A Neighborhood Algorithm (NA) for Modeling Dual-Medium Scenario Uncertainty from Production Data
Abstract/Contents
- Abstract
- Uncertainty quantification and history matching have been a challenge in reservoir forecasting, especially in naturally fractured reservoirs (NFRs) where the nature of the fractures is unknown resulting in large prior uncertainty. Multiple geological scenarios are generated for NFRs and a single best-fit model is not sufficient for decision-making under large uncertainty. In this report, we propose a neighborhood algorithm (NA) stochastic search method to search for multiple models that match the historical production data rather than producing one single best-fit model. Uncertainty is then quantified by calculating posterior probabilities using the data generated from NA. The method is illustrated with an application to a synthetic reservoir model analogous to fractured reservoirs in the Middle East. NA is proved to be an efficient and a fast way to calculate the posterior probabilities and quantify uncertainty.
Description
| Type of resource | text |
|---|---|
| Date created | June 2014 |
Creators/Contributors
| Author | Alsaif, Sarah |
|---|---|
| Primary advisor | Caers, Jef |
| Degree granting institution | Stanford University, Department of Energy Resources 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
- Alsaif, Sarah (2014). A Neighborhood Algorithm (NA) for Modeling Dual-Medium Scenario Uncertainty from Production Data. Stanford Digital Repository. Available at: https://purl.stanford.edu/qj528qh2080
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...