| Autor: |
Alan Reynolds, Asaad Abdollahzadeh, David Corne, Brian Davies, Glyn Williams, Michael Andrew Christie |
| Rok vydání: |
2011 |
| Předmět: |
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| Zdroj: |
IEEE Congress on Evolutionary Computation |
| DOI: |
10.1109/cec.2011.5949713 |
| Popis: |
In order to make effective decisions regarding the exploitation of oil reservoirs, it is necessary to create and update reservoir models using observations collected over time in a process known as history matching. This is an inverse problem: it requires the optimization of reservoir model parameters so that reservoir simulation produces response data similar to that observed. Since reservoir simulations are computationally expensive, it makes sense to use relatively sophisticated algorithms. This led to the use of the Bayesian Optimization Algorithm (BOA). However, the high performance of a much simpler algorithm — Particle Swarm Optimization (PSO) — led to the development of a BOA-PSO hybrid that outperformed both BOA and PSO on their own. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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