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Geostatistical Integration of Crosswell Data for Carbonate Reservoir Modeling, Mcelroy Field, Texas

By
William M. Bashore
William M. Bashore
1Reservoir Characterization Research and Consulting, Inc., 2524 Monterey Place, Fullerton, CA 92633
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Robert T. Langan
Robert T. Langan
2Chevron Petroleum Technology Company, 1300 Beach Blvd., La Habra, CA 92631
3Department of Geophysics, Stanford University, Stanford, CA 94305
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Karla E. Tucker
Karla E. Tucker
2Chevron Petroleum Technology Company, 1300 Beach Blvd., La Habra, CA 92631
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Paul J. Griffith
Paul J. Griffith
3Department of Geophysics, Stanford University, Stanford, CA 94305
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Published:
January 01, 1995

ABSTRACT

By establishing a statistical link between a reservoir property of interest (e.g., porosity) and a more extensively available seismic attribute (e.g., acoustic impedance), one can use geostatistical integration algorithms to build improved reservoir models over those generated using well-based information only. This improvement is the result of the additional secondary information regarding interwell heterogeneities of the reservoir property that are known to influence heavily fluid flow in the reservoir (Hewett and Behrens, 1990; Omre, 1991). When these improved models are used in reservoir simulations, the expected value of a suite of simulation outcomes is likely to be closer to the true value than predictions made with only the sparser well control. In addition, the range of outcomes (i.e., uncertainty) should be less with these improved estimates. This reduces the risk associated with using the simulation results when considering various investment options for reservoir management.

In a proof-of-concept exercise, we use the P-wave reflection image from a crosswell seismic survey in the McElroy oil field of west Texas to help build a suite of porosity-permeability models. Because we are dealing with crosswell seismic data, the resolution is more than an order of magnitude better than we can get with surface seismic data. We apply a seismic inversion methodology (Bashore and Araktingi, 1994) to compute an image of acoustic impedance between the wells, which we then correlate at each wellbore with porosities obtained from well logs and core measurements. We expect impedance to be a good predictor of porosity because both velocity and density are variably influenced by differing amounts of porosity.

We generate several equiprobable porosity and permeability models using conditional simulation. A comparison of two reservoir flow simulations, one using well data only and the other using both well data and crosswell seismic data, suggests that models based upon the second set of data provide results more similar to actual field observations.

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Contents

SEPM Short Course Notes

Hydrocarbon Reservoir Characterization: Geologic Framework and Flow Unit Modeling

Emily L. Stoudt
Emily L. Stoudt
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Paul M. Harris
Paul M. Harris
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SEPM Society for Sedimentary Geology
Volume
34
ISBN electronic:
9781565761032
Publication date:
January 01, 1995

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