Estimation of water content of natural gases using particle swarm optimization method

被引：5

作者：

Ahmadi, Mohammad-Ali ^{[1
]}

Ahmad, Zainal ^{[2
]}

Le Thi Kim Phung ^{[3
]}

Kashiwao, Tomoaki ^{[4
]}

Bahadori, Alireza ^{[5
]}

机构：

[1] Petr Univ Technol, Ahwaz Fac Petr Engn, Dept Petr Engn, Ahvaz, Iran

[2] Univ Sains Malaysia, Sch Chem Engn, Engn Campus, George Town, Malaysia

[3] Hochiminh City Univ Technol, Fac Chem Engn, Dept Chem Proc & Equipment, Hochiminh City, Vietnam

[4] Niihama Coll, Natl Inst Technol, Dept Elect & Control Engn, Niihama, Japan

[5] So Cross Univ, Sch Environm Sci & Engn, Lismore, NSW 2480, Australia

来源：

PETROLEUM SCIENCE AND TECHNOLOGY | 2016年 / 34卷 / 07期

关键词：

Artificial neural network; modeling; natural gas; particle swarm optimization; water content; ARTIFICIAL NEURAL-NETWORK; ASPHALTENE PRECIPITATION; PREDICTION; ALGORITHM; PRESSURE;

D O I：

10.1080/10916466.2016.1153655

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A precise estimation of natural gas water content is a significant constraint in appropriate planning of natural gas production, processing services and transmission. The main contribution of this research is to develop a machine learning approach for predicting water content of sweet and sour natural gases. In this regard, a joining of particle swarm optimization and an artificial neural network was utilized. The suggested model presents good predictions of the sour natural gas water content with following circumstances, including CO2 contents of 0-40 mol%, H2S contents of 0-50 mol%, pressures in range from atmospheric to 70,000 KPa for sour gas and 100,000 KPa for sweet gas, and temperatures from 10-200 degrees C for sweet gases and 10-150 degrees C for sour gases.

引用

页码：595 / 600

页数：6

共 30 条

[1]

Ahmadi Mohammad Ali, 2016, Petroleum, V2, P67, DOI 10.1016/j.petlm.2015.07.008

[2]

Ahmadi Mohammad Ali, 2016, Petroleum, V2, P177, DOI 10.1016/j.petlm.2015.10.002

[3] Accurate prediction of properties of carbon dioxide for carbon capture and sequestration operations [J].

Ahmadi, M. A. ;

Kashiwao, T. ;

Rozyn, J. ;

Bahadori, A. .

PETROLEUM SCIENCE AND TECHNOLOGY, 2016, 34 (01) :97-103

[4] Prediction of Oil Production Rate Using Vapor-extraction Technique in Heavy Oil Recovery Operations [J].

Ahmadi, M. A. ;

Kashiwao, T. ;

Bahadori, A. .

PETROLEUM SCIENCE AND TECHNOLOGY, 2015, 33 (20) :1764-1769

[5]

Ahmadi M.A., 2015, Petroleum, V1, P118, DOI [DOI 10.1016/J.PETLM.2015.06.004, 10.1016/j.petlm.2015.06.004]

[6]

Ahmadi M. A., 2015, Petroleum, V1, P307, DOI [10.1016/j.petlm.2015.08.003, DOI 10.1016/J.PETLM.2015.08.003]

[7] Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process [J].

Ahmadi, Mohammad Ali ;

Zahedzadeh, Mohammad ;

Shadizadeh, Seyed Reza ;

Abbassi, Reza .

FUEL, 2015, 148 :202-211

[8] Developing a Robust Surrogate Model of Chemical Flooding Based on the Artificial Neural Network for Enhanced Oil Recovery Implications [J].

Ahmadi, Mohammad Ali .

MATHEMATICAL PROBLEMS IN ENGINEERING, 2015, 2015

[9] Prediction breakthrough time of water coning in the fractured reservoirs by implementing low parameter support vector machine approach [J].

Ahmadi, Mohammad Ali ;

Ebadi, Mohammad ;

Hosseini, Seyed Moein .

FUEL, 2014, 117 :579-589

[10] Evolving smart approach for determination dew point pressure through condensate gas reservoirs [J].

Ahmadi, Mohammad Ali ;

Ebadi, Mohammad .

FUEL, 2014, 117 :1074-1084

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