Application of an Artificial Neural Network (ANN) for the Correction of NMR Total Porosity, Effective Porosity, and the Bulk Volume Irreducible Water Data Obtained From a Heavy-Oil Reservoir Using an Oil-Based-Mud Drilling Fluid in the Samaria: Tertiary Reservoir in Southeastern Mexico

2010 ◽  
Author(s):  
Luis Granados Cruz ◽  
J.C. Sosa ◽  
J.A. Galicia ◽  
R. Rodriguez-Monroy ◽  
Gelmunt Escamilla ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heavy Oil ◽  
Drilling Fluid ◽  
Total Porosity ◽  
Effective Porosity ◽  
Oil Reservoir ◽  
Bulk Volume ◽  
Heavy Oil Reservoir ◽  
Artificial Neural Network Ann

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.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

MONITORING CO2 SATURATION USING THREE-DETECTOR PNC LOGGING TECHNIQUE FOR CO2 EOR IN HEAVY OIL RESERVOIR

2019 ◽  
Author(s):  
Feng Zhang ◽  
Quanying Zhang ◽  
Lili Tian ◽  
Xiaoyang Zhang ◽  
Qian Chen ◽  
...  
Keyword(s):  
Heavy Oil ◽  
Oil Reservoir ◽  
Heavy Oil Reservoir ◽  
Co2 Eor
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