Holistic strategies for prediction uncertainty quantification of contaminant transport and reservoir production in field cases
Abstract/Contents
- Abstract
- In this dissertation, it aims to tackle prediction uncertainty reduction and quantification in complex subsurface problems. Examples of such problems are reactive contaminant transport prediction or reservoir production prediction using time lapse (4D) seismic data. Strategies developed within this dissertation will help practitioners to answer the following questions: (1) what field data to acquire at initial stage and how to determine that; (2) how to build appropriate prior model set, falsify such prior and validate posterior prediction; (3) what is an effective method to build statistical relationships between spatial data and prediction variables. Strategies are applied to a uranium remediation problem in a real field case and a reservoir management problem involving time lapse seismic data. Firstly, a sensitivity-based method is introduced to determine informative data to acquire before going to the field. It is demonstrated in the pre-field data assessment step in both the uranium remediation and reservoir prediction application. Secondly, a model scenario falsification method using spatial data, such as time lapse seismic data, is demonstrated. This method is also used to evaluate appropriate distance measures for distance based sensitivity analysis. The analysis of different distance is demonstrated in a synthetic reservoir case. In addition, this thesis introduces two novel dimension reduction techniques for spatial variables, which are crucial steps in scenario falsification and sensitivity analysis. Lastly, strategies are deployed in two field cases to demonstrate their efficacies.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Yang, Guang |
|---|---|
| Associated with | Stanford University, Department of Energy Resources Engineering. |
| Primary advisor | Caers, Jef |
| Thesis advisor | Caers, Jef |
| Thesis advisor | Mavko, Gary, 1949- |
| Thesis advisor | Mukerji, Tapan, 1965- |
| Advisor | Mavko, Gary, 1949- |
| Advisor | Mukerji, Tapan, 1965- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Guang Yang. |
|---|---|
| Note | Submitted to the Department of Energy Resources Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Guang Yang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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