Abstract
Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO2 injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.
| Original language | English |
|---|---|
| Pages (from-to) | 857-875 |
| Number of pages | 19 |
| Journal | Chemical Engineering Research and Design |
| Volume | 92 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - Jan 1 2014 |
| Externally published | Yes |
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Keywords
- Deposition of asphaltene
- Laboratory data
- Oil production
- Precipitation of asphaltene
- Predictive tools
- Smart techniques
- Visual methods
ASJC Scopus subject areas
- Chemistry(all)
- Chemical Engineering(all)
Cite this
Asphaltene precipitation and deposition in oil reservoirs - Technical aspects, experimental and hybrid neural network predictive tools. / Zendehboudi, Sohrab; Shafiei, Ali; Bahadori, Alireza; James, Lesley A.; Elkamel, Ali; Lohi, Ali.
In: Chemical Engineering Research and Design, Vol. 92, No. 5, 01.01.2014, p. 857-875.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Asphaltene precipitation and deposition in oil reservoirs - Technical aspects, experimental and hybrid neural network predictive tools
AU - Zendehboudi, Sohrab
AU - Shafiei, Ali
AU - Bahadori, Alireza
AU - James, Lesley A.
AU - Elkamel, Ali
AU - Lohi, Ali
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO2 injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.
AB - Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO2 injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.
KW - Deposition of asphaltene
KW - Laboratory data
KW - Oil production
KW - Precipitation of asphaltene
KW - Predictive tools
KW - Smart techniques
KW - Visual methods
UR - http://www.scopus.com/inward/record.url?scp=84899930772&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899930772&partnerID=8YFLogxK
U2 - 10.1016/j.cherd.2013.08.001
DO - 10.1016/j.cherd.2013.08.001
M3 - Article
AN - SCOPUS:84899930772
VL - 92
SP - 857
EP - 875
JO - Chemical Engineering Research and Design
JF - Chemical Engineering Research and Design
SN - 0263-8762
IS - 5
ER -