Simultaneous Interpretation and Uncertainty Analysis of SCAL Data from Complex Rocks

Relative permeability and capillary pressure saturation functions are key uncertainties in reservoir engineering. Many resources are spent on measuring these functions in special core analysis (SCAL) for field developments. Despite the effort and time invested, it is not yet common practice to numerically interpret SCAL data, nor is it common practice to study the resulting uncertainty to reliably extract these two-phase saturation functions. Further developments are particularly important for complex rock types such as microscopically and macroscopically heterogeneous carbonates.
Here, we present a MATLAB-MRST-based simulator for simultaneous matching of SCAL data sets from different experimental techniques. We focus on the most common SCAL techniques, namely, steady-state relative permeability and centrifuge capillary pressure. We discuss the implementation of the common parametrized relative permeability representations and their deficiency to describe data from rather complex rocks such as carbonates. 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 (MCMC) 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, which is an essential step toward stochastic reservoir modeling.