定量自动识别测井微相的数学方法  

Mathematic method for quantitative automatic identification of logging microfacies

作　　者：马世忠[1] 黄孝特[1] 张太斌[2] 

机构地区：[1]大庆石油学院石油勘探系 [2]胜利石油管理局试油试采公司

出　　处：《石油地球物理勘探》2000年第5期582-586,616,共6页Oil Geophysical Prospecting

摘　　要：此前的测井微相自动识别方法多侧重于数理统计定量分析 ,或仅采用少数几个数学参数进行识别 ,不能全面体现反映沉积环境的测井特征。本文从沉积成因角度 ,采取用反映沉积环境的全部测井相要素进行建模、识别的思路 ,建立了以测井曲线幅度、形态等 9个测井相要素作定量描述的多个数学模型 ;再用优选的测井相曲线 (特征曲线 )按各个测井相要素 (尤其是特征测井相要素 )对各测井微相建模 ,并据此用不同井的各曲线提取的测井相要素识别测井微相 ,这样就建立了一种全面而有效的定量识别测井微相的数学方法。All previous automatic identification methods for logging microfacies mostly emphasized on mathematic statistical analysis or adopted a few mathematic parameters for identification, therefore they can't fully reflect the logging characters on a depositional environment.The paper took the train of thoughts using all key logging facies elements that reflect the depositional environment for model building and identification from angle of contributing factors in deposition,built multiple mathematic models using 9 key logging facies elements (log amplitude, pattern, etc.) for quantitative description; then the model building was conducted for each microfacies according to the key logging facies elements (especially to characteristic logging facies) respectively by using optimal log of logging facies (characteristic log), and accordingly using the key logging facies elements taken from logs of different wells for identifying logging microfacies. So the mathematic method for quantitative identification of logging microfacies has been fully and effectively constructed.

关 键 词：测井资料 沉积微相 数学模型 定量自动识别 

分 类 号：P631.84[天文地球—地质矿产勘探]