Name
Abstract | ||
|---|---|---|
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 computation ally 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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CEC.2011.5949713 | Evolutionary Computation |
Keywords | Field | DocType |
Bayes methods,decision making,hydrocarbon reservoirs,inverse problems,parallel algorithms,parameter estimation,particle swarm optimisation,Bayesian optimization algorithm,decision making,history matching,inverse problem,oil reservoir simulation,parallel BOA-PSO hybrid algorithm,particle swarm optimization,reservoir model parameter optimization | Particle swarm optimization,Reservoir simulation,Data modeling,Mathematical optimization,Hybrid algorithm,Computer science,Parallel algorithm,Inverse problem,Artificial intelligence,Estimation theory,Machine learning,Bayesian probability | Conference |
ISSN | ISBN | Citations |
Pending | 978-1-4244-7834-7 | 3 |
PageRank | References | Authors |
0.42 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alan P. Reynolds | 1 | 157 | 11.57 |
| Asaad Abdollahzadeh | 2 | 3 | 0.76 |
| David W. Corne | 3 | 2161 | 152.00 |
| Mike Christie | 4 | 13 | 3.15 |
| Brian Davies | 5 | 3 | 0.76 |
| Glyn Williams | 6 | 3 | 0.76 |