Interfacial robustness of commercial amine-based inhibitors mitigates under-deposit corrosion of carbon steel in simulated sour conditions: a merged electrochemical and machine learning study

Abstract In oil and gas industry, under-deposit corrosion (UDC) of metallic pipelines is a major problem, especially in sour environments. Not much research has been done on the effectiveness of commercial inhibitors with potent interfacial qualities in reducing UDC. In order to prevent sand (SiO 2 )-induced UDC on CS in simulated sour conditions, two different commercial inhibitors, CRONOX-CRW85719 (CR1) and CRONOX-CRW85282 (CR2), were thoroughly tested over a concentration range (5–400 ppm). Performance was evaluated using electrochemical studies, physicochemical characterizations, and machine learning (ML) modeling. The results showed that, at optimal concentrations of 50 ppm and 200 ppm for CR1 and CR2, respectively, there were nearly total inhibition efficiencies (IE), outperforming the corrosion resistance of un-inhibited CS. Nevertheless, after 24 h, the IEs of CR1 and CR2 were reduced by 14.7% and 4.0%, respectively, due to the presence of fully covered SiO 2 deposits on the CS surface, suggesting that deposit coverage reduces inhibitor efficacy. Because of the reduced molecular bulk and improved ability of CR2 to penetrate SiO 2 deposits, it performs better and provides better access to the metal surface relative to CR1. The random forest technique was shown to be the most appropriate predictive ML model, with an optimized mean coefficient of determination ( R 2 = 0.85 ± 0.05), a root mean square error (RMSE = 3.6%), and a mean absolute error (MAE = 2.7%), amongst the various ML models. This study emphasizes how important inhibitor molecular properties are in preventing SiO 2 -induced UDC of CS in sour conditions, especially penetrating ability and strong interfacial contacts.