Permeability is one of the most important features for reservoir characterization. The correlation between permeability versus particle size distribution (PSD) and porosity is widely acknowledged in oil-sand reservoirs. PSD is a main physical property in oil-sands, and it is an important variable in many complex hydrological, geological, and geophysical processes. Developing a relationship for permeability estimation based on such fast-gathering and low-cost data, namely PSD and porosity measurements, offers a cost-effective alternative for permeability predictions, as well as surveying permeability variations in a large-scale study. In the literature, many investigations have been carried out for developing a relationship between permeability versus PSD and porosity (Arshad et al., 2019). However, the literature lacks the application of machine learning algorithms in developing such relationship.

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