This study presents a corrosion database powered by machine learning to link product performance data with formulation design and field application strategies. By leveraging past laboratory data, including fluid and gas chemistry, shear, and corrosion inhibitor performance, the tool identifies patterns and relationships to generate targeted recommendations for both existing products and new corrosion inhibitor formulations. Supervised and unsupervised machine learning algorithms are applied to extract insights from complex datasets, reveal hidden patterns, and enable predictive analytics for corrosion inhibitor performance. In addition to streamlining formulation screening, the tool supports field recommendations by predicting effective chemistries for specific conditions. It employs similarity-based algorithms to identify prior scenarios that align with current inputs, allowing users to adjust parameter weights—such as temperature, pressure, shear, and other relevant factors—to emphasize variables critical to their application. This data-driven approach reduces redundant testing, shortens development timelines, and improves formulation success rates. Implementation has demonstrated faster, more informed decision-making and optimized product selection, highlighting the value of applying machine learning to improve corrosion management.
