Oil and gas transport is today a vital part of the industry. Oil cooled during transport in pipelines may precipitate paraffin wax. Precipitated wax may deposit on pipe walls and cause flow restrictions. Deposition models are used to understand and predict deposition of solids. A deposition model can help predict wax problems before a pipe line is set into operation. If the amount of deposited wax is predicted it can help operators to develop removal plans and strategies. A total of 21 wax deposition experiments performed by others were digitized and evaluated. The logarithmic deposition-release model showed a good match with 18 of the experiments. The experiments tested the effect of varying flow rate, temperature or both. Most experiments behaved as expected when flow rate and temperature were varied. The deposition-release model consists of two coefficients, k1 and k2. Both coefficients were evaluated against wall shear stress for the varying rate experiments. The coefficients in the varying temperature series were evaluated against the temperature driving force. Linear trends between most coefficients and physical parameters were found. These linear trends lead to the development of four models that predict wax deposition. The models use either wall shear stress, the temperature driving force or both as an input. All models produce similar results. Each model was based on an experimental series. A study of a real pipeline with wax deposition was also investigated. Temperature and viscosity calculations matched well with values used in the study. The study reported calculated wax thickness based on measurements of pressure drop. The pressure drop method was evaluated and explained. The method does not consider an altered pressure drop due to increased pipe roughness and non-evenly distribution of deposits. Both of these effects will increase the pressure drop. It was found that neglecting these will cause the calculated thickness to be overestimated. Because of the overestimation of thickness it was hard to get an accurate match with models.