Crude unit overhead corrosion is a major issue in the refinery field. However, the corrosion models in the literature are difficult to apply to real refinery processes due to the characteristics of corrosion. We propose a Kriging model, an advanced statistical tool for geostatistics, to forecast the corrosion rate in a real refinery plant. Instead of spatial coordinates, the proposed model employs the non-spatial coordinates of six key corrosion variables: H2S, Cl., Fe2+, NH3, pH, and flowrate. The Kriging model is compared with two well-known forecasting models, multiple linear regression and an artificial neural network. To overcome the insufficiency of the number of data sets measured in the plant to use the six non-spatial coordinates, the significance probability is applied to reduce the dimensions from six to four. Among all the developed models in this paper, the Kriging model with four corrosion variables showed the best forecasting performance.