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Corrosion inhibitors (CI) are typically evaluated using either short-term electrochemical methods or long-term weight loss methods in laboratory set up. Although electrochemical methods provide fast and real-time corrosion information, corrosion subject matter experts, in general, rely on long term weight loss methods to determine localized corrosion information. These long-term methods include exposure of the metal coupon in a corrosive media under specific field conditions/parameters such as temperature, pressure, wall shear stress, corrosive gas species and test length in the presence of corrosion inhibitor active(s).
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Electrochemical methods have long been utilized to provide real-time corrosion information but have rarely been used to study localized corrosion. Most recently, electrochemical methods such as electrochemical impedance spectroscopy (EIS) and cyclic polarization (CP) were investigated to predict pitting tendencies and the work was presented in 2022.
Based on this work, it was proved that CP technique can be used to predict localized corrosion tendency of continuous corrosion inhibitors.
Throughout the oil and gas industry, carbon steel continues to be the material of choice for most downhole production tubulars and pipelines. Given the environment of typical oilfield operations, comprehensive integrity management programs are followed to guard against the threats of internal corrosion and material degradation of such assets. Although there are various corrosion mitigation options available, the application of corrosion inhibitor chemical products is commonplace given their relative ease of use and cost effectiveness.
In most engineering and scientific applications, machine learning (ML) or artificial intelligence (AI) methods in general, are primarily oriented to design a statistical/heuristic procedure to predict the outcome of a system under new conditions. This mechanism aims at exploring non-evident correlations between inputs and outputs that are embedded in the data. However, a large body of this effort relies on black-box function approximations (e.g., neural networks) that have shown limitations to elucidate additional insights from the underlying physical process that generated the data. Thus, this type of knowledge is generated in a data-driven manner without fully explaining the physics governing the problem.