Any
change in the crude oil system equilibrium causes asphaltene
precipitation, aggregation, and finally deposition on rock or pipe
surfaces. Treatment cost and production lost time associated with
deposition have a huge impact on production revenue. Therefore, developing
a model that can reliably predict the asphaltene deposition behavior
is essential. Although there are several deposition models proposed
in literature, however, they lack precision and are mostly based on
outdated assumptions that do not correctly capture the physics behind
asphaltene deposition reported in the last decade. In addition, available
models excessively rely on tuning parameters that are purely mathematical
parameters with no experimental background or known dependency on
influential parameters. It gives their model unnecessary flexibility
and can mostly be used to regenerate the deposition processes in which
field data are present. The current study attempts to develop a model
based on recent research findings that can resolve the shortcomings
of the previous models. The developed model treats asphaltenes as
a polydisperse system and tracks their behavior (precipitation, advection,
diffusion, aggregation, breakage, and deposition) along the flow path.
It can successfully simulate the experimental capillary deposition
tests and is able to predict particle size distribution, which denotes
capturing aggregates behavior at the microscopic level. It explains
that the temperature increment, mainly by increasing interaction frequency
of aggregates with the deposit surface, can accelerate the deposition
process. On the other hand, the composition change depending on how
it affects medium stability and viscosity can either enhance or decelerate
the deposition process.