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Model Parameterization and P-wave AVA Direct Inversion for Young's Impedance

机译:模型参数化和P-Wave AVA用于年轻的阻抗的直接反演

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摘要

AVA inversion is an important tool for elastic parameters estimation to guide the lithology prediction and "sweet spot" identification of hydrocarbon reservoirs. The product of the Young's modulus and density (named as Young's impedance in this study) is known as an effective lithology and brittleness indicator of unconventional hydrocarbon reservoirs. Density is difficult to predict from seismic data, which renders the estimation of the Young's impedance inaccurate in conventional approaches. In this study, a pragmatic seismic AVA inversion approach with only P-wave pre-stack seismic data is proposed to estimate the Young's impedance to avoid the uncertainty brought by density. First, based on the linearized P-wave approximate reflectivity equation in terms of P-wave and S-wave moduli, the P-wave approximate reflectivity equation in terms of the Young's impedance is derived according to the relationship between P-wave modulus, S-wave modulus, Young's modulus and Poisson ratio. This equation is further compared to the exact Zoeppritz equation and the linearized P-wave approximate reflectivity equation in terms of P- and S-wave velocities and density, which illustrates that this equation is accurate enough to be used for AVA inversion when the incident angle is within the critical angle. Parameter sensitivity analysis illustrates that the high correlation between the Young's impedance and density render the estimation of the Young's impedance difficult. Therefore, a de-correlation scheme is used in the pragmatic AVA inversion with Bayesian inference to estimate Young's impedance only with pre-stack P-wave seismic data. Synthetic examples demonstrate that the proposed approach is able to predict the Young's impedance stably even with moderate noise and the field data examples verify the effectiveness of the proposed approach in Young's impedance estimation and "sweet spots" evaluation.
机译:AVA反演是弹性参数估计的重要工具,可以指导岩性预测和油气藏的“甜点”识别。杨氏模量和密度的乘积(本研究中称为杨氏阻抗)是非常规油气藏的有效岩性和脆性指标。密度很难从地震数据中预测,这使得传统方法中对杨氏阻抗的估计不准确。在本研究中,提出了一种仅利用P波叠前地震数据的实用地震AVA反演方法来估计杨氏阻抗,以避免密度带来的不确定性。首先,基于线性化的P波近似反射率方程(以P波和S波模量表示),根据P波模量、S波模量、杨氏模量和泊松比之间的关系,导出了以杨氏阻抗表示的P波近似反射率方程。进一步将该方程与精确的Zoeppritz方程和线性化的P波近似反射率方程在P波和S波速度和密度方面进行了比较,这表明当入射角在临界角范围内时,该方程足够精确,可用于AVA反演。参数敏感性分析表明,杨氏阻抗和密度之间的高度相关性使得杨氏阻抗的估计变得困难。因此,在实用的AVA反演中,使用贝叶斯推理的去相关方案,仅利用叠前P波地震数据估计杨氏阻抗。综合算例表明,即使在中等噪声条件下,该方法也能稳定地预测杨氏阻抗,现场数据算例验证了该方法在杨氏阻抗估计和“最佳点”评估中的有效性。

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