Quantitative Methods for Reservoir Characterization and Improved Recovery: Application to Heavy Oil Sands

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Description

Improved prediction of interwell reservoir heterogeneity was needed to increase productivity and to reduce recovery cost for California's heavy oil sands, which contain approximately 2.3 billion barrels of remaining reserves in the Temblor Formation and in other formations of the San Joaquin Valley. This investigation involved application of advanced analytical property-distribution methods conditioned to continuous outcrop control for improved reservoir characterization and simulation.

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9 pages

Creation Information

Castle, J.W.; Bridges, R.A.; Lorinovich, C.J.; Molz, Fred J.; Dinwiddie, C.L. & Lu, S. February 7, 2003.

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Description

Improved prediction of interwell reservoir heterogeneity was needed to increase productivity and to reduce recovery cost for California's heavy oil sands, which contain approximately 2.3 billion barrels of remaining reserves in the Temblor Formation and in other formations of the San Joaquin Valley. This investigation involved application of advanced analytical property-distribution methods conditioned to continuous outcrop control for improved reservoir characterization and simulation.

Physical Description

9 pages

Notes

OSTI as DE00807568

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  • Other Information: PBD: 7 Feb 2003

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  • Report No.: DOE/BC/15119-4
  • Grant Number: AC26-98BC15119
  • DOI: 10.2172/807568 | External Link
  • Office of Scientific & Technical Information Report Number: 807568
  • Archival Resource Key: ark:/67531/metadc739472

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Creation Date

  • February 7, 2003

Added to The UNT Digital Library

  • Oct. 18, 2015, 6:40 p.m.

Description Last Updated

  • April 8, 2016, 4:34 p.m.

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Castle, J.W.; Bridges, R.A.; Lorinovich, C.J.; Molz, Fred J.; Dinwiddie, C.L. & Lu, S. Quantitative Methods for Reservoir Characterization and Improved Recovery: Application to Heavy Oil Sands, report, February 7, 2003; Tulsa, Oklahoma. (digital.library.unt.edu/ark:/67531/metadc739472/: accessed September 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.