Title: Prediction of two-phase compressibility factor in gas condensate reservoirs using genetic algorithm approach

Authors: Ehsan Kamari; Saber Mohammadi; Mohammad Mahdi Mohammadi; Mohsen Masihi

Addresses: Department of Petroleum Engineering, Research Institute of Petroleum Industry (RIPI), Tehran, Iran ' Department of Petroleum Engineering, Research Institute of Petroleum Industry (RIPI), Tehran, Iran ' Islamic Azad University, Science and Research Branch, Tehran, Iran ' Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, Iran

Abstract: As experimental determination of two-phase compressibility factor in gas condensate reservoirs is expensive/time-consuming, developing a reliable theoretical-based method is vital for this purpose. Here, based on data of constant-volume-depletion experiments, genetic algorithm method was used to develop a correlation for estimating the two-phase compressibility factor in gas condensate reservoirs. The proposed correlation was validated with experimental data of five gas condensate reservoirs, and also compared with most reliable correlation presented in the literature by Rayes et al. (1992). It was found that the proposed correlation by genetic algorithm predicts the experimental values of two-phase compressibility factor with a good accuracy and better than the Rayes et al.'s (1992) correlation. [Received: July 30, 2016; Accepted: February 9, 2017]

Keywords: gas condensate; two-phase compressibility factor; genetic algorithm; correlation; experimental.

DOI: 10.1504/IJOGCT.2019.098456

International Journal of Oil, Gas and Coal Technology, 2019 Vol.20 No.3, pp.266 - 281

Received: 30 Jul 2016
Accepted: 09 Feb 2017
Published online: 25 Mar 2019 *

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