Corrosion poses a major challenge in the O&G industry, causing safety hazards, operational downtime, and significant financial losses. This paper presents a data-driven approach to corrosion prediction using advanced machine learning (ML) algorithms Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost). These models are trained on diverse datasets that include sensor metrics (temp, pressure, pH, corrosion rate), fluid properties (CO₂, H₂S, flow velocity), environmental conditions, and historical maintenance records. The models demonstrate strong predictive accuracy, enabling early detection of corrosion and real-time forecasting to support proactive maintenance. A conceptual framework for an ML-powered predictive monitoring tool is introduced, capable of estimating site-specific corrosion rates, and issuing alerts to guide targeted interventions. In addition, this paper addresses the effectiveness of a numerical method to predict CO₂ corrosion using an iterative Newton-Raphson technique within a mechanistic modeling framework. There remains an opportunity to explore the integration of autonomous monitoring systems and Industry 4.0 technologies through smart sensors and real-time analytics. Challenges such as data quality inconsistencies, lack of standardization, and the interpretability of ML models are discussed, along with potential solutions like Explainable AI (XAI), while ensuring a secure integration, and scalable digital infrastructure is maintained.
