Porosity Permeability (Poro-Perm) Log-Linear Regression in Python - Petrophysics

Permeability is one of the key reservoir properties we as petrophysicists attempt to derive as part of our workflow. As well logging tools do not provide a direct measurement for permeability, we have to infer it through relationships with core data from the same field or well, from empirically derived equations or NMR data.
One common method of deriving permeability is to plot core porosity (on a linear scale) against core permeability (on a logarithmic scale) and observe the trend.
From this, a regression can be applied to the porosity permeability (poro-perm) crossplot to derive an equation. This can subsequently be used to predict a continuous permeability from a computed porosity in any well.
In this week's video, I demonstrate the application of linear regression within Python to derive a poro-perm relationship. However, when doing this we need to account for permeability being logarithmically scaled and porosity being linearly scaled.
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