Cogent prognostic of mellowed wax ubiety temperature of crude oil in the field of study has been a major challenge the four wells were commissioned in 2002. This may be due to the wrong application of conventional techniques of perhaps non- effective wax inhibitors, pipeline insulations, pipeline cleaning, heating and pigging capacity issues, higher cost maintenance and monitoring. The current wax models have numerous short comings in the estimation of simple modification of complex classical wax models and to accurately describe percentage wax solid weights and solid phase trends at controlled temperatures and pressures. Simulation constraints of available crude oil wax simulators are incapacitated by their inconsistent ability to delineate wax appearance patterns at different temperatures with minute time. Non validation of data used with field data makes wax prediction and management difficult. The aim of this study is to compare laboratory results with simulated wax appearance models using the Won original, Won with Sol Params, Chung modified and Pedersen wax models and predict new simplified models from a Field in the Niger Delta. The objectives are to develop improved correlations from the cumulative models for the percentage (%) wax amount (weight) and solid phases at different temperatures using laboratory and mathematically simulated results. To compare results of modified and conventional wax models and technically describing the patterns identified. Four samples of waxy crudes in the Niger Delta were collected intermittently for a period of 16 weeks after pigging. They were characterized, tested and cooled. The wax deposit was scrapped and solid weights and phases were measured at a range of 40 – 100°F and controlled pressure range of 250 to 1200psi. This was used as the controlled data and inputted into the PVTP-IPM mathematical simulator. Results of this modified wax model for solid phase gave Mod Sph = -0.0704T + 6.7536 with R2 = 0.9736. This shows similar pattern (inverse straight line) with the won with the Sol Params and Won original but with high variance from the Pederson wax model due to its higher pour point. Results of Modified Wax model for Solid weight gave Mod Swt = -0.2899T + 28.293 with R2 = 0.987 gave similar patterns greater divergence of the Pederson model. This disparity may be due parameters in the model and wax types. Comparison of the model gave good matches with appreciable square of regressions. Field results showed improved wax predictions, crude oil flow, monitoring and management. The validity of this model is however hinged on the temperature and pressures with PVT data applied while lab results are in consonant with the model results. Wax challenges must be thoroughly examined and the linearization modeling pattern approach embraced and perhaps should deviate from a scholarly exercise but an industry energizer which is strongly recommended as a technical guide to predict, control, interpret and effectively manage wax precipitations.
