摘要
应用神经网络和分形几何等模式 ,研究了测井资料沉积微相解释方法 ;分析了测井曲线对沉积环境的敏感性 ,提取反映沉积环境的特征参数 ,利用特征参数建立了沉积微相与测井曲线形态之间的关系及沉积微相模式 ;开发了人机联作解释软件 ,将其应用于葡北油田 ,对葡I组油层的 12 0口井做沉积微相连续解释 ,取心井处理结果的符合率为91.0 % ,非取心井处理结果的平均符合率为 84.8% .
This paper deals with the interpretation method for sedimentary microfacies according to logging data. It applies updated model identification method such as artificial nerve net and fractal geometry. This kind of interpretation method sets up a sedimentary model by means of sensitivity analysis and characteristic parameter as well as the relationship between sedimentary microfacies and logging curve. Describing quantificationally sedimentary microfacies by feature parameter and geometric parameter and image feature of logging curve, we have developed explanation software of cooperation between man and computer. The application result to 120 wells in Pubei Oilfield interpreted by the software is promising.
出处
《大庆石油学院学报》
CAS
北大核心
2004年第4期18-20,共3页
Journal of Daqing Petroleum Institute
关键词
词
测井沉积微相
神经网络
分形几何
相模式
sedimentary microfacies of logging
nerve net
fractal geometry
facies model
