非均匀介质特征参数地震随机反演方法

地下介质普遍存在非均质特性，即表征含油气储层特征的弹性、物性及流体性质在空间上是不均匀的。常规的地震随机反演主要以测井数据为核心，求取变差函数表征地下地层的空间结构特征，难以有效描述地下复杂非均质储层的空间变化特性。为此，通过描述不同非均匀介质特征参数对介质的空间扰动特性，并在贝叶斯理论框架下充分利用已知测井和地震数据中蕴含的地下地层信息，提出了基于非均匀介质特征参数的随机反演方法。该方法融合已知测井和地震数据，依据随机介质理论估算的非均匀介质特征参数能更好地描述地下储层空间结构特征，进而构建后续反演所需的先验信息模型，最后利用非常快速量子退火算法优化求解目标函数，得到波阻抗的随机反演结果。模型试算表明，非均质特征参数先验模型可以描述储层的非均质特性，为后续反演提供了可靠的地质统计先验信息。实际资料的应用进一步证明，所提方法可以较好地实现对地下复杂储层的高分辨率反演，获得更可靠的反演数据体。

Study on seismic stochastic inversion method based on characteristic parameters of inhomogeneous media

The underground media generally have heterogeneous characteristics, which means the elastic, petrophysical, and fluid parameters that characterize the oil and gas reservoirs are spatially inhomogeneous. The conventional seismic stochastic inversion mainly uses the variogram function obtained from well log data to characterize the spatial structure of underground formations, and it is difficult to effectively describe the spatial variation characteristics of underground complex heterogeneous reservoirs. Therefore, this paper first describes the characteristics of the spatial disturbance caused by different inhomogeneous media characteristic parameters to the media. Then, guided by the Bayesian theory, this paper makes use of the underground formation information contained in the known well log data and seismic data and proposes the stochastic inversion method based on the characteristic parameters of inhomogeneous media. This method integrates the given well log data and seismic data and estimates the characteristic parameters of inhomogeneous media which can better describe the spatial structure characteristics of underground reservoirs based on random medium theory. Next, these parameters are utilized to build a prior information model required for the subsequent inversion process. Finally, a very fast quantum annealing algorithm is adopted to optimize the objective function, and the stochastic inversion results are obtained. The model test shows that the prior model of heterogeneous characteristic parameters can describe the heterogeneous characteristics of the reservoirs and provide reliable geostatistical prior information for subsequent inversion. The case analysis further shows that this method can better achieve high-resolution inversion of complex underground reservoirs and obtain more reliable inversion results.