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Estimation of changes in saturation and pressure from 4D seismic AVO and time-shift analysis

机译:通过4D地震AVO和时移分析估算饱和度和压力的变化

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摘要

A reliable estimate of reservoir pressure and fluid saturation changes from time-lapse seismic data is difficult to obtain. Existing methods generally suffer from leakage between the estimated parameters. We propose a new method using different combinations of time-lapse seismic attributes based on four equations: two expressing changes in prestack AVO attributes (zero-offset and gradient reflectivities), and two expressing poststack time-shifts of compressional and shear waves as functions of production-induced changes in fluid properties. The effect of using different approximations of these equations was tested on a realistic, synthetic reservoir, where seismic data have been simulated during the 30-year lifetime of a water-flooded oil reservoir. Results found the importance of the porosity in the inversion with a clear attenuation of the porosity imprint on the final estimates in case the porosity field or the vertically averaged porosity field is known a priori. The use of a first-order approximation of the gradient reflectivity equation leads to severely biased estimates of changes in saturation and leakage between the two different parameters. Both the bias and the leakage can be reduced, if not eliminated, by including higher-order terms in the description of the gradient, or by replacing the gradient equation with P- and/or S-wave time-shift data. The final estimates are relatively robust to random noise, as they present fairly high accuracy in the presence of white noise with a standard deviation of 15%. The introduction of systematic noise decreases the inversion accuracy more severely.
机译:难以从延时地震数据中可靠地估算储层压力和流体饱和度变化。现有方法通常遭受估计参数之间的泄漏。我们基于四个方程式提出了一种使用时移地震属性的不同组合的新方法:两个表示叠前AVO属性的变化(零偏移和梯度反射率),以及两个表示压缩波和切变波的叠后时移作为函数的函数。生产引起的流体特性变化。在一个实际的合成油藏上测试了使用这些方程式的不同近似值的效果,该油藏在水淹油藏30年的使用寿命中模拟了地震数据。结果发现,在先验已知孔隙度场或垂直平均孔隙度场的情况下,孔隙度在反演中的重要性以及在最终估计值上明显减弱的孔隙度印记。梯度反射率方程的一阶近似的使用导致两个不同参数之间的饱和度和泄漏变化的严重偏差估计。如果不消除偏斜和泄漏,则可以通过在梯度的描述中包括高阶术语,或者通过用P波和/或S波时移数据替换梯度方程来减少偏斜和泄漏。最终估计值对随机噪声比较鲁棒,因为在存在白噪声(标准偏差为15%)的情况下,它们的准确性很高。系统噪声的引入更加严重地降低了反演精度。

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