PERMEABILITY PREDICTION FOR GEOSTATISTICAL CHARACTERIZATION OF A DEEP SALINE RESERVOIR

Geologic characterization for any project that deals with the deep subsurface, such as carbon sequestration, requires careful consideration of the heterogeneity of the target formation. One of the most crucial yet difficult properties to estimate is the permeability of the reservoir or aquifer. Generally, permeability is correlated with porosity using basic regression techniques with core analysis data. However, as permeability is a function of multiple factors including grain size, packing arrangement, and cementation, this method works best when the data is derived from homogenous material. When there is a change in properties, then a single porosity-permeability transform may not capture the variation in permeability as well. Therefore it may be necessary to identify and derive separate correlations between porosity and permeability based on other log derived attributes.

As part of reservoir characterization for a CO₂ sequestration project, a methodology was developed to estimate permeability for the entire length of a log in a 100% brine saturated reservoir. The method used Archie’s cementation exponent calculated from resistivity logs and core permeability via log porosity. A traditional semilog relationship between core porosity and permeability was refined by dividing the data into grain size categories which were correlated to the cementation exponent. This methodology was first applied to two wells drilled in the Mt Simon Sandstone within the Illinois Basin. The ability of the method to predict permeability in other wells from the same formation outside the study area will also be examined. Prior to the new data collection, the Mt Simon was considered to be relatively homogenous. However, geologic characterization efforts have determined there are subtle yet important variations within the formation.

The results of the method were then used in combination with geostatistics to characterize the reservoir architecture for reservoir modeling purposes. The results of the reservoir modeling were used to better understand behavior of CO₂ in the subsurface.