基于叠前概率反演的致密砂岩甜点直接预测方法

Direct prediction of sweet spots in sandstone reservoirs based on pre⁃stack probability inversion

叠前地震反演技术可以获取多种弹性参数的空间展布特征,是识别致密砂岩甜点的一种重要手段。致密砂岩的甜点均位于内部,砂体与甜点往往具有不同的敏感弹性参数,因而需要进行反演参数之间的转换,这往往降低了预测结果的精度和效率。为此,利用叠前地震数据,提出了一种基于贝叶斯概率反演的致密砂岩甜点直接预测方法。首先,推导岩性与甜点敏感弹性参数的Zeoppritz近似方程,结合褶积模型构建敏感参数与地震记录之间的关系;然后,在贝叶斯理论的框架下,建立敏感参数的后验概率分布表达式,利用马尔科夫链—蒙特卡洛算法对后验概率分布进行抽样,并在抽样过程中充分考虑反演参数之间的相关性;最后,利用改进的贝叶斯概率反演方法实现致密砂岩甜点的直接预测。该方法引入基于条件概率分布的抽样策略以约束反演参数的采样空间,有效提高了预测结果的精度和效率。模型数据和实际数据的测试均验证了该方法的可行性。

Pre‑stack seismic inversion is an important method to obtain spatial distribution characteristics of several elastic parameters and identify sweet spots in tight sandstone.As sweet spots of tight sandstone are all located in the inner sandstone,and sand bodies and sweet spots often have different sensitive parameters,inversion parameters usually need to be converted,which reduces the inversion accuracy and efficiency.Thus,this paper proposes a direct prediction method of sweet spots in tight sandstone based on Bayesian probability inversion.Firstly,the Zeoppritz approximation equation of lithology and sensitive elastic parameters of sweet spots is derived,and then the convolution model is combined to construct the relationship between sensitive parameters and seismic records.Secondly,under the framework of Bayesian theory,the posterior probability distribution expression of sensitive parameters is established,and the Markov chain‑Monte Carlo method is adopted to sample the posterior probability distribution,with the correlation between inversion parameters during the sampling being fully considered.Finally,the improved Bayesian probability inversion method is employed to directly predict the sweet spots in tight sandstone.The proposed method introduces a sampling method based on conditional probability distribution to constrain the sampling space of inversion parameters and improve prediction efficiency and accuracy.The feasibility of the method is verified by the model data and actual data.