Intelligent well completions are increasingly being used in horizontal, multilateral, and multi-branching wells. Such completions are equipped with permanent sensors to measure temperature and pressure profiles, which must then be interpreted to determine the inflow profiles of the various phases produced that are needed to characterize the well??s performance. Distributed temperature measurements, using fiber optics in particular, are becoming increasingly more often applied. The value of an intelligent completion hinges on our capability to extract such inflow profiles or, at a minimum, to locate the entry locations of undesirable water or gas entries. In this research, a model of temperature behavior in multilateral wells was developed. The model predicts the temperature profiles in the build sections connecting the laterals to one another or to a main wellbore, thus accounting for the changing well angle relative to the temperature profile in the earth. In addition, energy balance equations applied at each junction predict the effects of mixing on the temperature above each junction. The multilateral wellbore temperature model was applied to a wide range of cases, in order to determine the conditions for which intelligent completions would be most useful. Parameters that were varied for this experiment included fluid thermal properties, absolute values of temperature and pressure, geothermal gradients, flow rates from each lateral, and the trajectories of each build section. From this parametric study, guidelines for an optimal application of intelligent well completion are represented.