Abstract
Miscible gas injection (MGI) processes such as miscible CO2 flooding have been in use as attractive EOR options, especially in conventional oil reserves. Optimal design of MGI is strongly dependent on parameters such as gas-oil minimum miscibility pressure (MMP), which is normally determined through expensive and time-consuming laboratory tests. Thus, developing a fast and reliable technique to predict gas-oil MMP is inevitable. To address this issue, a smart model is developed in this paper to forecast gas-oil MMP on the basis of a feed-forward artificial neural network (FF-ANN) combined with particle swarm optimisation (PSO). The MMP of a reservoir fluid was considered as a function of reservoir temperature and the compositions of oil and injected gas in the proposed model. Results of this study indicate that reservoir temperature among the input parameters selected for the PSO-ANN has the greatest impact on MMP value. The developed PSO-ANN model was examined using experimental data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of gas-oil MMP. Compared with other available methods, the proposed model is capable of forecasting oil-gas MMP more accurately in wide ranges of thermodynamic and process conditions. All predictive models used other than the PSO-ANN model failed in providing a good estimate of the oil-gas MMP of the hydrocarbon mixtures in Azadegan oilfield, Iran.
| Original language | English |
|---|---|
| Pages (from-to) | 1325-1337 |
| Number of pages | 13 |
| Journal | Canadian Journal of Chemical Engineering |
| Volume | 91 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Jul 1 2013 |
| Externally published | Yes |
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Keywords
- EOR
- Experimental study
- Minimum miscible pressure
- Miscible gas flooding
- Optimised neural network
- Smart technique
ASJC Scopus subject areas
- Chemical Engineering(all)
Cite this
A developed smart technique to predict minimum miscible pressure-eor implications. / Zendehboudi, Sohrab; Ahmadi, Mohammad Ali; Bahadori, Alireza; Shafiei, Ali; Babadagli, Tayfun.
In: Canadian Journal of Chemical Engineering, Vol. 91, No. 7, 01.07.2013, p. 1325-1337.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A developed smart technique to predict minimum miscible pressure-eor implications
AU - Zendehboudi, Sohrab
AU - Ahmadi, Mohammad Ali
AU - Bahadori, Alireza
AU - Shafiei, Ali
AU - Babadagli, Tayfun
PY - 2013/7/1
Y1 - 2013/7/1
N2 - Miscible gas injection (MGI) processes such as miscible CO2 flooding have been in use as attractive EOR options, especially in conventional oil reserves. Optimal design of MGI is strongly dependent on parameters such as gas-oil minimum miscibility pressure (MMP), which is normally determined through expensive and time-consuming laboratory tests. Thus, developing a fast and reliable technique to predict gas-oil MMP is inevitable. To address this issue, a smart model is developed in this paper to forecast gas-oil MMP on the basis of a feed-forward artificial neural network (FF-ANN) combined with particle swarm optimisation (PSO). The MMP of a reservoir fluid was considered as a function of reservoir temperature and the compositions of oil and injected gas in the proposed model. Results of this study indicate that reservoir temperature among the input parameters selected for the PSO-ANN has the greatest impact on MMP value. The developed PSO-ANN model was examined using experimental data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of gas-oil MMP. Compared with other available methods, the proposed model is capable of forecasting oil-gas MMP more accurately in wide ranges of thermodynamic and process conditions. All predictive models used other than the PSO-ANN model failed in providing a good estimate of the oil-gas MMP of the hydrocarbon mixtures in Azadegan oilfield, Iran.
AB - Miscible gas injection (MGI) processes such as miscible CO2 flooding have been in use as attractive EOR options, especially in conventional oil reserves. Optimal design of MGI is strongly dependent on parameters such as gas-oil minimum miscibility pressure (MMP), which is normally determined through expensive and time-consuming laboratory tests. Thus, developing a fast and reliable technique to predict gas-oil MMP is inevitable. To address this issue, a smart model is developed in this paper to forecast gas-oil MMP on the basis of a feed-forward artificial neural network (FF-ANN) combined with particle swarm optimisation (PSO). The MMP of a reservoir fluid was considered as a function of reservoir temperature and the compositions of oil and injected gas in the proposed model. Results of this study indicate that reservoir temperature among the input parameters selected for the PSO-ANN has the greatest impact on MMP value. The developed PSO-ANN model was examined using experimental data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of gas-oil MMP. Compared with other available methods, the proposed model is capable of forecasting oil-gas MMP more accurately in wide ranges of thermodynamic and process conditions. All predictive models used other than the PSO-ANN model failed in providing a good estimate of the oil-gas MMP of the hydrocarbon mixtures in Azadegan oilfield, Iran.
KW - EOR
KW - Experimental study
KW - Minimum miscible pressure
KW - Miscible gas flooding
KW - Optimised neural network
KW - Smart technique
UR - http://www.scopus.com/inward/record.url?scp=84878685578&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878685578&partnerID=8YFLogxK
U2 - 10.1002/cjce.21802
DO - 10.1002/cjce.21802
M3 - Article
AN - SCOPUS:84878685578
VL - 91
SP - 1325
EP - 1337
JO - Canadian Journal of Chemical Engineering
JF - Canadian Journal of Chemical Engineering
SN - 0008-4034
IS - 7
ER -