Ant colony optimization for history matching

Yasin Hajizadeh; Michael A. Christie; Vasily Demyanov

doi:10.2118/121193-MS

Ant colony optimization for history matching

Yasin Hajizadeh, Michael A. Christie, Vasily Demyanov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper introduces a new stochastic approach for automatic history matching based on a continuous ant colony optimization algorithm. Ant colony optimization (ACO) is a multi-agent optimization algorithm inspired by the behaviour of real ants. ACO is able to solve difficult optimization problems in both discrete and continuous variables. In the ACO algorithm, each artificial ant in the colony searches for good models in different regions of parameter space and shares information about the quality of the models with other agents. This gradually guides the colony towards models that match the desired behaviour-in our case the production history of the reservoir. The use of ACO history-matching has been illustrated on a reservoir simulation case for Gulf of Mexico which showed that Ant Colony optimization can be used to generate multiple history-matched reservoir models.

Original language	English
Title of host publication	EUROPEC/EAGE Conference and Exhibition 2009
Publisher	Society of Petroleum Engineers
ISBN (Print)	9781613994276
DOIs	https://doi.org/10.2118/121193-MS
Publication status	Published - 2009
Event	2009 SPE EUROPEC/EAGE Annual Conference and Exhibition - Amsterdam, Netherlands Duration: 8 Jun 2009 → 11 Jun 2009

Conference

Conference	2009 SPE EUROPEC/EAGE Annual Conference and Exhibition
Country/Territory	Netherlands
City	Amsterdam
Period	8/06/09 → 11/06/09

ASJC Scopus subject areas

Geotechnical Engineering and Engineering Geology
Geochemistry and Petrology

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10.2118/121193-MS

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title = "Ant colony optimization for history matching",

abstract = "This paper introduces a new stochastic approach for automatic history matching based on a continuous ant colony optimization algorithm. Ant colony optimization (ACO) is a multi-agent optimization algorithm inspired by the behaviour of real ants. ACO is able to solve difficult optimization problems in both discrete and continuous variables. In the ACO algorithm, each artificial ant in the colony searches for good models in different regions of parameter space and shares information about the quality of the models with other agents. This gradually guides the colony towards models that match the desired behaviour-in our case the production history of the reservoir. The use of ACO history-matching has been illustrated on a reservoir simulation case for Gulf of Mexico which showed that Ant Colony optimization can be used to generate multiple history-matched reservoir models.",

author = "Yasin Hajizadeh and Christie, {Michael A.} and Vasily Demyanov",

year = "2009",

doi = "10.2118/121193-MS",

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isbn = "9781613994276",

booktitle = "EUROPEC/EAGE Conference and Exhibition 2009",

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T1 - Ant colony optimization for history matching

AU - Hajizadeh, Yasin

AU - Christie, Michael A.

AU - Demyanov, Vasily

PY - 2009

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N2 - This paper introduces a new stochastic approach for automatic history matching based on a continuous ant colony optimization algorithm. Ant colony optimization (ACO) is a multi-agent optimization algorithm inspired by the behaviour of real ants. ACO is able to solve difficult optimization problems in both discrete and continuous variables. In the ACO algorithm, each artificial ant in the colony searches for good models in different regions of parameter space and shares information about the quality of the models with other agents. This gradually guides the colony towards models that match the desired behaviour-in our case the production history of the reservoir. The use of ACO history-matching has been illustrated on a reservoir simulation case for Gulf of Mexico which showed that Ant Colony optimization can be used to generate multiple history-matched reservoir models.

AB - This paper introduces a new stochastic approach for automatic history matching based on a continuous ant colony optimization algorithm. Ant colony optimization (ACO) is a multi-agent optimization algorithm inspired by the behaviour of real ants. ACO is able to solve difficult optimization problems in both discrete and continuous variables. In the ACO algorithm, each artificial ant in the colony searches for good models in different regions of parameter space and shares information about the quality of the models with other agents. This gradually guides the colony towards models that match the desired behaviour-in our case the production history of the reservoir. The use of ACO history-matching has been illustrated on a reservoir simulation case for Gulf of Mexico which showed that Ant Colony optimization can be used to generate multiple history-matched reservoir models.

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U2 - 10.2118/121193-MS

DO - 10.2118/121193-MS

M3 - Conference contribution

AN - SCOPUS:78651089790

SN - 9781613994276

BT - EUROPEC/EAGE Conference and Exhibition 2009

PB - Society of Petroleum Engineers

T2 - 2009 SPE EUROPEC/EAGE Annual Conference and Exhibition

Y2 - 8 June 2009 through 11 June 2009

ER -

Ant colony optimization for history matching

Abstract

Conference

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