Wax deposition on inner surfaces of pipelines is a costly problem for the petroleum industry. This flow assurance problem is of particular interest during the production and transportation of waxy oils in cold environments. An understanding of known mechanisms and available thermodynamic models will be useful for the management and planning of mitigation strategies for wax deposition. This paper presents a critical review of wax prediction models used for estimation of wax deposition based on chemical hydrocarbon compositions and thermobaric condition. The comparative analysis is applied to highlight the effective mechanisms guiding the wax deposition, and how this knowledge can be used to model and provide solutions to reducing wax deposition issues. One group of thermodynamic models assume that the precipitated wax is a solid solution. These models are divided into two categories: ideal (Erickson and Pedersen models) and non-ideal solutions (Won and Coutinho models). In the other group of models, the wax phase consists of many solid phases (Lira-Galeana model).

The authors summarized the limitations of the models, evaluated, and identified ways to represent the overview of existing thermodynamical models for predicting wax precipitation.

Within the strong demand from industry, the results of this manuscript can aid to aspire engineers and researcher.

Keywords:
scale remediation, oilfield chemistry, asphaltene remediation, wax remediation, hydrate remediation, hydrate inhibition, machine learning, wax inhibition, neural network, remediation of hydrates
Subjects:
Production Chemistry, Metallurgy and Biology, Inhibition and remediation of hydrates, scale, paraffin / wax and asphaltene
Copyright 2021, Society of Petroleum Engineers
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