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首页> 外文期刊>International Journal of Oil, Gas and Coal Technology: IJOGCT >Prediction of two-phase compressibility factor in gas condensate reservoirs using genetic algorithm approach
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Prediction of two-phase compressibility factor in gas condensate reservoirs using genetic algorithm approach

机译：遗传算法方法预测气体冷凝水储层中的两相可压缩因子

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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.
机译：随着试气储存器中的两相可压缩系数的实验测定是昂贵/耗费的，开发可靠的基于理论方法对于此目的至关重要。 这里，基于恒定体积耗尽实验的数据，遗传算法方法用于开发用于估计气体冷凝水储存器中的两相可压缩因子的相关性。 提出的相关性被验证了五种气体冷凝水储层的实验数据，也与雷斯等人的文献中呈现的最可靠相关性相比。 （1992）。 结果发现，遗传算法的提议相关性预测了具有良好精度的两相可压缩因子的实验值，比雷德等人更好。的（1992）相关性。

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