Optimization of Nonconventional Well Placement Using Genetic Algorithms and Statistical Proxy
Abstract/Contents
- Abstract
- The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells under geological uncertainty. In order to reduce the excessive computational requirements of the base method, a statistical proxy based on cluster analysis is applied into the optimization process. This proxy provides an estimate of the cumulative distribution function (cdf) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxybased performance estimate in conjunction with the proxy cdf enables the systematic selection of the most appropriate scenarios for full simulation. The proxy is extended for application to the optimization of multiple nonconventional wells opened at different times. The proxy in this case is referred to as dynamic proxy. For optimization of a single nonconventional well, it is shown that by simulating only 10 or 20% of the scenarios, optimization results very close to those achieved by simulating all cases are obtained. For multiple wells drilled at different times, the dynamic proxy is effective though a relatively high percentage (e.g., 50%) of the cases must be simulated.
Description
| Type of resource | text |
|---|---|
| Date created | June 2006 |
Creators/Contributors
| Author | Onwunalu, Jerome |
|---|---|
| Primary advisor | Durlofsky, Louis J. |
| 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
- Onwunalu, Jerome. (2006). Optimization of Nonconventional Well Placement Using Genetic Algorithms and Statistical Proxy. Stanford Digital Repository. Available at: https://purl.stanford.edu/kf532yh6869
Collection
Master's Theses, Doerr School of Sustainability
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