Multiphase flow in porous media is relevant in many areas of geoenergy engineering and is governed by relative permeability and capillary pressure saturation functions. These functions are key uncertainties in reservoir engineering and their measurement is demanding and resource intensive. Despite the experimental effort, it is not yet common practice to numerically interpret the data, nor is it common practice to investigate their uncertainty. Furthermore, data interpretation is limited to power-law functions, which is insufficient for describing complex rock types such as microscopically heterogeneous carbonates.
We developed a MATLAB-MRST-based simulator for simultaneous interpretation of data sets from different experimental techniques. We discuss the implementation of the common parametrized relative permeability representations and their deficiency to describe data from complex rocks. To overcome this limitation, a point-by-point approach is developed and applied to an extensive carbonate data set. For uncertainty analysis, a Markov Chain Monte Carlo sampling-based workflow is implemented and applied. The uncertainty is discussed in the frame of the individual data set, simultaneously analyzed data sets, and the sample-to-sample variation. The developed method makes the interpretation of relative permeability and capillary pressure saturation functions conclusive, especially in cases of complex line shapes, and is an essential step toward uncertainty-driven stochastic reservoir modeling.