Abstract
The gas solubility in water is one of the important influencing factors for foam rheology in porous media in foam flood processes for enhanced oil recovery (EOR). With regard to the application, numerical simulation plays a critical role in integrating geological, geophysical, and hydrological lab or field observations. In other words, validation of such processes can be based on lab analogues. Therefore, this type of exercise is necessary before making decision for permitting of foam flood projects.
In this study, fluid flow components of foam injection process are identified and quantified using well-constrained coreflood experimental data. The method allows estimating the foam evolution within complex numerical models. In particular, solubility features associated with injected gas at room temperature were first determined experimentally using a coreflood and X-ray tomography. Then, this problem is modeled with our in-house compositional reservoir simulator employing different foam models with varying complexity. These include local-equilibrium approximation considerations based on population balance and a table-look-up approach modeling of the fully coupled fluid flow and mass transport equations on different grid systems. A three-phase flash cubic equation of state (EOS) is used to allow for mutual solubilities of water, oil, and gas in all three phases. Also, the finite-difference structured and un-structured grids and the finite-difference approximation of the pressure and component molar-balance equations are used in the simulator employing an implicit pressure and explicit phase saturations and compositions (IMPES) solution scheme.
We obtained a fairly good match between the experimental and the simulation results confirming that we can use the simulator to predict the physical and chemical behavior of Foam-Assisted-EOR systems. With respect to the oil recovery, the simulation results indicate that unlike CO2, N2 foam produces more oil. It is also shown that the key parameters in model development include compositional EOS, the choice of solver and solution algorithm, grid discretization, and fluid flow characteristic curves.
This method is particularly important since it allows for the easy identification and evolution of the spread of foam plume. Further, this framework offers a guide for inclusion of existing knowledge on foam into practical numerical simulator.
