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    Time-lapse density prediction for reservoir characterization using probabilistic neural networks at Postle Field, Texas County, Oklahoma

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    Time-lapse density prediction ...
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    Author
    Vega Diaz, Andrea C.
    Advisor
    Davis, Thomas L. (Thomas Leonard), 1947-
    Date issued
    2012
    Date submitted
    2012
    Keywords
    time-lapse
    neural networks
    characterization
    density
    reservoir
    Hydrocarbon reservoirs -- Mathematical models
    Geophysical well logging -- Mathematical models
    Neural networks (Computer science)
    Seismic prospecting -- Oklahoma -- Texas County
    
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    URI
    https://hdl.handle.net/11124/76641
    Abstract
    At Postle Field the main challenge has been to map the Morrow A sandstone. By using a non-linear approach to predict density values from p-wave seismic. I was able to discriminate the reservoir sandstones and identify areas of high quality reservoir. My work confirms the connectivity of the two northern sandstone bodies, increasing the potential resource volume in the study area. The baseline density prediction showed the dry wells were drilled in areas of poor reservoir quality. With the results of this work future drilling locations can be located with less uncertainty. The application of time-lapse neural network prediction successfully predicted changes in the reservoir. Analysis of reservoir modeling and simulation suggest that the time-lapse density changes shown in the neural network prediction at Postle Field are related to pressure changes in the reservoir. Two areas of high quality reservoir have been identified and proposed for future drilling programs. Further engineering evaluation is recommended.
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