--> Abstract: Application of n-dimensional Log Analysis in Predicting Reservoir Properties from Core Data in both Cored and Un-cored Wells in Tight Gas Reservoirs, by Handwerger, David, Roberto Suarez-Rivera, Tim Sodergren, Mary Milner, and Keith Greaves; #90071 (2007)

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Application of n-dimensional Log Analysis in Predicting Reservoir Properties from Core Data in both Cored and Un-cored Wells in Tight Gas Reservoirs

Handwerger, David, Roberto Suarez-Rivera, Tim Sodergren, Mary Milner, and Keith Greaves
TerraTek, a Schlumberger Company, Salt Lake City, UT

     Tight gas shales present interesting challenges to the interpretation of reservoir properties using logs. Unfortunately, none of these is easily addressable using conventional log analysis techniques. Most of the challenges stem from the fact that in unconventional reservoirs, the porosities are typically 7-8% or less, and the rock matrix has highly variable intrinsic properties associated with variability in silica, carbonate, the presence of other minerals, and most importantly, clay. As a result, the amount of variability in log responses caused by the rock matrix is high, and overwhelms the amount of variability associated to the materials of interest (gas, oil and water in the pore space). The latter is at best a secondary effect when compared with the variability of >90% of the volume investigated by logging tools – the matrix. To address these issues, we have employed n-dimensional cluster analysis techniques to define zones of statistically similar or dissimilar bulk log response, over multiple log dimensions, in an effort to separate the log signatures into zones of consistent material properties. These zones can then be integrated with core data to produce continuous vertical profiles of reservoir and mechanical properties measured in the lab. Additionally, using pattern recognition techniques, we can then identify these zones in other, non-cored wells throughout the field and use the zonal models to predict the reservoir and mechanical properties with a much higher degree of accuracy and interpret log responses in terms of petrologic and geologic properties. This leads to much better understanding of the gas in place as well as the fraction of the reservoir that is most producible.

 

AAPG Search and Discovery Article #90071 © 2007 AAPG Rocky Mountain Meeting, Snowbird, Utah