The ability to accurately predict the probability of fluid migration from depth through existing wells based on known well properties, such as age and depth, would be enormously helpful in understanding how migration pathways develop and the identification of potential migration without extensive field tests. The presence of fluid pathways is an important environmental issue because such pathways allow gas, either naturally occurring methane or sequestered CO₂, to move into the atmosphere. In this paper, we explore the ability of various predictive models to forecast gas migration at existing wells in Alberta, Canada, based upon the characteristics of existing deep wells. Alberta was selected as a case study because of the availability of data in an area that has required wells to be tested for pathway development after rig release since 1995. Wells that do not demonstrate pathway development require no further testing until the well is abandoned. We show that accurately predicting fluid migration requires detailed information on well construction, production, and fluid properties, and even then, the models considered in this study misclassify a large number of wells. This suggests other factors may contribute to pathway formation. Of the models investigated, random forests provide the best results on this data set, correctly identifying 78% of the wells used.