an investigation into the effect of fracture opening on empirical foam parameters

Foam is a demonstrated technique for improving gas mobility through trapping gas bubbles. it has been indicated that foam is a complex colloidal structure, and its performance depends on various parameters. however, most of the available studies in the literature have been conducted in the core/sandpack media, and only a handful of evaluations can be found for foam in the fracture. in this study, a supervised learning algorithm is regenerated to examine the effect of fracture opening/permeability on stars foam model parameters. three previously carried out quality-scan experiments at different fracture networks are selected as the algorithm input data. low fmdry values, which represent a concept called limiting capillary pressure, were obtained for the fractures. this indicates that foam can survive in fractures at lower water saturation, compared to the matrix media. the modeling results are compared with the output previously obtained in rocks. it is indicated here that each parameter shows a different trend against fracture permeability. moreover, foam exhibits shear-thickening behavior in all cases.