Constructing a unique two-phase compressibility factor model for lean gas condensates

다국어 초록 (Multilingual Abstract)

Generating a reliable experimental model for two-phase compressibility factor in lean gas condensate reservoirshas always been demanding, but it was neglected due to lack of required experimental data. This study presentsthe main results of constructing the first two-phase compressibility factor model that is completely valid for Iranianlean gas condensate reservoirs. Based on a wide range of experimental data bank for Iranian lean gas condensate reservoirs,a unique two-phase compressibility factor model was generated using design of experiments (DOE) method andneural network technique (ANN). Using DOE, a swift cubic response surface model was generated for two-phase compressibilityfactor as a function of some selected fluid parameters for lean gas condensate fluids. The proposed DOEand ANN models were finally validated using four new independent data series. The results showed that there is agood agreement between experimental data and the proposed models. In the end, a detailed comparison was madebetween the results of proposed models.

참고문헌 (Reference)

1 I. E. Lagaris, 9 (9): 987-, 1998

2 O. A. Osborne, 28 : 80-, 1992

3 O. A. Osborne, 28 : 80-, 1992

4 S. Mohaghegh, 16 (16): 263-, 1996

5 W. A. Habiballah, "Use of neural networks for prediction of vapor/liquid equilibrium K-values for light-hydrocarbon mixtures" 121 : 1996

6 R.B. Gharibi, "Universal neural network based model for estimating the PVT properties of crude oil systems" 1997

7 D.G. Rayes, "Twophase compressibility factors for retrograde gases" 87 : 1992

8 P. Accarian, "Neuro-computing help pore pressure determination" 1993

9 W. J. Epping, "Neural network for analysis and improvement of gas well production" 1990

10 J. E. Arthur, "Material Balance Modeling and Performance Prediction of a Composite Gas Reservoir" AEC Oil & Gas Co., and, K.O. Temeng, Mobil E&P Services Co 1993

11 Elsharkawy, Adel M., "MB Solution for High Pressure Gas Reservoirs" Petroleum Engineering Department-Kuwait University 1996

12 A.O. Kumoluyi, "Identification of well test models using high order neural networks" 1994

13 Elsharkawy, Adel M., "EOS simulation and GRNN modeling of the behavior of retrograde-gas condensate reservoirs" Kuwait University 1997

14 W. Sung, "Development of the HT-BP Neural Network System for the Identification of a Well-Test Interpretation Model" 1996

15 C.D. Zhou, "Determining reservoir properties in reservoir studies using a fuzzy neural network" 1993

16 M. Anderson, "Design of Experiments" American Institute of Physics 1997

17 R. P. Sutton, "Compressibility factors for high-molecular-weight reservoir gases" 1985

18 A. M. Elsharkawy, "Compressibility factor for gas condensates" Alikhan Kuwait University 2000

19 I.R. Juniardi, "Complexities of using neural network in well test analysis of faulted reservoir" 1993

20 R. A. Arehart, "Artificial Intelligence in Exploration and Production" Texas A&M 1989

21 M. F. Briones, "Application of neural network in the prediction of reservoir hydrocarbon mixture composition from production data" 1994

22 A. W. Al-Kaabi, "An artificial neural network approach to identify the well test interpretation model" 1990