Computational models for closed-loop reservoir optimization under uncertainty
Abstract/Contents
- Abstract
- This data set contains flow simulation models generated for computational experiments towards developments of new algorithms in closed-loop optimization of subsurface resources (with results published in a Stanford PhD dissertation). Data sets are provided for each chapter of the dissertation. These data sets correspond to 2D and 3D Gaussian and channelized models (with binary and bimodal distribution) and can be used for testing new methods and algorithms for flow simulation or optimization of subsurface resources.
Description
| Type of resource | software, multimedia |
|---|---|
| Date created | 2016 |
Creators/Contributors
| Author | Shirangi, Mehrdad Gharib |
|---|
Subjects
| Subject | Optimization |
|---|---|
| Subject | subsurface flow |
| Subject | reservoir model |
| Subject | geostatistics |
| Subject | multipoint statistics |
| Subject | closed-loop optimization |
| Subject | reservoir optimization |
| Subject | model selection. |
| Genre | Dataset |
Bibliographic information
| Related Publication | Shirangi, M. G., and Durlofsky, L. J. (2016). A general method to select representative models for decision making and optimization under uncertainty, Computers & Geosciences 96: 109-123. http://dx.doi.org/10.1016/j.cageo.2016.08.002 |
|---|---|
| Related Publication | Shirangi, M. G., Volkov, O., Durlofsky, L. J. (2017). Joint optimization of economic project life and well controls. SPE Journal. https://doi.org/10.2118/182642-MS |
| Related Publication | Shirangi, M. G., & Durlofsky, L. J. (2015). Closed-loop field development under uncertainty by use of optimization with sample validation. SPE Journal, 20(05), 908-922. https://doi.org/10.2118/173219-PA |
| Related item | |
| Location | https://purl.stanford.edu/tm285tx5110 |
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.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
Shirangi, M. G. (2017). Advanced techniques for closed-loop reservoir optimization under uncertainty. Stanford University.
https://searchworks.stanford.edu/view/11999955
Collection
Stanford Research Data
View other items in this collection in SearchWorksContact information
- Contact
- mehr@stanford.edu
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