噪音背景下流体识别应用方法探讨

地震资料处理中,难免存在噪声,因此在频率域储层识别的研究中,低频噪音问题是干扰储层识别的重要影响因素,在噪音问题上必须采取有效的手段,才能排除低频信息是流体的响应而不是噪音的响应。据此在储层检测手段上,首先要进行噪音对算法的干扰分析,只要在检测方法上噪音不产生干扰,那么用到实际地震资料流体识别上才有意义,通过在噪音背景下的模型进行试算,优选了抗噪性好、分辨率高的小波基函数对实际资料进行处理。另外,对地震流体识别可靠性检验方面,主要是通过测井解释来验证地震检测流体的可靠性。研究结果表明,优选的小波基函数不仅在模型上取得了很好的效果,在研究区过井地震资料上存在低频异常,与测井解释油气显示基本吻合。

Spectrum decomposition techniques and applications discussion based on noised-seismic data in the fluid Identification

The purpose of this paper was to perform optimize the best wavelet basis function and well controlled-TFA (Time frequency analysis) techniques on a target, in order to provide high-resolution instant spectrum data to help in the fluid detection. The high-resolution frequency counts with several processes designed specifically to cover the need for noise removal and preservation of signal and relative amplitudes; however, it was decided to run a different wavelet basis sequence designed to attenuate some potential noise existing in the data and to stabilize the spectrum of the data in the target window. In order to attenuate the random noise and potential acquisition/processing footprint, different wavelet basis was tested on a subset of the seismic volume in order to determine the optimum parameters on the noised-sinusoidal model. Several wavelet basis were tested for the frequency recognition capability on the model, and then the optimum wavelet base function was used in seismic data. The optimal wavelet basis was selected to test in the acoustic wave, also strong amplitude anomaly showed. Analysis of the subset of the seismic volume and correlation with well data interpretation on the oil. And so may be use the well-log interpretation result to guarantee that the strong amplitude anomaly have effects at the target.