2/CaCO3 under-deposits driven welded X65 carbon steel corrosion mitigation in sour service conditions[J]. Petroleum Research, 2026 , 11 (2) : 613 -632 . DOI: 10.1016/j.ptlrs.2025.09.006
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Funding
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  • Qatar National Research Fund (QNRF, a Division of the QRDI Council) through the Academic Research Grant (ARG)(ARG01–0524–230330)
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Year 2026 volume 11 Issue 2
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Article Info
doi: 10.1016/j.ptlrs.2025.09.006
  • Receive Date：2025-07-13
  • Online Date：2026-07-29
  • Published：2026-06-10
Article Data
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History
  • Received：2025-07-13
  • Revised：2025-09-27
  • Accepted：2025-09-28
Funding
Qatar National Research Fund (QNRF, a Division of the QRDI Council) through the Academic Research Grant (ARG)(ARG01–0524–230330)
Affiliations
    aCenter for Advanced Materials, Qatar University, Doha 2713, Qatar
    bQatar Shell Research and Technology Centre, P.O. Box 3747, Doha, Qatar
    cDepartment of Computational and Data Sciences (CDS), Indian Institute of Science (IISc), Bangalore, India

Corresponding:

*

E-mail addresses: (A.K. Ipadeola)

**

(P.K. Yalavarthy)

***

(A.M. Abdullah).
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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