Two- and three-phase flow functions for numerical simulation of EOR processes
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The understanding of governing mechanisms of multi-phase (oil, water, and gas) flow in porous media is of keen interest in petroleum and environmental engineering. In the petroleum engineering context, three-phase flow occurs in several important processes including in enhanced oil recovery (EOR). Recovery of a significant amount of the residual oil in reservoirs after primary recovery and secondary recovery (waterflooding) is important in order to tackle the increasing demand for the energy. EOR methods mainly involve two and three-phase flow in the reservoir. Relative permeability (kr) and capillary pressure (Pc) are two important parameters in multiphase flow which describe the interaction of each fluid in porous media. The importance of these flow functions will be even more significant for three-phase flow systems. This thesis attempts to address three key issues. (i) Improved determination of multi-phase flow functions (kr and Pc). (ii) The impact of parameters affecting flow functions. (iii) Prediction of multi-phase flow functions. Relative permeability (kr) can be measured in the laboratory using steady-state and unsteady-state methods, or estimated by mathematical correlations and pore-network models. As multi-phase flow experiments and in particular steady-state measurements are very time consuming and expensive, more often the unsteady-state method is used for multi-phase kr measurements. In this thesis, a methodology has been devised for calculating kr values and in particular three-phase kr from unsteady-state experiments. The effort was extended to simultaneously calculating Pc from the same coreflood experiment. There are different physical parameters which can affect flow functions. The effect of gas/oil interfacial tension (IFTg/o) on two and three-phase kr and also on residual saturation during alternative water and gas injections has also been studied. Finally, two-phase kr have been estimated for rock and fluid conditions where there is no previous data. This has been achieved by taking data from different conditions under which measurements were made.