Uncertainty Assessment of Saturation Modeling in Geocellular Models Using a Well Log Inversion Technique

Static or dynamic modeling of hydrocarbon reservoirs requires a detailed description of the initial capillary-gravity equilibrium saturations of each individual phase at reservoir conditions. Typically, this is a function of local rock quality and the presence of capillary forces. Without understanding these, both initial volumetric estimates and subsequent predictions are potentially meaningless. The main idea of a novel approach is to treat this challenge as a full-scale inversion problem of all suitable well and core data, similar to history matching. At first, a physical model is developed that describes the saturation anywhere in the reservoir and honors different rock types and reservoir regions. The resulting large set of parameters is initially based on core observations if available. An objective function then describes the mismatch between the simulated and observed saturations based on maximum likelihood theory. The model calibration is fully automatic using nonlinear solvers. The calibration will lead to the set of parameters which best fit core- and log-observed saturations. It can be populated in 3D geocellular models. A comprehensive statistical analysis of the results helps define the role of the individual physical processes, such as capillary pressure, as well as confidence intervals and correlations between coefficients.