基于LR算法同步挤压广义S变换的隐蔽河道识别

Hidden Channel Sand Bodies Identification Based on the Synchrosqueezing Generalized S-Transform with the Lucy-Richardson Algorithm

近年来,致密油气藏勘探形式发展迅猛,部分井上钻遇到了一些前期目标评价未有效识别的河道砂体,这类砂体被证实也具有良好勘探潜力,如何有效识别隐蔽类河道成为气田增储上产急需解决的突出问题。由于谱分解技术可从全频地震资料中剥离出反映河道地质特征的特定频段信息,因此该技术有助于河道识别。然而,传统时频分析方法分频结果分辨率较低,难以清晰刻画出隐蔽河道边界。为此,本文引入了LR算法同步挤压广义S变换进行地震时频谱分解。数值模拟表明,同步挤压算法极大地提高了广义S变换时频分辨率,较好地区分开了非平稳信号中不同频率信号分量,更适应于实际地震信号的时频谱分解;而LR算法则进一步消除了信号间存在的虚假交叉项。实际资料应用表明,本文提出的谱分解方法精确刻画出了川东南地区凉高山组一期“隐蔽”河道砂体的空间展布,证实了方法的有效性和优越性。

In recent years, the exploration of tight oil and gas reservoirs has developed rapidly. Some wells have encountered a few channel sand bodies that were not effectively identified by early target evaluations, and these sand bodies have been proven to have good exploration potential. How to effectively identify hidden channels has become a prominent problem that urgently needs to be solved for gas field storage and production increase. Due to the fact that spectral decomposition technology can extract specific frequency band information reflecting the geological characteris-tics of channels from full frequency seismic data, it is helpful for channel sand bodies identifica-tion. However, traditional time-frequency analysis methods have low resolution in frequency decomposition results, making it difficult to clearly detect hidden channel boundaries. Therefore, this article introduces the synchrosqueezing generalized S-transform with the Lucy-Richardson algorithm for seismic time-frequency spectrum decomposition. Numerical simulation shows that the synchrosqueezing algorithm greatly improves the time-frequency resolution of the generalized S-transform, effectively distinguishes different frequency signal components in non-stationary signals, and it is more suitable for the time-frequency decomposition of actual seismic signals. The Lucy-Richardson algorithm further eliminates false cross terms between signals. The practical application of data shows that the spectral decomposition method proposed in this paper accurately characterizes the spatial distribution of a hidden channel sand body in the Lianggaoshan Formation in southeastern Sichuan, which confirming the effectiveness and superiority of the method.