Spectral Decomposition AVO attributes for identifying potential hydrocarbon-related frequency anomalies
Spectral Decomposition AVO attributes for identifying potential hydrocarbon-related frequency anomalies
复制标题
用于识别潜在的碳氢化合物相关频率异常的频谱分解 AVO 属性
作者：
C. Han
低频地震异常长期以来一直是参与油气勘探的地球科学家感兴趣的主题，因为这种“气影”可以是直接的油气指示（DHI）。已发表的研究已经证明，由于碳氢化合物饱和，地震衰减和速度分散增加可能会导致这些问题。在2000年代，Castagna等人（2003年）、Ebrom（2004年）、Chapman等人（2005年，2006年）和Odebeatu等人（2006年）等发表了大量被引用的出版物，这一主题引起了广泛的兴趣。这些研究的共识是，预测与碳氢化合物相关的频率效应在叠加地震数据上是可检测的。此外，有人建议，低频往往表现出对流体变化的最高敏感性。(e.g. Korneev等人，2004年）。这种影响不仅从地震数据中得到了证明，而且涉及实验室试验和钻孔数据的研究也得出了类似的结论。总的来说，人们普遍认为这种影响是存在的。然而，物理原因仍不确定。多项研究还表明，碳氢化合物储层具有振幅与偏移（AVO）频率相关性（例如Chapman等人（2005，2006）、Odebeatu等人（2006）、Liu等人（2006）、Ren等人（2007）、Zhang等人（2007）、Chen等人（2008）、Wu等人（2014））。在模拟的气体饱和砂层情况下，低频往往显示振幅随炮检距的最大变化（图1a）。这些作者中的一些人已经探讨了使用AVO和谱分解（SD）来了解频率异常和与储层流体含量的潜在联系的想法。已发表的工作主要使用基于模型的技术来预测不同频率的AVO效应（图1b），然后应用这些信息来帮助解释在等频率剖面上观察到的异常。结果令人信服地表明，
地震数据的频谱内容存在差异，可以利用这些差异来改进碳氢化合物识别。这些开创性出版物的结果是本文提出的工作流程的基础。在这里，这些想法是接近从解释的角度来看，并以实际的方式使用商业解释软件应用。
Low-frequency seismic anomalies have long been a subject of interest to geoscientists involved in hydrocarbon exploration since such ‘gas-shadows’ can be a direct hydrocarbon indicator (DHI). Published studies have demonstrated evidence for them potentially resulting from increased seismic attenuation and velocity dispersion, as a result of hydrocarbon saturation. The topic gained wide interest during the 2000s with well-cited publications by Castagna et al. (2003), Ebrom (2004), Chapman et al. (2005, 2006) and Odebeatu et al. (2006), to name a few. The consensus of these studies was that hydrocarbon related frequency effects are predicted to be detectable on stacked seismic data. Furthermore, it has been suggested that low frequencies tend to show the highest sensitivity to fluid changes. (e.g. Korneev et al., 2004). The effect has been shown not only from seismic data; studies involving laboratory tests and borehole data provide similar conclusions. Overall there is general agreement that the effect exists. However the physical cause remains inconclusive. Several studies have also shown evidence that hydrocarbon reservoirs have an amplitude-versus-offset (AVO) frequency dependence (e.g. Chapman et al. (2005, 2006), Odebeatu et al. (2006), Liu et al. (2006), Ren et al. (2007), Zhang et al. (2007), Chen et al. (2008), Wu et al. (2014)). In the modelled case of gas-saturated sands, low frequencies have tended to show the greatest change in amplitude with offset (Figure 1a). The idea of using AVO and Spectral Decomposition (SD) to understand frequency anomalies and potential links to reservoir fluid content has been approached by several of these authors. The published work largely used model-based techniques to predict AVO effects for different frequencies (Figure 1b), and then applied this information to aid interpretation of anomalies observed on iso-frequencies sections. The results convincingly suggest that
there are differences in the spectral content of seismic data which could be exploited for improved hydrocarbon identification. The results of these pioneering publications is the basis from which the workflow presented in this paper was conceived. Here these ideas are approached from an interpretation perspective and applied in a practical manner using commercial interpretation software.