Carbonate rocks: Matrix permeability estimation

by Cardona, A., J. C. Santamarina
Year: 2019


Cardona, A., and J. C. Santamarina (2019). Carbonate rocks: Matrix permeability estimation, (in press; preliminary version published online Ahead of Print 27 June 2019): AAPG Bulletin, doi: 10.1306/05021917345.


Carbonate rocks store half of the world’s proven oil reserves. Genesis
and postdepositional diagenetic processes define the porous network
topology and the matrix permeability. This study compiles a database
of porosity, specific surface, mercury porosimetry, and permeability
values extracted from published sources and complements
the database through a focused experimental study. Specific surface
and porosity combine to estimate the pore size (Dsur). Permeability
versus Dsur data cluster along a single trend with a slope of 2 in a
log–log scale, which is in agreement with the Kozeny–Carman
model. Discordant data points correspond to samples with dual
porosity or broad pore-size distributions with long tails, where flow
channels along larger interconnected pores. Indeed, the detailed
analysis of all the porosimetry data in the database shows that
permeability correlates best with the pore size D80, that is, the 80th
percentile in pore-size distributions. Once again, the best fit is a
power function in terms of (D80)2, analogous to Kozeny–Carman.
The prediction uncertainty usingD80 is one order of magnitude and
has the same degree of uncertainty as more complex models and
analyses. This observation suggests an irreducible uncertainty of one
order ofmagnitude in permeability estimation from index properties
such as porosity,mercury porosimetry, and specific surface probably
resulting from specimen preparation effects, inherent physical differences
in permeation versus invasion, and difficulties in data interpretation.
These estimates of permeability are most valuable
when specimens are limited to small sizes, such as cuttings.


Carbonate rocks matrix permeability estimation