Date of Award

5-17-2009

Document Type

Thesis

Abstract

"Permeability is a fundamental and often difficult to predict property of any reservoir. This is especially true for unconsolidated formations where any type of physical permeability measurement is difficult. This study develops a more detailed picture of reservoir permeability by generating continuous predicted permeability logs for the Schrader Bluff sands. Schrader Bluff sands are a medium heavy oil reservoir currently produced from the Milne Point oil field on the Alaska North Slope (ANS). A total of about 400 ft of core samples from two Milne Point wells were analyzed using a probe permeameter. These data were then integrated with available permeability data and used along with electric well log data for training an Artificial Neural Network to obtain continuous predicted permeability logs. The predicted data were then used to make Modified Lorenz Plots to study the flow unit behavior and to identify possible flow units. A similar dual approach that includes both probe permeameter measured and predicted permeability data can be used for flow unit characterization in other reservoirs with deficient permeability datasets. This approach would be especially useful for permeability characterization of unconsolidated or semiconsolidated reservoirs"--Leaf iii

Handle

http://hdl.handle.net/11122/12800

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