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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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