Permeability Estimator
Estimate permeability from porosity and irreducible water saturation using five empirical correlations. Results in millidarcies (mD).
Input Parameters
Permeability Estimates
| Method | Formula | k (mD) |
|---|
Geometric Mean of All Methods
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Permeability vs. Porosity — All Methods
How this was calculated
Timur (1968): k = 0.136 × φ4.4 / Swi2. Derived from NMR and core data.
Morris-Biggs Oil: k = 62,500 × φ6 / Swi2. Empirical for oil-bearing sands.
Morris-Biggs Gas: k = 6,241 × φ6 / Swi2. Empirical for gas-bearing sands.
Coates-Dumanoir: k = 6,500 × φ4 × ((1−Swi)/Swi)2. Also known as Coates equation.
Wyllie-Rose: k = 250 × φ3 / Swi. General empirical correlation.
Assumptions: Clean sandstone reservoir. Swi from log or capillary pressure data. These are screening estimates only.
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Permeability is among the most difficult reservoir properties to measure accurately from well logs. Unlike porosity, which can be measured directly by multiple logging tools, permeability depends on pore geometry, connectivity, and the distribution of fluids within the pore space. Empirical correlations relating permeability to porosity and irreducible water saturation provide useful screening estimates.
The Timur equation is one of the most widely used, originally derived from NMR and core data. The Morris-Biggs correlations differentiate between oil- and gas-bearing formations. The Coates-Dumanoir equation introduces the ratio of free fluid to bound water, while the Wyllie-Rose correlation provides a simpler power-law relationship.
These correlations should be calibrated against core measurements whenever possible. In heterogeneous reservoirs or tight formations, log-derived permeability estimates can differ from core values by orders of magnitude.
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