Performance Prediction of Waterflooding in Western Canadian Heavy Oil Reservoirs Using Artificial Neural Network

2010 ◽  
Vol 24 (4) ◽  
pp. 2520-2526 ◽  
Author(s):  
Farid Ahmadloo ◽  
Koorosh Asghari ◽  
Gay Renouf
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Performance Prediction ◽  
Heavy Oil ◽  
Oil Reservoirs ◽  
Heavy Oil Reservoirs ◽  
Artificial Neural

Stochastic Reconstruction of Complex Heavy Oil Molecules Using an Artificial Neural Network

2017 ◽  
Vol 31 (11) ◽  
pp. 11932-11938 ◽  
Author(s):  
Celal Utku Deniz ◽  
Muzaffer Yasar ◽  
Michael T. Klein
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heavy Oil ◽  
Stochastic Reconstruction ◽  
Artificial Neural

scholarly journals Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2390 ◽  
Author(s):  
Olalekan Alade ◽  
Dhafer Al Shehri ◽  
Mohamed Mahmoud ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heavy Oil ◽  
Atmospheric Pressure ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Viscosity Data ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The viscosity data of two heavy oil samples X and Y, with asphaltene contents 24.8% w/w and 18.5% w/w, respectively, were correlated with temperature and pressure using empirical models and the artificial neural network (ANN) approach. The viscosities of the samples were measured over a range of temperatures between 70 °C and 150 °C; and from atmospheric pressure to 7 MPa. It was found that the viscosity of sample X, at 85 °C and atmospheric pressure (0.1 MPa), was 1894 cP and that it increased to 2787 cP at 7 MPa. At 150 °C, the viscosity increased from 28 cP (at 0.1 MPa) to 33 cP at 7 MPa. For sample Y, the viscosity at 70 °C and 0.1 MPa increased from 2260 cP to 3022 cP at 7 MPa. At 120 °C, the viscosity increased from 65 cP (0.1 MPa) to 71 cP at 7 MPa. Notably, using the three-parameter empirical models (Mehrotra and Svrcek, 1986 and 1987), the correlation constants obtained in this study are very close to those that were previously obtained for the Canadian heavy oil samples. Moreover, compared to other empirical models, statistical analysis shows that the ANN model has a better predictive accuracy (R2 ≈ 1) for the viscosity data of the heavy oil samples used in this study.

Performance Prediction Using Fuzzy Mapping and Artificial Neural Network

2014 ◽  
Vol 539 ◽  
pp. 901-903
Author(s):  
Feng Yu ◽  
Yi Wu ◽  
Xian Ming Shao ◽  
Jian Chun Guo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Performance Prediction ◽  
Fuzzy Neural Network ◽  
Good Precision ◽  
Fuzzy Mapping ◽  
Model Combining ◽  
Fuzzy Neural ◽  
Artificial Neural ◽  
Comparative Validation

This paper presents a fuzzy neural network model combining fuzzy mapping and artificial neural network for the prediction of sports results. The model for predicting track & field results of each individual event at the 27th Olympic Games is established. Through modeling and comparative validation it is shown that since 1950s the modeling of track & field results according to the sequence of number reflects the basic trends of the track & field result development with good precision.

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