Abstract
In this paper, the latest approaches for automated history matching (AHM) were applied to a real brown field having 14 active wells with multiple responses (production rate, bottom hole pressure and well block pressure) located in the south of Iran. A modified support vector machine was employed to create a proxy model incorporated based on design of experimental. Thereafter, all model parameters were adjusted to reproduce the observed history within the created proxy model. Accordingly, the proposed proxy model was successfully constructedusing1086samplesbasedonanR 2 coefficientofabout0.9forthetrainedandtestdataset.Finally,theprocess was optimized by two main algorithms to reach the best solutions, which are genetic and particle swarm optimization.
| Original language | English |
|---|---|
| Pages (from-to) | 84-89 |
| Number of pages | 6 |
| Journal | Kuwait Journal of Science |
| Volume | 46 |
| Issue number | 1 |
| Publication status | Published - Jan 1 2019 |
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Keywords
- Cubic centered face
- Fast history matching
- Least square support sector
- Optimization
ASJC Scopus subject areas
- General
Cite this
Applying an optimized low risk model for fast history matching in a giant oil reservoir. / Karimi, Mojtaba; Mortazavi, Ali; Ahmadi, Mohammad.
In: Kuwait Journal of Science, Vol. 46, No. 1, 01.01.2019, p. 84-89.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - Applying an optimized low risk model for fast history matching in a giant oil reservoir
AU - Karimi, Mojtaba
AU - Mortazavi, Ali
AU - Ahmadi, Mohammad
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In this paper, the latest approaches for automated history matching (AHM) were applied to a real brown field having 14 active wells with multiple responses (production rate, bottom hole pressure and well block pressure) located in the south of Iran. A modified support vector machine was employed to create a proxy model incorporated based on design of experimental. Thereafter, all model parameters were adjusted to reproduce the observed history within the created proxy model. Accordingly, the proposed proxy model was successfully constructedusing1086samplesbasedonanR 2 coefficientofabout0.9forthetrainedandtestdataset.Finally,theprocess was optimized by two main algorithms to reach the best solutions, which are genetic and particle swarm optimization.
AB - In this paper, the latest approaches for automated history matching (AHM) were applied to a real brown field having 14 active wells with multiple responses (production rate, bottom hole pressure and well block pressure) located in the south of Iran. A modified support vector machine was employed to create a proxy model incorporated based on design of experimental. Thereafter, all model parameters were adjusted to reproduce the observed history within the created proxy model. Accordingly, the proposed proxy model was successfully constructedusing1086samplesbasedonanR 2 coefficientofabout0.9forthetrainedandtestdataset.Finally,theprocess was optimized by two main algorithms to reach the best solutions, which are genetic and particle swarm optimization.
KW - Cubic centered face
KW - Fast history matching
KW - Least square support sector
KW - Optimization
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UR - http://www.scopus.com/inward/citedby.url?scp=85062540133&partnerID=8YFLogxK
M3 - Review article
VL - 46
SP - 84
EP - 89
JO - Kuwait Journal of Science
JF - Kuwait Journal of Science
SN - 2307-4108
IS - 1
ER -