Asphaltene deposition
during oil production is a major flow assurance
problem. The asphaltene deposit layer reduces the pipe cross-section,
leading to a significant reduction in the flow rate and eventually
plugging the pipeline. This flow assurance problem caused during oil
production has motivated the development of several experimental and
modeling techniques to investigate the asphaltene behavior. This study
proposes an integrated approach to simultaneously model asphaltene
precipitation, aggregation, and deposition on a single platform. It
focuses on the development of a deposition simulator that performs
thermodynamic modeling using the perturbed chain version of the statistical
associating fluid theory equation of state (PC-SAFT EOS) and depicts
the deposition profile by means of a computational fluid dynamics
(CFD) model based on the finite element method. In this work, the
asphaltene deposition risk was assessed in the near-wellbore region
and the production tubing as a result of gas breakthrough. To achieve
this goal, a sample of crude C2 was analyzed to determine its properties
and also the tendency of the asphaltenes contained in this sample
to precipitate and deposit under various conditions. Laboratory-scale
experiments were performed to analyze the rates of asphaltene precipitation,
aggregation, and deposition. With the results obtained from the various
experiments, advanced modeling methods based on PC-SAFT EOS and CFD
models were calibrated and used to predict asphaltene precipitation
and deposition under field conditions. Simulation methods for oil
flow and asphaltene precipitation in the near-wellbore region of the
reservoir and inside the production tubing were coupled to provide
the most rigorous modeling approach ever developed to understand and
predict this complex flow assurance problem. The results show a low
to moderate asphaltene deposition rate produced by crude C2 as the
gas breaks through. Nevertheless, further investigation is recommended
to analyze the effect of other fluids that may be co-produced to enhance
our ability to understand and predict asphaltene deposition under
different conditions.