Carbon dioxide corrosion of carbon steel in brine has been a recognized problem in oil production and it is becoming more common with the use of CO2-flooding as an oil recovery technique. The presence of acetic acid is systematic in oil fields, however, its role in corrosion has not been well-recognized and it is still debated. The present work was carried out, first, focusing on an understanding of CO2-corrosion of low-carbon steel and, later to ascertain the effect of acetic acid. This understanding is achieved by developing a deterministic model that explains and accounts for the experimental observations from Electrochemical Impedance Spectroscopy (EIS) data. The impedance model, based on the Point Defect Model, provides a good account of the formation and growth of a bi-layer that comprises an oxide (inner) layer adjacent the surface and a precipitated (outer) layer of siderite, FeCO3, forming on the top of the inner layer.
This study was developed to provide an optimum methodology for designing metal alloys. Information on general and localized (pitting and crevices) corrosion of the most commonly used Nickel alloys were obtained from the National Institute of Standards and Technology (NIST). The pH, temperature, and major ions in the electrolyte used in each experiment, for which data were collected, were used to define the environment. The alloy composition, as given by its UNS number and the corrosion rates for general corrosion and the pit depths and duration of the experiments for localized corrosion of each sampleelectrolyte were collected in a single data vector. Accordingly, one experimental set corresponded to one vector. To efficiently cluster the data vectors by similarities, a publicly available Kohonen mapping software was used. Twodimensional maps were constructed to perform classification analysis. Kohonen maps have the ability to preserve the topological properties of the data i.e. similar vectors cluster together, while different vectors are separated on the map. A 2-dimentional Kohonen map was designed and trained with the available data. We constructed a 3D figure (2D Kohonen map and a third dimension containing the corrosion rates of each cell in the Kohonen map) for all alloy-environment couples in the data. Once the Kohonen map was trained, we "designed" new Nickel-alloys by choosing two of the alloys (C22 and Incoloy 825 (UNS: N008825, alloy 825)) on the Nickel NIST list (composition, environment and corrosion rates), and by systematically modifying the alloy compositions by adding minor alloy elements (one at a time). It was done such that the total weight percentage of the "newly designed" alloy added to 100% (Nickel weight percent was lowered when a different minor alloy was added). For the newly created alloy, we chose the same environment as that of the original alloys (C22 or Incoloy 825). For these "new designed" alloys, the corrosion rates were unknown. However, the Kohonen map-once it is trained--clustered the new alloys in a Kohonen cell containing the most similar vectors, for which an average corrosion rate already exists (of all the vectors stored in that cell). We locatedin the Kohonen map--the cells in which the original alloys (C22 and Incoloy 825) were stored; then, we located in which cells the "new alloys" are clustered. The alloys may migrate towards higher or lower corrosion rate cells. Accordingly, the beneficial or detrimental impact of adding those minor alloy elements in the alloys of origin (C22 or Incoloy 825) can be explored. Thereby, the "evolutionary road" of corrosion properties of metal alloys can be traced in the 3D figure and "If-then" scenarios for the new alloys -can be explored.
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