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The AAPG/Datapages Combined Publications Database

New Orleans Geological Society


New Discoveries Point to a Bright Future: South Louisiana Onshore Petroleum Exploration Symposium, May 22, 2003
Pages 21-21

Progressive Previous HitSeismicNext Hit Data Mining for Reservoir Characterization: Lake Theriot 3-D Survey, Terrebonne Parish, Louisiana [Abstract]

Uwe Strecker*, G. Taylor, M. Smith, M. Pou


Previous HitSeismicNext Hit interpreters are required to work with larger and larger Previous HitseismicNext Hit volumes as the amount of Previous HitseismicNext Hit data we acquire and process continues to increase. Rapid advances in Previous HitseismicNext Hit attribute methods further increase our data-set sizes by providing many coincident Previous HitseismicNext Hit attribute volumes for each data set.

These exponential increases in available data represent huge data management and data interpretation challenges to our industry. There are clear similarities between the Previous HitseismicNext Hit exploration industry and the Internet in terms of the volume of information that is available for analysis, and therefore it makes sense to deploy data mining tools and methodologies developed for other industries to address the needs of the oil and gas exploration business. Here we employ some aspects of the data mining workflow to enrich and discover knowledge about possibly productive regions within a 3-D Previous HitseismicNext Hit data volume from South Louisiana.

Previous HitSeismicNext Hit data mining is applied to multiple Previous HitseismicNext Hit attribute volumes calculated from a 3-D dataset acquired for the Lake Theriot area, Terrebonne Parish, South Louisiana. Various instantaneous and geometric Previous HitseismicNext Hit attributes are used during data reduction for the rapid delineation of a possibly prospective, faulted subsurface channel system and the Previous HitseismicNext Hit properties of its sediment fill. Additionally, selected Previous HitseismicNext Hit attributes of the propagated wave field can be recombined mathematically to produce an algorithm that encapsulates geophysically descriptive aspects of subsurface Previous HitseismicNext Hit facies. Previous HitSeismicNext Hit attributes including variance of angle, time variance of instantaneous frequency, and variance of similarity, combine to form the "Shale Indicator". This is a "hybrid" Previous HitseismicNext Hit attribute that integrates certain depositional characteristics of shales, such as lateral continuity, thin-bed layering, and parallelism of bedding, in an effort to seismically differentiate "Previous HitseismicNext Hit shales" from "Previous HitseismicNext Hit non-shales."

Additionally, application of neural network technology generates a single attribute volume of the multi-attribute response illuminating discrete Previous HitseismicNext Hit facies. The results of this study demonstrate the value of applying data mining techniques to Previous HitseismicTop data volumes to rapidly establish zone prospectivity, thereby mitigating future drilling risk.

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