Automated History Matching Using Approximately Linear Objective Functions
Abstract/Contents
- Abstract
- The automated history matching method proposed by Lester Dye was first reviewed and reexamined. The problem addressed is that of determining appropriate absolute permeabilities to be used in a reservoir model, assuming all other properties are known, so that the model will reproduce observed data. Limitations and shortcomings associated with the method were identified and simpler and more direct linear models were developed. Direct tests to study the behavior of the linear models and application tests to examine and compare the accuracies of the proposed method using the linear models were next performed. While results of the simple direct tests indicate some potential for Dye's proposed method in serving its intended purpose, results of a more stringent application test show that satisfactory accuracy using the method is assured only under single-phase or essentially single-phase flow situations. This, together with its other inherent limitations, detracts from the practical usefulness of Dyeís proposed method. An extension of the proposed method as suggested by Dye to simultaneously determine appropriate absolute and relative permeabilities was also considered, with negative result. In its place, a potential method for solving the simpler yet practically interesting problem of determining only relative permeabilities, assuming absolute permeabilities are known, is proposed.
Description
| Type of resource | text |
|---|---|
| Date created | June 1987 |
Creators/Contributors
| Author | Koh, Lee-Song |
|---|---|
| Primary advisor | Horne, Roland N. |
| Advisor | Aziz, Khalid |
| Degree granting institution | Stanford University, Department of Petroleum Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
Preferred citation
- Preferred Citation
- Koh, Lee-Song. (1987). Automated History Matching Using Approximately Linear Objective Functions. Stanford Digital Repository. Available at: https://purl.stanford.edu/jn435wv2825
Collection
Master's Theses, Doerr School of Sustainability
View other items in this collection in SearchWorksContact information
- Contact
- brannerlibrary@stanford.edu
Also listed in
Loading usage metrics...