基于马尔科夫链先验模型的贝叶斯岩相识别  

Bayesian facies identification based on Markov-chain prior model

作　　者：王芳芳[1,2] 李景叶[1,2] 陈小宏[1,2] 

机构地区：[1]中国石油大学(北京)油气资源与探测国家重点实验室,北京102249 [2]中国石油大学(北京)CNPC物探重点实验室,北京102249

出　　处：《石油地球物理勘探》2014年第1期183-189,306,共7页Oil Geophysical Prospecting

基　　金：国家自然科学基金项目(41074098;U1262207)资助

摘　　要：基于贝叶斯判别法的基本理论,通过实际应用,文中归纳出基于马尔科夫链先验模型的贝叶斯判别法识别岩性的主要步骤:①选择确定关键井,联合应用岩心、测井信息定义岩相;②进行岩石物理分析,扩展训练数据,由测井数据合成地震属性,估算地震岩相的条件概率密度函数;③将测井数据得到的地震属性利用Backus平均进行尺度放大;④利用基于马尔科夫链先验模型的贝叶斯判别方法,得到样点的后验概率密度函数,从而获得岩相的最大后验概率解。为了验证方法的有效性,将该方法应用于模型数据和海上实际资料,并采用熵和贝叶斯分类混淆矩阵对加入马尔科夫链前、后的效果进行评价,得到了较好的岩相预测结果。According to Bayesian classification theory, we propose in this paper a workflow for facies identification based on Markov-chain prior model. First we choose the key well logs and define different facies based on the log data and core data. Then we extend log data as training data through rock physics modeling,synthetize seismic attributes,and estimate facies-dependent conditional probability density function.After that,we upscale seismic attributes by Backus average model. Finally we obtain posterior probability density functions by Bayesian classification on the basis of a Markov-chain prior model,and obtain the solution of the maximum posterior probability. We have tested this method on model data and marine seismic data with entropy and Bayesian confusionmatrix.Test results prove that this method provides very good facies prediction.

关 键 词：岩相识别 贝叶斯判别 马尔科夫链 熵 分类混淆矩阵 

分 类 号：P631[天文地球—地质矿产勘探]