Anchoring Multi-Scale Models to Micron-Scale Imaging of Multiphase Flow in Rocks

Image-based pore-scale modeling is an important method to study multiphase flow in permeable rocks. However, in many rocks, the pore size distribution is so wide that it cannot be resolved in a single pore-space image, typically acquired using micro-computed tomography (micro-CT). Recent multi-scale models therefore incorporate sub-voxel porosity maps, created by differential micro-CT imaging of a contrast fluid in the pores. These maps delineate different microporous flow zones in the model, which must be assigned petrophysical properties as input. The uncertainty on the pore scale physics in these models is therefore heightened by uncertainties on the representation of unresolved pores, also called sub-rock typing. Here, we address this by validating a multi-scale pore network model using a drainage experiment imaged with differential micro-CT on an Estaillades limestone sample. We find that porosity map-based sub-rock typing was unable to match the micrometer-scale experimental fluid distributions. To investigate why, we introduce a novel baseline sub-rock typing method, based on a 3D map of the experimental capillary pressure function. By incorporating this data, we successfully remove most of the sub-rock typing uncertainty from the model, obtaining a close fit to the experimental fluid distributions. Comparison between the two methods shows that in this sample, the porosity map is poorly correlated to the multiphase flow behavior of microporosity. The method introduced in this paper can help to constrain the sources of uncertainties in multi-scale models in reference cases, facilitating the development of simulations in complex reservoir rocks important for for example, geological storage of CO2.

Automated summary

Image-based pore-scale modeling is an important method for studying multiphase flow in permeable rocks. However, the wide distribution of pore sizes in many rocks cannot be resolved in a single image. Recent research has incorporated sub-voxel porosity maps and introduced a baseline sub-rock typing method to reduce uncertainty in the models.