Prediction of formation damage during fluid injection into fractured, low permeability reservoirs via neural networks

M. Nikravesh; A. R. Kovscek; R. M. Johnston; T. W. Patzek

doi:10.2523/31103-ms

Prediction of formation damage during fluid injection into fractured, low permeability reservoirs via neural networks

M. Nikravesh^*, A. R. Kovscek, R. M. Johnston, T. W. Patzek

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

20 Scopus citations

Abstract

Neural network models can be used to predict the dynamics of low permeability, fractured reservoirs undergoing fluid injection. One such model was developed using data from a waterflood project in the South Belridge Diatomite (Kern County, California) to predict wellhead pressure as a function of injection rate, and vice versa. Neural networks were also created using data from a dual injector steamdrive pilot in the same field to correlate injection pressures and rates and temperature responses in seven observation wells.

Original language	English (US)
Pages	351-365
Number of pages	15
DOIs	https://doi.org/10.2523/31103-ms
State	Published - 1996
Externally published	Yes
Event	Proceedings of the 1996 International Symposium on Formation Damage Control - Lafayette, LA, USA Duration: Feb 14 1996 → Feb 15 1996

Other

Other	Proceedings of the 1996 International Symposium on Formation Damage Control
City	Lafayette, LA, USA
Period	02/14/96 → 02/15/96

ASJC Scopus subject areas

Energy Engineering and Power Technology
Geotechnical Engineering and Engineering Geology

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10.2523/31103-ms

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title = "Prediction of formation damage during fluid injection into fractured, low permeability reservoirs via neural networks",

abstract = "Neural network models can be used to predict the dynamics of low permeability, fractured reservoirs undergoing fluid injection. One such model was developed using data from a waterflood project in the South Belridge Diatomite (Kern County, California) to predict wellhead pressure as a function of injection rate, and vice versa. Neural networks were also created using data from a dual injector steamdrive pilot in the same field to correlate injection pressures and rates and temperature responses in seven observation wells.",

author = "M. Nikravesh and Kovscek, {A. R.} and Johnston, {R. M.} and Patzek, {T. W.}",

year = "1996",

doi = "10.2523/31103-ms",

language = "English (US)",

pages = "351--365",

note = "Proceedings of the 1996 International Symposium on Formation Damage Control ; Conference date: 14-02-1996 Through 15-02-1996",

}

Prediction of formation damage during fluid injection into fractured, low permeability reservoirs via neural networks. / Nikravesh, M.; Kovscek, A. R.; Johnston, R. M. et al.
1996. 351-365 Paper presented at Proceedings of the 1996 International Symposium on Formation Damage Control, Lafayette, LA, USA.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Prediction of formation damage during fluid injection into fractured, low permeability reservoirs via neural networks

AU - Nikravesh, M.

AU - Kovscek, A. R.

AU - Johnston, R. M.

AU - Patzek, T. W.

PY - 1996

Y1 - 1996

N2 - Neural network models can be used to predict the dynamics of low permeability, fractured reservoirs undergoing fluid injection. One such model was developed using data from a waterflood project in the South Belridge Diatomite (Kern County, California) to predict wellhead pressure as a function of injection rate, and vice versa. Neural networks were also created using data from a dual injector steamdrive pilot in the same field to correlate injection pressures and rates and temperature responses in seven observation wells.

AB - Neural network models can be used to predict the dynamics of low permeability, fractured reservoirs undergoing fluid injection. One such model was developed using data from a waterflood project in the South Belridge Diatomite (Kern County, California) to predict wellhead pressure as a function of injection rate, and vice versa. Neural networks were also created using data from a dual injector steamdrive pilot in the same field to correlate injection pressures and rates and temperature responses in seven observation wells.

UR - http://www.scopus.com/inward/record.url?scp=0029753857&partnerID=8YFLogxK

U2 - 10.2523/31103-ms

DO - 10.2523/31103-ms

M3 - Paper

AN - SCOPUS:0029753857

SP - 351

EP - 365

T2 - Proceedings of the 1996 International Symposium on Formation Damage Control

Y2 - 14 February 1996 through 15 February 1996

ER -

Prediction of formation damage during fluid injection into fractured, low permeability reservoirs via neural networks

Abstract

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