基于Kramers-Kronig关系的频变AVO地震P波衰减估计及其在气藏预测中的应用

中文摘要 AI 辅助

地震衰减（品质因数的倒数）的估计对于气藏预测具有重要意义。地震衰减估计中的两个关键问题是建立物理上一致的反射系数方程以及从地震数据中稳定地估计地震衰减。为解决这些问题，本研究在各向同性线性粘弹性介质框架内，从Kramers-Kronig关系出发，将粘弹性刚度矩阵表示为地震衰减的函数。在弱衰减以及界面两侧弹性参数和衰减参数对比度较小的假设下，利用散射理论推导出显式包含地震衰减项的频变PP波反射系数方程。所推导的反射系数方程不仅满足因果性约束，而且保持了紧凑的数学形式。基于该反射系数方程，在频变AVO反演框架内估计地震衰减。合成地震数据测试表明，估计的P波衰减属性对储层含气饱和度的变化敏感，含气饱和度较高的储层表现出较强的P波衰减异常。实际地震数据的应用进一步表明，P波衰减异常与含气饱和度测井曲线吻合良好，能够有效识别高含气饱和度储层。本研究为从地震数据中提取P波衰减信息以及实现气藏高分辨率预测提供了一种新方法。

英文摘要

Estimation of seismic attenuation (inverse quality factor) is important for gas reservoir prediction. Two key issues in seismic attenuation estimation are the development of a physically consistent reflection coefficient equation and stable estimation of seismic attenuation from seismic data. To address these issues, this study, within the framework of isotropic linear viscoelastic media, starts from the Kramers-Kronig relations and expresses the viscoelastic stiffness matrix as a function of seismic attenuation. Under the assumptions of weak attenuation and small elastic and attenuation contrasts across the interface, a frequency-dependent PP-wave reflection coefficient equation explicitly containing seismic attenuation terms is derived using scattering theory. The derived reflection coefficient equation not only satisfies the causality constraint, but also preserves a compact mathematical form. Based on this reflection coefficient equation, seismic attenuation is estimated within the framework of frequency-dependent AVO inversion. Synthetic seismic data tests show that the estimated P-wave attenuation attribute is sensitive to variations in reservoir gas saturation, with reservoirs of higher gas saturation exhibiting stronger P-wave attenuation anomalies. Application to field seismic data further demonstrates that the P-wave attenuation anomalies agree well with the gas saturation log and effectively identify high gas saturation reservoirs. This study provides a new approach for extracting P-wave attenuation information from seismic data and achieving high resolution prediction of gas reservoirs.

发表机构

• Chengdu University of Technology（成都理工大学）
• State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation（油气藏地质及开发工程国家重点实验室）
• Key Laboratory of Earth Exploration & Information Techniques of Ministry of Education（教育部地球探测与信息技术重点实验室）

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