One important challenge working in heavy oil field development is to present a set of production profiles representing low, base and high production without being overly optimistic or pessimistic. The paper give examples on how various methods of assessing the uncertainty in the relative permeability curve shapes changes the view on project uncertainty. Heavy oil laboratory relative permeability experiments face many well-known challenges. The core data is usually unconsolidated with very high permeability. This combined with high viscosity usually leads to unfavourable laboratory conditions like in example capillary end effects and viscous fingering. Typical core issues are fractures, laminations, loose sand, issues with use of multiple core plugs and very low pressure differential at initiation of the experiment. In addition there is always a discussion about reservoir wettability and core history including impact of drilling operations, drilling mud and core depressurization. The total effect is that the laboratory data is often seen as highly unreliable. The challenges are usually solved by using either light oil or by using steady state together with unsteady state experiments. Weaknesses and strength of the steady state method is addressed. Discussion of the alternative methods that is available is given with examples. Can light oil really be used instead of heavy oil given the large compositional differences? What could the impact of asphaltene content be? Examples are given of some typically laboratory effects. A brief discussion is given regarding possible reasons for differences between light and heavy oil behaviour. A benchmark of relative permeability experience from 50 heavy oil fields is presented. The benchmarking methodology is given and explained. Strength and weaknesses of the method is discussed. Actual field data is shown. Examples of relative permeability data used in simulation models are shown. Impact of grid size and properties on relative permeability is shown. Use of rock curves and alternative curve fit methods is presented with weakness and strengths.