Fluid identification based on frequency-dependent AVO attribute inversion in multi-scale fracture media
Fluid identification based on frequency-dependent AVO attribute inversion in multi-scale fracture media
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DOI：
10.1007/s11770-014-0454-0
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发表时间：
2014-12
影响因子：
0.7
通讯作者：
Cai Liu;Bonan Li;Xuan Zhao;Yang Liu;Q. Lu
中科院分区：
文献类型：
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作者：
Cai Liu;Bonan Li;Xuan Zhao;Yang Liu;Q. Lu
地震反演的一个关键问题是储层流体的识别。由于地震波速度和地层密度等弹性参数对地震波的敏感性不高，常规的振幅随炮检距变化（AVO）方法已不适用。频率相关AVO方法考虑地震振幅对频率的依赖性，并利用这种依赖性来获得关于储层裂缝中流体的信息。提出了一种改进的基于Chapman模型参数化的贝叶斯反演方法。所提出的方法是基于1）非弹性属性反演FDAVO方法和2）贝叶斯统计流体识别。首先，我们通过制定误差函数来反演非弹性断裂参数，该误差函数用于匹配观测数据和模型数据。其次，我们确定流体类型，通过使用马尔可夫随机场先验模型考虑来自不同来源的数据，如叠前反演和测井。我们考虑非弹性参数，以利用不同流体之间的粘度差异。最后，我们使用最大后验概率获得最佳的岩性/流体识别结果。
A key problem in seismic inversion is the identification of the reservoir fluids. Elastic parameters, such as seismic wave velocity and formation density, do not have sufficient sensitivity, thus, the conventional amplitude-versus-offset (AVO) method is not applicable. The frequency-dependent AVO method considers the dependency of the seismic amplitude to frequency and uses this dependency to obtain information regarding the fluids in the reservoir fractures. We propose an improved Bayesian inversion method based on the parameterization of the Chapman model. The proposed method is based on 1) inelastic attribute inversion by the FDAVO method and 2) Bayesian statistics for fluid identification. First, we invert the inelastic fracture parameters by formulating an error function, which is used to match observations and model data. Second, we identify fluid types by using a Markov random field a priori model considering data from various sources, such as prestack inversion and well logs. We consider the inelastic parameters to take advantage of the viscosity differences among the different fluids possible. Finally, we use the maximum posteriori probability for obtaining the best lithology/fluid identification results.