Development of shale reservoirs such as the Barnett Shale frequently includes the study of associated geomechanical and rock properties. Considerable effort has been placed into understanding properties that can be obtained from seismic data using inversion such as of λρ and μρ.. In most cases, studies of these properties are driven by theoretical understanding combined with careful analysis or core. In this abstract, we present a data driven approach based examining the statistical properties of λρ and μρ attributes obtained from well logs from the Lower Barnett Shale. Using an unsupervised learning approach to data clustering, we allow our data to speak for themselves, providing insight into the underlying data distribution. We then look at the rock proprieties of the discovered clusters to better understand the nature of the data.
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