Optimization of Network Models to Predict Relative Permeability in Porous Media
Abstract/Contents
- Abstract
- A good knowledge of the relative permeabilities of the rocks in a hydrocarbon reservoir is critical to the management of the reservoir and to the prediction of production rates and overall recovery. Relative permeability curves are both difficult and expensive to obtain experimentally. When they have been found it is difficult to use them to predict the relative permeabilities of other similar, but different rocks, even if they are from the same formation. In this report an attempt is made to use network models to predict drainage relative permeability curves using information obtained from absolute permeability, porosity and capillary pressure data. Optimization using the "Polytope" method was performed on various parameters used to define the network model, with the aim of generating a capillary pressure curve close to an experimentally obtained curve of a real porous medium. Once the defining parameters were thus obtained, the network model was then used to generate predictive relative permeability curves. It was found that there is a non-unique relationship between relative permeability and capillary pressure. A good prediction was obtained for the relative permeability of sand-pack data, but this solution was not unique. The method had increasing difficulty in matching capillary pressure curves for more complex porous media, and was unable to provide a satisfactory match for capillary pressure of consolidated sandstones.
Description
| Type of resource | text |
|---|---|
| Date created | August 1994 |
Creators/Contributors
| Author | Gillespie, Patricia |
|---|---|
| Primary advisor | Blunt, Martin |
| Degree granting institution | Stanford University, Department of Petroleum Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
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Preferred citation
- Preferred Citation
- Gillespie, Patricia. (1994). Optimization of Network Models to Predict Relative Permeability in Porous Media. Stanford Digital Repository. Available at: https://purl.stanford.edu/rt167dk4264
Collection
Master's Theses, Doerr School of Sustainability
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