Wax removal by pigging is costly in sub-sea oil production. Cost-effective scheduling of pigging can be achieved based on the deposition rate predicted by wax deposition models. Conventional wax deposition models predict wax deposition rates on the basis of Newtonian fluid mechanics. Such an approach can become invalid for highly waxy crude oils with non-Newtonian rheology. In this investigation, different simulation techniques, including large eddy simulation, Reynolds-averaged Naiver–Stokes equations, and the law of the wall, were applied to model non-Newtonian pipe flow. It was discovered that the law of the wall method is the best method to calculate the velocity profile, shear stress and the turbulent momentum diffusivity in turbulent non-Newtonian pipe flow of waxy oil. An enhanced wax deposition model considering the non-Newtonian characteristics of waxy oil using the law of the wall method was developed and applied to predict wax deposition rates in a field-scale pipeline.

Wax deposition poses severe risks to crude oil production systems. In order to remediate wax deposition, pigging operation is performed routinely to scrape wax deposits from the pipe wall. Proper determination of the pigging frequency is crucial to estimating the operating costs associated with the pigging operations as well as the risks of pipeline blockage by wax deposit. In order to predict the wax deposition rate and the deposit thickness to be pigged, existing wax deposition models simulate the hydrodynamics, heat and mass transfer of oil pipe flows based on Newtonian fluid mechanics. However, when temperature of the oil drops below the wax appearance temperature (WAT), wax molecules precipitate to form a suspension of wax crystals in oil, resulting in non-Newtonian fluid characteristics. In order to generate more reliable wax deposition predictions, the methodology to model the hydrodynamics, heat and mass transfer as well as deposit growth considering the non-Newtonian fluid characteristics needs to be developed. In this study, we present an improvement of the existing university-developed wax prediction model1 by incorporating the non-Newtonian fluid characteristics of waxy crude oil described by the suspension of fractal aggregates (SoFA) model. This enhancement is first presented for laminar flow regime. This improved model is then applied to provide insights on 1) the impacts of non-Newtonian characteristics on the heat and mass transfer aspects of wax deposition, 2) the effect of shear on wax deposition and 3) the role of wax inhibitors on wax deposition.

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An immersed quartz crystal resonator (QCR) was employed to assess the effectiveness of a modified alkylphenol resin in reducing asphaltene deposition on metal surfaces under various temperature and pressure conditions. The QCR response to asphaltene flocculation was first monitored during isothermal n-heptane titration experiments of dead crude oils with different contents of the asphaltene inhibitor (AI). In addition, the effect of the AI presence on the morphology of asphaltene deposits was analyzed by atomic force microscopy (AFM). Then, constant mass expansion experiments were carried out to determine whether the presence of the AI influenced the asphaltene instability pressure and the deposition rate of asphaltenes in a dead oil + CH4 system with various CH4 contents. The results of all these investigations in the nanometer range shed new light on the AI technology and clearly demonstrate that the presence of the AI can reduce the asphaltene deposition rate and modify the viscoelastic properties of the asphaltene solution in oil production conditions.