Join us for a new esymposium with Roderick Perez on reservoir characterization and cluster analysis in unconventional reservoirs on September 29, 2011 at 2 pm CST. Based upon recent studies of the Barnett Shale in Texas, a systematic characterization workflow is necessary to do an accurate reservoir characterization. This should incorporate different tools such as: litho- and sequence-stratigraphy, geochemistry, petrophysics, geomechanics, well log, and 3D seismic analysis. This study will focus in the combination of λρ – μρ inversion with clustering analysis techniques in order to discriminate brittle zones in the Barnett Shale.

3D seismic characterization includes structural and stratigraphic mapping using seismic attributes; calibrating seismic characteristics to lithofacies and gamma ray parasequences (GRP’s) for seismic mapping purposes; and determining and mapping petrophysical properties using seismic inversion modeling. GRP gives us a good understanding of the depositional environment of the shale. On the other hand, petrofacies show the mechanical behavior of the rock in reservoir conditions. Clustering the vertical sequence will allow us to estimate the spatial distribution of the petrofacies.

On the other hand, post-stack seismic inversion was proving to be a very useful tool in order to enhance the seismic resolution of the internal reflectors in the Barnett Shale (Perez, 2008). The objective is to integrate analyzed properties into a geologically-realistic 3D stratigraphic model to better understand the fine-scale stratigraphy of shales and as an aid to improved horizontal well placement. Preliminary results show that the optimum gas shale properties have relatively low λ’s (incompressibility) and high µ’s (rigidity) that give rise to geo-mechanical brittleness capable of supporting extensive induced fractures. These results will be focus in the mechanical properties of the rock, instead of the depositional environment. Plotting λ vs. Young’s modulus allows discrimination between ductile and brittle shales in the area of study (Goodway, 2007). Comparing these results and the clustering analysis, we will be able to discriminate different production zones, and correlate these results with the zones having the best petrophysical and mechanical properties (TOC, porosity and permeability).

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