Relative permeability normalization - effects of permeability, wettability and interfacial tension

The relative permeabilities (kr) are crucial flow functions governing the fluid distribution within and production from the petroleum reservoirs under various oil recovery methods. To obtain these important reservoir parameters, conventionally, it is required to take rock samples from the reservoir and perform appropriate laboratory measurements. Although, kr is expressed as a function of fluid saturation, it is now well-known that kr values are affected by; pore structure and distribution, absolute permeability, wettability, IFT, and saturation history. These rock/fluid properties often change from one region of the reservoir to another but it would be impossible to perform kr measurements for all regions of a reservoir. Generally, performing experiment on a core with higher permeability is faster and easier than a low permeability rock. Therefore, assuming all other parameters such as wettability, IFT, and displacement direction are the same for two rocks with different permeability, the question is, how do we estimate the kr of a rock with lower permeability from available (measured) kr of a higher permeability rock? How do we account for wettability and IFT differences? Normalization techniques have been proposed to remove the effect of irreducible water and trapped saturations, which would be different under different conditions. The relative permeabilities can then be de-normalized and assigned to different regions (rock types) of the reservoir based on their own irreducible water and trapped saturations. The objective of this study is to predict the kr for a new rock/fluid conditions (i.e., permeability, wettability, IFT) using existing kr data measured at different conditions. Using measured data from coreflood experiments, we show that by applying an appropriate normalization technique one can adequately predict kr of rocks with different permeability and wettability conditions in two-phase flow. However, the results show that the effect of IFT change cannot be captured by normalization techniques. To improve the methodology, a new hypothesis is introduced and proposed here based on Dynamic Trap Saturation. Finally, using our experimental data, we evaluate the validity of Coats IFT scaling method. We demonstrate the shortcomings of the method and offer an improvement to its prediction.