Workflow Development to Scale up Petrophysical Properties from Digital Rock Physics Scale to Laboratory Scale

Petrophysical rock properties are the crucial point of any reservoir characterization project and represent fundamental input parameters for any simulation. To obtain reservoir characterization data such as porosity, absolute and relative permeabilities, typically core analysis tests are needed. Unfortunately, there are cases where these tests cannot be accomplished. In these situations, digital rock physics (DRP) techniques are useful and may represent a powerful approach to obtain these parameters. Fluid flow at the pore scale can be simulated by DRP. To compare DRP results (micrometric scale) and laboratory tests (centimetric scale), the implementation of an upscaling method is required. In this context, this work aims to propose a novel methodology to allow the digital characterization of rock properties at the plug scale. In particular, the developed workflow exploits and combines different technologies: micro-CT scan, advanced image processing, machine learning, CFD numerical simulation. The first step of the methodology consists of acquiring micro-CT low-resolution scan of the entire core plug; then, machine learning techniques are applied to decompose the digital plug (derived by image processing on micro-CT scan) in reference element of volume (REV)-type equivalent blocks, determining the optimum number of REV type and their locations. One or several high-resolution 3D fine-scale images are used to derive the petrophysical properties of each REV type from individual fluid flow simulations at the pore scale. The resulting REV-type properties are then scaled up to the core plug scale. Finally, the scaled up results are compared to the results of core analysis tests. The overall methodology is validated on a heterogeneous carbonate rock.