The Role of Artificial Intelligence in Enhancing Corrosion Inhibitor Application in Oil and Gas Facilities
DOI:
https://doi.org/10.64137/31078699/IJETET-V2I4P101Keywords:
Artificial Intelligence, Corrosion Inhibitors, Machine Learning, Oil And Gas Facilities, Corrosion Monitoring, Dosage OptimizationAbstract
Corrosion poses a major challenge to oil and gas facilities. While chemical inhibitors are widely used for mitigation, their effectiveness depends on complex, nonlinear interactions among temperature, pressure, fluid chemistry, material properties, microbial activity, and flow conditions. Traditional selection and dosage approaches, relying on lab tests, empirical models, and periodic inspections, often fail to adapt to dynamic operating environments. Artificial Intelligence (AI), especially machine learning and data-driven optimization offers powerful opportunities to transform corrosion inhibitor management by extracting insights from lab, sensor, inspection, and operational datasets. This review analyzes some artificial intelligence applications throughout the entire inhibitor life cycle: corrosion-rate forecasting, inhibitor selection, dosage optimization, molecular screening and the design of green inhibitors. The discussion focused on use cases like real-time monitoring, predictive maintenance and digital-twin-enabled asset integrity management. Additionally, core AI algorithms such as neural networks, Support Vector Machines (SVMs), Random Forests (RF), Gradient Boosting (GBM), genetic algorithms, fuzzy logic and reinforcement learning, together with more application-oriented QSAR modelling and physics-informed hybrid approaches were assessed. Nonetheless, there are significant barriers to industrial adoption, including data shortage and heterogeneity, interpretability of models, transferability from laboratory scale to field conditions, cybersecurity concerns, as well as a lack of validation under field conditions. To address this shortcoming, how data-driven predictive models are integrated with the experiment must be a prime focus of future research, with standardized datasets and interpretable or physics-based guided AI systems, IIoT integration, experimentally validated closed-loop systems, etc. In the end, AI is a decision-support tool, and when combined with effective engineering mitigations, it can make inhibitor applications more efficient, adaptive, and sustainable.
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