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The application of quantitative gas saturation estimation based on the seismic wave dispersion inversion

机译:地震波频散反演定量瓦斯饱和度估算方法的应用

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Trace volumes of pore gas result in a drastic reduction of P-wave velocity, making it hard to determine the degree of gas saturation in a reservoir from P-wave velocity or related seismic attributes. An analysis of the seismic data suggests that the presence of hydrocarbons in reservoir units results in higher relative degrees of seismic wave dispersion and attenuation. These effects are generally ignored during a conventional seismic data analysis and inversion. In this paper, we applied a crossplot inversion method to estimate gas saturation. Based on the modeling results, a useful method for the quantitative determination of gas saturation is established based on the poststack seismic dataset Gas saturation is a frequency-dependent attribute, which allows a crossplot inversion by way of a time frequency decomposition of the seismic data. The modeling of reflections from the interface between a medium that disperses seismic waves and its elastic overburden indicates that the reflection coefficient is frequency-dependent and varies with gas saturation. This relationship may in turn vary with the numerical model used to describe the reservoir medium (e.g., sand) and with the depositional environment, which affect the amplitude-versus-offset behavior at the interface. Applying this method to field seismic data shows frequency-dependent anomalies similar to those predicted by the model. The gas saturation predicted by these methods has also been verified by well data. (C) 2015 Elsevier B.V. All rights reserved.
机译:痕量的孔隙气体导致P波速度急剧降低,因此很难根据P波速度或相关的地震属性来确定储层中的气体饱和度。对地震数据的分析表明,储层单元中碳氢化合物的存在会导致地震波分散和衰减的相对程度更高。在常规地震数据分析和反演期间,这些影响通常被忽略。在本文中,我们采用了交叉图反演方法来估算气体饱和度。根据建模结果,基于叠后地震数据集,建立了定量确定天然气饱和度的有用方法。天然气饱和度是一种与频率有关的属性,它可以通过地震数据的时频分解进行交会图反演。来自分散地震波的介质与其弹性覆盖层之间的界面反射的建模表明,反射系数与频率有关,并且随气体饱和度而变化。该关系反过来可能随用于描述储层介质(例如,沙子)的数值模型以及随沉积环境而变化,这影响了界面处的振幅对偏移行为。将这种方法应用于现场地震数据显示出与频率相关的异常,与模型预测的异常相似。用这些方法预测的气体饱和度也已通过井数据验证。 (C)2015 Elsevier B.V.保留所有权利。

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