TY - JOUR
T1 - Artificial neural network model prediction of bitumen/light oil mixture viscosity under reservoir temperature and pressure conditions as a superior alternative to empirical models
AU - Ssebadduka, Ronald
AU - Le, Nam Nguyen Hai
AU - Nguele, Ronald
AU - Alade, Olalekan
AU - Sugai, Yuichi
N1 - Funding Information:
Acknowledgments: The authors extend their gratitude to Japan Petroleum Exploration for supplying the crude oils used in this study. The authors are very thankful for the financial support provided to them by the Japanese government through the Ministry of Education, Culture, Sports, Science, and Technology.
Funding Information:
The authors extend their gratitude to Japan Petroleum Exploration for supplying the crude oils used in this study. The authors are very thankful for the financial support provided to them by the Japanese government through the Ministry of Education, Culture, Sports, Science, and Technology.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Herein, we show the prediction of the viscosity of a binary mixture of bitumen and light oil using a feedforward neural network with backpropagation model, as compared to empirical models such as the reworked van der Wijk model (RVDM), modified van der Wijk model (MVDM), and Al-Besharah. The accuracy of the ANN was based on all of the samples, while that of the empirical models was analyzed based on experimental results obtained from rheological studies of three binary mixtures of light oil (API 32◦) and bitumen (API 7.39◦). The classical Mehrotra–Svrcek model to predict the viscosity of bitumen under temperature and pressure, which estimated bitumen results with an %AAD of 3.86, was used along with either the RVDM or the MVDM to estimate the viscosity of the bitumen and light oil under reservoir temperature and pressure conditions. When both the experimental and literature data were used for comparison to an artificial neural network (ANN) model, the MVDM, RVDM and Al-Besharah had higher R2 values.
AB - Herein, we show the prediction of the viscosity of a binary mixture of bitumen and light oil using a feedforward neural network with backpropagation model, as compared to empirical models such as the reworked van der Wijk model (RVDM), modified van der Wijk model (MVDM), and Al-Besharah. The accuracy of the ANN was based on all of the samples, while that of the empirical models was analyzed based on experimental results obtained from rheological studies of three binary mixtures of light oil (API 32◦) and bitumen (API 7.39◦). The classical Mehrotra–Svrcek model to predict the viscosity of bitumen under temperature and pressure, which estimated bitumen results with an %AAD of 3.86, was used along with either the RVDM or the MVDM to estimate the viscosity of the bitumen and light oil under reservoir temperature and pressure conditions. When both the experimental and literature data were used for comparison to an artificial neural network (ANN) model, the MVDM, RVDM and Al-Besharah had higher R2 values.
UR - http://www.scopus.com/inward/record.url?scp=85121528271&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121528271&partnerID=8YFLogxK
U2 - 10.3390/en14248520
DO - 10.3390/en14248520
M3 - Article
AN - SCOPUS:85121528271
VL - 14
JO - Energies
JF - Energies
SN - 1996-1073
IS - 24
M1 - 8520
ER -