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Shallow Sands of North Slope, Alaska and Their Hydrocarbon Potentials

  • G. D. Sharma
  • D. O. Ogbe
  • V. A. Kamath
  • M. Zhang

Abstract

Extensive shallow sand deposits on North Slope Alaska contain about 35 billion barrels of oil in place. These Cretaceous and Tertiary sands are divided into two distinct units; the upper unit is termed as Ugnu sands and the lower unit is known as West Sak sands.

The Ugnu sands which occur at 2,000 ft below surface in the southwest dip towards northeast to a depth of 4,000 ft. These sands are divided into upper and lower units. The upper unit is discontinuous while the lower Ugnu sands are on an average 275 ft thick with good reservoir properties. The units contain approximately 15 billion barrels of 8–12°API crude characterized as tar sand bitumen.

The underlying West Sak sands, at depth ranging from 2,500 to 4,500 ft, consist of upper and lower units. These units contain approximately 20 billion barrels of crude ranging from 10 to 22°API.

These reservoirs are described using well log, core description and analyses of fluids therein. The well log interpretation was done using LOGCALC. Various reservoir parameters were evaluated to (i) characterize each pay zone, (ii) correlate lithofacies distributions (iii) estimate hydrocarbon in place, and (iv) environment of deposition.

The viscosities of crude oil found in these sands are typically high because of low temperature and recovery from these sands will require enhanced oil recovery (EOR) technology. Based on the EOR screening, miscible and thermal processes are considered to be suitable for recovery of crude from West Sak sands, while only thermal processes are considered to be applicable for recovery of Ugnu tar sands. The unconsolidated and friable sands also pose problems in well completion and production.

Keywords

North Slope Petrophysical Property Reservoir Parameter Sand Unit Reservoir Sand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • G. D. Sharma
    • 1
  • D. O. Ogbe
    • 1
  • V. A. Kamath
    • 1
  • M. Zhang
    • 1
  1. 1.Petroleum Development LaboratoryUniversity of Alaska FairbanksFairbanksUSA

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