Use of Hybrid Approaches and Metaoptimization for Well Placement Problems
Abstract/Contents
- Abstract
- In the context of oil field development, determining the well locations that maximize cumulative oil production or net present value is an important problem. A variety of optimization methods can be used to find the optimum well locations in the reservoir. In this study, gradient-free methods are considered. Both global (stochastic) and local (deterministic) methods are applied. A hybrid procedure that combines these two types of algorithms is developed. In addition, a metaoptimization technique is applied to determine the optimum way to combine different algorithms. For the global optimization algorithm, different families of particle swarm optimization (PSO) are investigated. Explorative PSO families, such as centered-progressive (CP-PSO) and progressive-progressive (PP-PSO), in addition to the standard PSO algorithm, are considered. The local optimization algorithm used is Hooke-Jeeves direct search (HJDS). The hybrid algorithm entails some number of function evaluations (reservoir simulations) using a PSO method. The best solution found is then used as the initial guess for HJDS. The overall algorithm takes advantage of the broad search provided by PSO and the fast convergence to a local optimum provided by HJDS. The hybrid algorithm is run for different PSO families and the results are compared to those using standalone PSO, and in some cases to standalone HJDS. Three cases, involving optimizing the locations of vertical wells in two-dimensional heterogeneous reservoir models, are considered. In general, the hybrid algorithms outperform the standalone methods, sometimes by a substantial margin. Metaoptimization is applied to determine the best PSO-HJDS hybrid algorithm. The parameters determined by metaoptimization are the number of PSO function evaluations and the PSO family type. The metaoptimization runs are very expensive, but they provide the best results for the three cases considered. The results achieved by the best PSO-HJDS hybrid are, however, very close to those from metaoptimization.
Description
| Type of resource | text |
|---|---|
| Date created | May 2011 |
Creators/Contributors
| Author | Aliyev, Elnur |
|---|---|
| Primary advisor | Durlofsky, Louis J. |
| Degree granting institution | Stanford University, Department of Energy Resources Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
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Preferred citation
- Preferred Citation
- Aliyev, Elnur. (2011). Use of Hybrid Approaches and Metaoptimization for Well Placement Problems. Stanford Digital Repository. Available at: https://purl.stanford.edu/dz450pr2075
Collection
Master's Theses, Doerr School of Sustainability
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