A Simplified Four Phase Compositional Simulator for Asphaltene Evaluation

One of the most promising Enhanced Oil Recovery (EOR) methods is CO2 injection. However, if the oil contains asphaltenes, CO2 injection may cause asphaltene precipitation and introduce production related challenges. Conventional three-phase (gas/oil/water) compositional simulators are unable to predict precipitation of asphaltenes and multiphase compositional simulators are required. The use of detailed multiphase equilibrium calculations is very CPU intensive and commercial simulation packages often employ a hybrid model that may not capture the true physics at play. Conflicting findings have been reported from experimental and theoretical studies: Some studies show that Asphaltene deposition, due to CO2 injection, takes place near the injection well, while others have reported that asphaltene deposition occurs near the production well. True multiphase equilibrium calculations can be used to demonstrate that both findings are possible and that many factors will affect the deposition behavior. Accordingly, a general statement such as CO2 injection causes more asphaltene precipitation relative to hydrocarbon (HC) gas injection is not always true. This added complexity indicates the need for multiphase compositional simulation to delineate asphaltene deposition behavior and quantity. In this work, we propose a four-phase compositional simulator (gas/oil/asphaltene/water) to predict the asphaltene precipitation during CO2 and HC gas injection processes. A new hybrid formulation, based on a simple table look-up approach, is introduced to replace detailed multiphase calculations (gas/oil/asphaltene) at a CPU requirement that is comparable to two-phase (gas/oil) equilibrium calculations. A range of simulation models/scenarios are presented to test and validate the new formulation against detailed multiphase compositional simulation, and we demonstrate an excellent agreement between the hybrid model and the full multiphase calculations. The proposed approach is easy to implement in commercial tools and provides a path to allow for more detailed studies of asphaltene precipitation and related production challenges.