Predicting Wax Deposition in Oil Pipelines: A Computational Model Incorporating Heat and Mass Transfer Effects

A novel computational model is presented for predicting wax deposition in crude oil pipelines, accounting for the complex interplay of multiphase flow scenarios involving water-in-oil emulsions, wax precipitation kinetics, molecular diffusion, and shear dispersion. The governing equations are solved numerically by the bivariate spectral collocation method using Chebyshev-Gauss-Lobatto grid points. The model’s predictive capabilities are evaluated by investigating the impact of various flow parameters, including Reynolds number (Re), Grashof number (Gr), Schmidt number (Sc), and Weber number (We), on the flow variables, wall shear stress, and heat and mass fluxes. The key findings reveal that wax deposition is significantly influenced by the intricate interplay of flow conditions, wax precipitation kinetics, and heat and mass transfer phenomena. Notably, increasing Reynolds number from 2 to 6.5 leads to at most 5% increase in wax deposition, while increasing mass Grashof number from 4 to 16 results in at most 10% reduction in wax accumulation. Similarly, higher Schmidt numbers (Sc > 1) and Weber numbers (We > 1) tend to mitigate wax deposition by at most 15% and 6%, respectively. These insights offer valuable guidance for optimizing pipeline operations, designing effective wax control strategies, and enhancing pipeline integrity management in field-scale crude oil transportation systems.