Abstract

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.

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