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Determination of best possible correlation for gas compressibility factor to accurately predict the initial gas reserves in gas-hydrocarbon reservoirs

机译:确定气体可压缩系数的最佳可能相关关系,以准确预测天然气气藏中的初始天然气储量

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Gas compressibility factor or z-factor plays an important role in many engineering applications related to oil and gas exploration and production, such as gas production, gas metering, pipeline design, estimation of gas initially in place (GIIP), and ultimate recovery (UR) of gas from a reservoir. There are many z-factor correlations which are either derived from Equation of State or empirically based on certain observation through regression analysis. However, the results of the z-factor obtained from different correlations have high level of variance for the same gas sample under the same pressure and temperature. It is quite challenging to determine the most accurate correlation which provides accurate estimate for a range of pressures, temperatures, and gas compositions. This paper presents a novel method to accurately estimate GIIP of an Australian tight gas field through identification of the most appropriate z-factor correlations, which can accurately determine the z-factor and other PVT properties for a wide range of gas compositions, temperatures, and pressures. The sensitivity study results demonstrated that a single correlation cannot work across the range of pressures and temperatures for a certain gas sample necessary to calculate z-factor during simulation process and/or other analysis, such as material balance and volumetric estimate. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
机译:气体可压缩因子或z因子在与油气勘探和生产相关的许多工程应用中起着重要作用,例如天然气生产,气体计量,管道设计,初始储量天然气(GIIP)估算和最终采收率(UR) )来自储层的气体。有很多z因子相关性,它们可以从状态方程中导出,也可以基于回归分析的某些观察经验地得出。但是,对于相同的气体样品在相同的压力和温度下,从不同的相关性获得的z因子的结果具有较高的方差水平。确定最准确的相关性非常困难,该相关性可为一系列压力,温度和气体成分提供准确的估计。本文提出了一种通过识别最合适的z因子相关性来准确估算澳大利亚致密气田GIIP的新方法,该方法可以针对各种气体成分,温度和温度范围准确确定z因子和其他PVT属性。压力。敏感性研究结果表明,对于在模拟过程和/或其他分析(例如材料平衡和体积估计)中计算z因子所必需的某种气体样品,在压力和温度范围内,单一相关性无法起作用。 (C)2017氢能出版物有限公司。由Elsevier Ltd.出版。保留所有权利。

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