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Unsupervised Classification of λρ-μρ Attributes Derived From Well Log Data in the Barnett Shale

Authors
Bradley C. Wallet (University of Oklahoma) | Roderick P. Altimar (DrillingInfo) | Roger M. Slatt (University of Oklahoma)
Document ID
SEG-2014-1586
Publisher
Society of Exploration Geophysicists
Source
2014 SEG Annual Meeting, 26-31 October, Denver, Colorado, USA
Publication Date
2014
Document Type
Conference Paper
Language
English
Copyright
2014. Society of Exploration Geophysicists
Keywords
shale gas, interpretation, logging, properties
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0 in the last 30 days
81 since 2007
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Price: USD 21.00

Summary

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.

File Size  625 KBNumber of Pages   5

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