基于AVO反演的频变流体识别方法

研究表明流体引起衰减与频散往往表现为频变AVO现象.一些频散地震属性,例如纵波频散,已经证实为可靠的碳氢指示因子.为了更有效地识别流体,基于f-μ-ρ近似构建了新的流体因子Df,即频变流体项.该属性的反演首先需要连续小波变换(CWT)谱分解得到不同频带地震数据,通过去相关与先验约束来保证反演结果可靠性.模型试算证实了频变反射系数近似公式的精度可靠性,Df可以识别出强衰减介质所引起的频散现象.实际数据试算中,Df可以较好地识别储层孔隙流体,尤其对于气层,具有较好的指示效果.该流体因子将Gassmann流体项的高孔隙流体敏感性与叠前数据丰富的振幅频率信息相结合,反演效果与岩石物理认识相符.此研究有助于利用衰减频散现象借助AVO反演实现流体识别.

Frequency-dependent fluid identification method based on AVO inversion

Researches suggest that fluid-sensitive dispersion generally leads to a frequency-dependent AVO response. Frequency-dependent seismic attributes, such as P-velocity dispersion, have proven to be practical carbon indicators in both poststack inversion and prestack inversion. In terms of more efficient fluid identification consideration, we propose a new fluid factor D_f, the frequency-dependent fluid term, based on f-μ-ρ AVO approximation. The algorithm to estimate D_f is based on CWT spectral decomposition and incorporates a priori information to enhance inversion stability. Model test demonstrates the accuracy of the frequency-dependent approximation, and the feasibility of D_f to distinguish attenuative medium. Applications to real data show D_f can characterize hydrocarbon especially gas clearly. The meaning of this study is that the constructed fluid factor, associating high sensitivity of Gassmann fluid term to pore fluid with sufficient amplitude and frequency information of prestack data, performs well as we expect from rock physics experience. And this may provide an insight into how to bridge the gap between conventional AVO and attenuation and dispersion of in-situ oil and gas.