Porosity-Permeability Relationships Using Linear Regression in Python

A short guide on applying a linear regression in Python to semi-log data .Porosity-Permeability Relationships Using Linear Regression in Python

Core data analysis is a key component in the evaluation of a field or discovery, as it provides direct samples of the geological formations in the subsurface over the interval of interest. It is often considered the ‘ground truth’ by many and is used as a reference for calibrating well log measurements and petrophysical analysis. Core data is expensive to obtain and not acquired on every well at every depth. Instead, it may be acquired at discrete intervals on a small number of wells within a field and then used as a reference for other wells.

Once the core data has been extracted from the well it is taken to a lab to be analysed. Along the length of the retrieved core sample a number of measurements are made. Two of which are porosity and permeability, both key components of a petrophysical analysis.

• Porosity is defined as the volume of space between the solid grains relative to the total rock volume. It provides an indication of the potential storage space for hydrocarbons.
• Permeability provides an indication of how easy fluids can flow through the rock.

Porosity is a key control on permeability, with larger pores resulting in wider pathways for the reservoir fluids to flow through.

Well logging tools do not provide a direct measurement for permeability and therefore it has to be inferred through relationships with core data from the same field or well, or from empirically derived equations.

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