Journal article 1286 views 284 downloads
Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
Applied Soft Computing, Volume: 34, Pages: 463 - 484
Swansea University Authors:
Johann Sienz , Helen Davies
-
PDF | Accepted Manuscript
Download (1.85MB)
DOI (Published version): 10.1016/j.asoc.2015.05.032
Abstract
This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, an...
Published in: | Applied Soft Computing |
---|---|
ISSN: | 1568-4946 |
Published: |
2015
|
Online Access: |
Check full text
|
URI: | https://cronfa.swan.ac.uk/Record/cronfa22073 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Abstract: |
This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, and may involve constraints. In this paper, we develop a robust particle swarm algorithm coupled with a novel adaptive constraint-handling technique to search for the global optimum of these formulations. However, this is a population-based method, which therefore requires a high number of evaluations of an objective function. Since the performance evaluation of a given management scheme requires a computationally expensive high-fidelity simulation, it is not practicable to use it directly to guide the search. In order to overcome this drawback, a Kriging surrogate model is used, which is trained offline via evaluations of a High-Fidelity simulator on a number of sample points. The optimizer then seeks the optimum of the surrogate model. |
---|---|
Keywords: |
Adaptive constraint handling; Global search; Particle swarm; Reservoir simulation; Surrogate-based optimization; Waterflooding management |
College: |
Faculty of Science and Engineering |
Start Page: |
463 |
End Page: |
484 |