Using Cementation Exponent to Improve Permeability Estimation from Porosity in a Deep Saline Reservoir
Correlating (log10) permeability with porosity is a well-established technique, particularly for intergranular clastic reservoirs. However, as permeability is a function of multiple factors including grain size, grain shape, sorting, packing arrangement, and cementation, this correlation works best when the a single rock type of varying porosity is correlated. When there is significant variation in the characteristics of a formation, then a single porosity-permeability correlation may be insufficient to capture the variation in permeability. Instead, it may be necessary to identify and derive multiple correlations between porosity and permeability.
As part of reservoir characterization for a CO2 storage project at a site in Decatur, Illinois, a methodology was developed to estimate permeability from basic geophysical log properties in a 100% brine saturated reservoir. Geologic characterization efforts have determined there are important geologic variations within the formation which in turn affect the petrophysical properties of the Mt. Simon. A semilog relationship between core porosity and permeability was insufficient because permeability was typically 2–3 orders of magnitude different for a single porosity value. Consequently this relationship was refined by classifying the porosity-perm relationships by grain size of the sidewall core samples. However, to derive permeability from well log porosity required an indicator of grain size that could be derived from basic log suites. After trying various parameters (e.g. volume of shale), the cementation exponent was used as a proxy for grain size and was used to determine which of the porosity-permeability correlations to apply at each half-foot interval of the well log. This methodology was successfully applied to wells drilled in the Mt Simon sandstone at the study site.
The method was used a part of the geocellular modelling of the site. The grain size categories were modelled as indicator values and porosity distributed using co-simulation with the grain size values. Permeability was then transformed from the porosity using the five different correlations. As a comparison, porosity was simulated using standard techniques and a single correlation was applied. Using the different permeability-porosity correlations produced considerable more heterogeneity within the model versus a single correlation.
AAPG Datapages/Search and Discovery Article #90218 © 2015 Eastern Section Meeting, Indianapolis, Indiana, September 20-22, 2015