
%0 Journal Article
%A Bathellier, E. 
%A Downton, J. 
%A Sena, A. 
%T Optimizing CSG development: Quantitative estimation of lithological
and geomechanical reservoir quality parameters from seismic data
%J ASEG Extended Abstracts
%D 2012/04/01
%V 2012
%N 1
%P 1-4
%K 
%R https://doi.org/10.1071/ASEG2012AB151
%U https://www.publish.csiro.au/paper/ASEG2012AB151 
%X Work over the last decade on seismic azimuthal anisotropy has identified a link between fracture density and orientation observed by well logs and the intensity and orientation of the observed anisotropy. Recent work has correlated these measurements to provide quantitative estimates of fracture density from 3D wide-azimuth seismic data for tight gas sands. The work highlights the impact of advanced seismic processing in successfully recovering reliable fracture estimates which correlate well with borehole observations. These kind of areal, quantitative estimates of fracture density provide a valuable tool to guide drilling and completion programs in tight reservoirs. Building upon this work and considering Coal Seam Gas plays in particular we need to consider some additional reservoir quality parameters whilst trying to impose the same quantitative approach on the interpretation of seismic data and correlation with borehole logging observations. The characterization of CSG plays involves the understanding of the reservoir matrix properties as well as the in-situ stresses and fracturing that will determine optimal producing zones. Pre-stack seismic data and azimuthal WAZ (wide azimuth) seismic processing can help in the identification of sweet spots in CSG resource plays through detailed reservoir-oriented gather conditioning followed by prestack seismic inversion and multi-attribute analysis. This analysis provides rock property estimates such Poisson?s ratio, and Young?s modulus, amongst others. These properties are in turn related to quantitative reservoir properties such as porosity and brittleness. In this presentation we show an integrated approach based on pre-stack azimuthal seismic data analysis and well log information to identify sweet spots, estimate geomechanical properties and in situ principal stresses. 
