利用叠前AVO反演预测砂岩储层有效压力

Pre-stack AVO inversion for estimating effective pressure in sandstone reservoirs

求取垂直上覆压力和孔隙压力估计有效压力的方法,由于常规的利用声波时差拟合的正常压实趋势曲线的精度较低,导致地层压力预测精度较低;岩石物理方法仅停留在实验阶段或依赖经验模型,在理论上无法准确预测有效压力;孔隙空间刚度理论为考虑压力因素的岩石物理建模提供了新思路,但有效压力研究仅停留在岩石物理建模阶段,缺少由地震资料直接稳定预测有效压力的手段。为了由地震数据直接预测有效压力,提出了利用叠前AVO反演预测砂岩储层有效压力的方法。首先,假设干岩石骨架的弹性模量比为定值,基于孔隙空间刚度理论构建了剪切模量与有效压力的岩石物理关系;其次,采用泰勒展开得到弹性参数与物性参数的线性转换关系;再次,推导有效压力、孔隙度、流体模量、基质剪切模量和密度的反射系数方程,并构建基于贝叶斯理论的叠前地震反演流程。模型测试与实际资料应用表明,即使在低信噪比情况下,所提方法依然能稳定预测地层有效压力。

The prediction accuracy of formation pressure is low when estimating effective pressure by obtaining vertical overburden pressure and pore pressure,due to the low accuracy of compaction curves fitted by acoustic travel time differences.The rock physical method only stays in the experimental stage or relies on an empirical model,failing to accurately predict the effective pressure in theory.The pore space stiffness theory provides a new approach for rock physical modeling considering pressure factors,but the study of effective pressure only remains in the stage of rock physical modeling and lacks methods to directly and stably predict effective pressure from seismic data.To predict effective pressure directly from seismic data,this paper proposes a method using pre-stack AVO inversion to predict effective pressure of sandstone reservoirs.Firstly,assuming that the elastic modulus ratio of a dry rock skeleton is constant,the rock physical relationship between shear modulus and effective pressure is established based on the pore space stiffness theory.Secondly,the linear transformation relationship between elastic parameters and physical property parameters is obtained by using Taylor expansion.Thirdly,the reflection coefficient equations of effective pressure,porosity,fluid modulus,matrix shear modulus and density are formulated,and a pre‐stack seismic inversion method based on the Bayesian theory is constructed.Model testing and practical data applications show that the proposed method can stably predict the effective pressure in formations even with a low signal-t-noise ratio.