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An Integrated Geostatistical Approach: Constructing 3D Modeling and Simulation of St. Louis Carbonate Reservoir Systems, Archer Field, Southwest Kansas

By
Lianshuang Qi
Lianshuang Qi
Kansas Geological Survey, University of Kansa 1930 Constant Ave. Lawrence, Kansas 66047-3726 USA email: qi@kgs.ku.edu
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Timothy R. Carr
Timothy R. Carr
Kansas Geological Survey, University of Kansas 1930 Constant Ave. Lawrence, Kansas 66047-3726 USA email: tcarr@kgs.ku.edu
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Robert H. Goldstein
Robert H. Goldstein
Department of Geology, University of Kansas 1475 Jayhawk Blvd. Lawrence, Kansas 66045-7613 USA email: gold@ku.edu
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Published:
December 01, 2006

Abstract

Many essential aspects are involved in quantitative characterization of oolite carbonate reservoirs. Rock-facies classification, external facies geometry, and internal rock-property distribution are fundamental to characterization for reservoir simulation and prediction of future hydrocarbon recovery. The typical challenge for small Midcontinent fields in the U.S is absence of high-resolution seismic data capable of resolving relatively thin reservoir intervals. An integrated geostatistical approach is presented that uses available well data from the St. Louis Limestone in the Archer Field, southwestern Kansas, to improve oolitic reservoir modeling and corresponding streamline simulation. The proposed approach uses neural network and stochastic methods to integrate different types of data (core, log, stratigraphic horizons, and production); at different scales (vertical, horizontal, fine-scale core data, coarse-scale well-log data); and variable degrees of quantification (facies, log, well data).

The results include:

  1. three-dimensional stochastic simulations of facies distribution of St. Louis oolitic reservoirs;

  2. improved reservoir framework models (lithofacies) for carbonate shoal reservoirs;

  3. increased understanding of spatial distribution and variability of petrophysical parameters within carbonate shoal reservoirs;

  4. quantified measures of flow-unit connectivity;

  5. 3D visualization of the St. Louis carbonate reservoir systems;

  6. streamline simulations of the static geostatistical models to rank and determine the efficacy of the geological modeling procedure; and

  7. better understanding of key factors that control the facies distribution and the production of hydrocarbons within carbonate shoal reservoir systems. Geostatistical 3D modeling methods are applicable to other complex carbonate oolitic reservoirs or siliciclastic reservoirs in shallow-marine settings.

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Reservoir Characterization: Integrating Technology and Business Practices

Roger M. Slatt
Roger M. Slatt
Houston, Texas
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Norman c. Rosen
Norman c. Rosen
Houston, Texas
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Michael Bowman
Michael Bowman
Houston, Texas
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John Castagna
John Castagna
Houston, Texas
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Timothy Good
Timothy Good
Houston, Texas
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Robert Loucks
Robert Loucks
Houston, Texas
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Rebecca Latimer
Rebecca Latimer
Houston, Texas
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Mark Scheihing
Mark Scheihing
Houston, Texas
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Hu Smith
Hu Smith
Houston, Texas
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SEPM Society for Sedimentary Geology
Volume
26
ISBN electronic:
978-0-9836096-4-3
Publication date:
December 01, 2006

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