The permeability of a pore structure is typically described by stochastic representations of its geometrical attributes (e.g. pore-size distribution, porosity, coordination number). Database-driven numerical solvers for large model domains can only accurately predict large-scale flow and reactive behaviour when they incorporate upscaled descriptions of that structure and its evolution. This is particularly challenging for rocks with multimodal porosity structures such as carbonates, where several different type of structures (e.g. micro-porosity, cavities, fractures) are interacting. It is the connectivity both within and between these fundamentally different structures that ultimately controls the porosity-permeability relationship at the larger length scales. Recent advances in machine learning techniques combined with both numerical modelling and informed structural analysis have allowed us to probe the relationship between structure, reaction, and permeability much more deeply. We have used this integrated approach to tackle the challenge of upscaling flow and reaction in rocks with complex pore structures with a combination of advanced pore-scale modelling and machine learning.

The permeability of a pore structure is typically described by stochastic representations of its geometrical attributes (e.g. pore-size distribution, porosity, coordination number). Database-driven numerical solvers for large model domains can only accurately predict large-scale flow and reactive behaviour when they incorporate upscaled descriptions of that structure and its evolution. This is particularly challenging for rocks with multimodal porosity structures such as carbonates, where several different type of structures (e.g. micro-porosity, cavities, fractures) are interacting. It is the connectivity both within and between these fundamentally different structures that ultimately controls the porosity-permeability relationship at the larger length scales. Recent advances in machine learning techniques combined with both numerical modelling and informed structural analysis have allowed us to probe the relationship between structure, reaction, and permeability much more deeply. We have used this integrated approach to tackle the challenge of upscaling flow and reaction in rocks with complex pore structures with a combination of advanced pore-scale modelling and machine learning.