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

Mahmood Moayyedi; Arash Azamifard; Aliashghar Gharesheikhlou; Emadoddin Mosaferi

Korean Journal of Chemical Engineering, Vol.32, No.2, 323-327, February, 2015

DOI10.1007/s11814-014-0233-x Export Citation

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

Mahmood Moayyedi^†, Arash Azamifard¹, Aliashghar Gharesheikhlou, and Emadoddin Mosaferi¹

Research Institute of Petroleum Industry (RIPI), Tehran, Iran
¹Department of Petroleum Engineering, Amirkabir University of Technology (AUT), Tehran, Iran

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Generating a reliable experimental model for two-phase compressibility factor in lean gas condensate reservoirs has always been demanding, but it was neglected due to lack of required experimental data. This study presents the main results of constructing the first two-phase compressibility factor model that is completely valid for Iranian lean 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 and neural network technique (ANN). Using DOE, a swift cubic response surface model was generated for two-phase compressibility factor as a function of some selected fluid parameters for lean gas condensate fluids. The proposed DOE and ANN models were finally validated using four new independent data series. The results showed that there is a good agreement between experimental data and the proposed models. In the end, a detailed comparison was made between the results of proposed models.

Keywords:Gas Condensate;Compressibility Factor;Correlation;Design of Experiment;Neural Network

[References]

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[2] Arthur JE, Essien Consulting Engineers Ltd., China, H. S, Material Balance Modeling and Performance Prediction of a Composite Gas Reservoir, AEC Oil & Gas Co., and, K.O. Temeng, Mobil E&P Services Co, Paper SPE 26194 (1993)

[3] Elsharkawy, Adel M., MB Solution for High Pressure Gas Reservoirs, Petroleum Engineering Department-Kuwait University, Paper SPE 35589-MS (1996)

[4] Elsharkawy, Adel M, Salah GF, EOS simulation and GRNN modeling of the behavior of retrograde-gas condensate reservoirs, Kuwait University, Paper SPE 38855 (1997)

[5] Rayes DG, Piper LD, McCain WD, Poston SW, Two phase compressibility factors for retrograde gases; SPE Formation Evaluation, 87 (1992)

[6] Sutton RP, Compressibility factors for high-molecular-weight reservoir gases, in 60th Annual Technical Conference and Exhibition of the Society of Petroleum Engineering, Las Vegas, September (1985)

[7] Osborne OA, Oil Gas J., 28, 80 (1992)

[8] Mohaghegh S, Arefi R, Ameri S, Aminiand K, Nutter R, J. Petroleum Sci. Eng., 16(4), 263 (1996)

[9] Zhou CD, Wu XL, Cheng JA, Determining reservoir properties in reservoir studies using a fuzzy neural network, in 68th Ann. Tech. Meeting, Houston (1993)

[10] Arehart RA, Drill bit diagnosis using neural networks, in Artificial Intelligence in Exploration and Production, Texas A&M (1989)

[11] Epping WJ, Nitters G, Neural network for analysis and improvement of gas well production, in Computer Simulation Conf, Calgary (1990)

[12] Briones MF, Rojas GA, Moreno JA, Martinez ER, Application of neural network in the prediction of reservoir hydrocarbon mixture composition from production data, in 69th Ann. Tech. Meeting, New Orleans (1994)

[13] Habiballah WA, Starzman RA, Barrufet MA, Use of neural networks for prediction of vapor/liquid equilibrium K-values for light-hydrocarbon mixtures, SPE Reservoir Engineering, 121 (1996)

[14] Gharibi RB, Elsharkawy AM, Universal neural network based model for estimating the PVT properties of crude oil systems, in Asia Pacific Conf., Kuala Lumpur, Malaysia (1997)

[15] Al-Kaabi AW, Lee JW, An artificial neural network approach to identify the well test interpretation model, in 65th Ann. Tech. Meeting, New Orleans (1990)

[16] Juniardi IR, Ershaghi I, Complexities of using neural network in well test analysis of faulted reservoir, in West Reg. Meeting, Alaska

[17] Kumoluyi AO, Daltaban TS, Archer JS, Identification of well test models using high order neural networks, in European Comp. Conf., SPE 27558, Aberdeen (1994)

[18] Sung W, Yoo I, Ra S, Park H, Development of the HT-BP Neural Network System for the Identification of a Well-Test Interpretation Model, SPE Computer Applications (1996)

[19] Accarian P, Desbrandes R, Neuro-computing help pore pressure determination, Pet. Eng. Int. (1993)

[20] Anderson M, Design of Experiments, American Institute of Physics (1997)

[21] Lagaris IE, Likas A, Fotiadis DI, IEEE Trans. Neural Netw., 9(5), 987 (1998)

[22] Osborne OA, Oil Gas J., 28, 80 (1992)