基于小波多分辨率分析的稀疏表示的集成4D地震历史匹配框架
An Ensemble 4D Seismic History Matching Framework with Sparse Representation Based on Wavelet Multiresolution Analysis
AI总结：
本文提出一种集成4D地震历史匹配框架，通过小波多分辨率分析和稀疏表示减少数据规模，提升储层表征精度。
摘要：
在本文中，我们提出了一种用于储层表征的集成4D地震历史匹配框架。与现有储层工程框架相比，该框架包含了一些较新的成分，包括所选地震数据的类型、用于所选地震数据的小波多分辨率分析及相关数据噪声估计，以及最近开发的迭代集合历史匹配算法的使用。典型用于历史匹配的地震数据，如声速阻抗，是反演量，反演过程中可能产生额外的不确定性。在所提出的框架中，我们避免了此类中间反演过程。此外，我们还采用基于小波的稀疏表示来减少数据规模。具体而言，我们使用从振幅对角（AVA）数据中导出的截距和梯度属性，对属性数据应用多级离散小波变换（DWT），并估计所得小波系数的噪声水平。然后，我们选择超过一定阈值的波let系数，并使用迭代集合平滑器对这些主导波let系数进行历史匹配。
英文摘要：
In this work we propose an ensemble 4D seismic history matching framework for reservoir characterization. Compared to similar existing frameworks in reservoir engineering community, the proposed one consists of some relatively new ingredients, in terms of the type of seismic data in choice, wavelet multiresolution analysis for the chosen seismic data and related data noise estimation, and the use of recently developed iterative ensemble history matching algorithms. Typical seismic data used for history matching, such as acoustic impedance, are inverted quantities, whereas extra uncertainties may arise during the inversion processes. In the proposed framework we avoid such intermediate inversion processes. In addition, we also adopt wavelet-based sparse representation to reduce data size. Concretely, we use intercept and gradient attributes derived from amplitude versus angle (AVA) data, apply multilevel discrete wavelet transforms (DWT) to attribute data, and estimate noise level of resulting wavelet coefficients. We then select the wavelet coefficients above a certain threshold value, and history-match these leading wavelet coefficients using an iterative ensemble smoother. (The rest of the abstract is omitted for exceeding the limit of length)
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