An Improved Method for Q Factor Estimation based on Frequency Weighted Exponential Function

Jiang, Yilin; Ma, Yanying; Cao, S.; Zheng, Duo

doi:10.3997/2214-4609.201800944

Cited by 1 publication

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“…However, choosing an optimal symmetry index can improve this problem. Therefore, many efforts have been made to recalculate the symmetry index (Jiang et al., 2018; Li & Liu, 2015, 2016; Li et al., 2020; Zheng et al., 2018), which enhances the accuracy of the Q estimation by the average and least‐square methods. Even so, the obtained symmetry indexes are still not optimal.…”

Section: Introductionmentioning

confidence: 99%

Q estimation based on the centroid frequency and standard deviation of frequency‐weighted‐exponential function

Zhang

Meng

et al. 2022

Geophysical Prospecting

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The quality factor Q is a vital parameter for quantitatively describing the attenuation information of underground reservoirs, which is of great significance for hydrocarbon detection and reservoir characterization. A frequency-weighted-exponential (FWE) method utilizing the symmetry index and the characteristic frequency can obtain this parameter. Unfortunately, the constant symmetry index assumption of it reduces the accuracy of Q values under the non-standard FWE shape. Selecting an optimal symmetry index is also a problem for this method. Hence, the basic idea of a novel Q estimation method is to substitute the symmetry index with the standard deviation of the source and attenuated wavelet spectrums. The added standard deviation varies with the degree of attenuation under different wavelet shapes, which can reduce the effects of the constant assumption. Meanwhile, it is simple to calculate this parameter directly from the spectrums. In this way, the proposed method has wider applicability to various wavelets. The synthetic records show the better performance of the novel method in improving the accuracy of Q values and the resistance to random noise than the FWE method. Furthermore, the results of matching wavelets exhibit the applicability of the proposed method in real data, and the quadratic spectrum simulation method improves the stability of the spectrums. Finally, real data experiments indicate the effectiveness of the proposed method after the above two processing means.

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