The integrated approach developed in this work successfully captured the field results, while significantly reduced the required computational time.The PIPESIM simulator incorporates a wide variety of industry-standard multiphase flow correlations, as well as advanced 3-phase mechanistic models, including OLGAS, Kongsberg LedaFlow Point Model, and the TUFFP unified model.
Despite the higher amount of deposited asphaltenes predicted for casing production, the flow cross-sectional area remained higher compared with tubing production. For wellbore that produces through casing, asphaltenes deposition suddenly increases at the entrance then gradually decreases as the casing diameter changes. Applying different flow models revealed the dependency of the deposition behavior onto model choice. Approximately 20% difference in the asphaltenes deposition thickness after 60 days production is calculated between updating and fixed profile scenarios. In addition, the bubble point and the asphaltenes lower-onset pressures are predicted to occur closer to the surface.
The results show that failing to update the pressure, temperature and velocity profiles to capture the growth of the asphaltenes layer in the wellbore underestimates the asphaltenes layer thickness.
Two case studies of wells producing through their tubing and casing are analyzed. The deposition module is based on conservation laws for asphaltenes transport and is linked to the flow simulator to account for the impact of asphaltenes deposit layer on the fluid flow. The Peng-Robinson equation of state and the modified Miller-Flory-Huggins theory are used to calculate the thermodynamic properties of the oil and asphaltenes precipitation, respectively. This work develops an integrated approach combining thermodynamic and deposition modules with a multiphase flow simulator to simultaneously model asphaltenes precipitation and deposition in wellbores. Deposition of asphaltenes upon precipitation is a main flow assurance concern, which propelled the development of various experimental and modeling techniques to accurately predict its occurrence.
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