Asphaltene deposition in one dimensional multicomponent flows

Abstract

In the petroleum industry, asphaltene deposition, as a complicated and not wellunderstood flow assurance problem, can occur at various stages of oil production and transportation leading to arterial blockage of wellbores, pipelines, separators and other facilities. The remediation and intervention of asphaltene deposition problems require a large amount of resources. This necessitates a comprehensive understanding and further prediction of asphaltene deposition. The present work intends to develop a numerical model to predict asphaltene deposition in wellbores/pipelines. This model consists of two modules: the CFD Module and the Thermodynamic Module. The CFD Module includes the modeling of: 1) Fluid Transport governed by the basic conservation laws of fluid mass, momentum and energy, 2) asphaltene Particle Transport governed by the material balance of various asphaltene species; and 3) Asphaltene Deposition governed by an m-th order deposition reaction. The Thermodynamic Module models Asphaltene Precipitation using the Peng-Robinson Equation of State (PR EOS) with Peneloux volume translation. In this work, those two modules are developed separately. To combine them together, a thermodynamic table for the oil of interest is generated by the Thermodynamic Module in advance of each simulation. During simulations, the CFD Module extracts the thermodynamic information from this table. In this work, the CFD Module and the Thermodynamic Module are first verified and validated separately. Then, the proposed model is used to investigate an actual oilfield asphaltene deposition problem. This case demonstrates that the proposed model can give reasonably accurate prediction of asphaltene deposit layer profile. More importantly, the proposed model is able to predict the effect of growing deposit layer on the flow fields (velocity and pressure). This study provides insights on coupling all involved processes: asphaltene precipitation, fluid transport, particle transport, and asphaltene deposition processes for modelling of asphaltene deposition in wellbores/pipelines.

Date of Award	May 2016
Original language	American English
Supervisor	Yap Fatt (Supervisor)