摘要
在钻井过程中,取心及岩屑录井资料都十分有限,以往通过岩心观察或者综合分析岩屑录井资料来进行岩性和沉积环境研究的方法受到很大限制,而利用各种测井资料所提供的丰富信息来进行沉积相研究已成为发展趋势。通过研究曲流河沉积相中各沉积微相的特征及在测井曲线上的响应特征,提取各不同沉积微相的测井相特征参数,建立曲流河沉积环境的各沉积微相的测井相模式及特征参数样本,利用BP神经网络技术反馈学习,获得一套适合研究区曲流河沉积微相的判别系数,并对其他实际测井资料进行沉积微相自动识别,所得结果与地质专家解释结果吻合率在84%以上,效果显著。由此表明在油气勘探进程中采用该方法进行沉积微相自动识别是切实可行的,可大大提高储层解释的速度和精度。
Previously,lithologies and sedimentary environments were commonly identified through core observation and/or comprehensive analysis of sample log data.However,the application of these methods is constrained because the core data and sample log data acquired during drilling are limited.The trend is to identify sedimentary facies through the integration of various log data.According to the features of sedimentary microfacies of meandering rivers and their corresponding responses on logs,we extracted the characteristic parameters of electrofacies for each sedimentary microfacies,and established electrofacies models and characteristic parameter samples for each sedimentary microfacies of meandering rivers.A set of discriminant coefficients suitable for sedimentary microfacies of meandering rivers were obtained through feedback learning with BP neural network,and were used to automatically identify sedimentary microfacies from real log data.The coincidence rate of the results with the interpretations of geologists is over 84%,indicating that this method is feasible and can greatly enhance the efficiency and accuracy of reservoir interpretation.
出处
《天然气工业》
EI
CAS
CSCD
北大核心
2010年第2期48-51,共4页
Natural Gas Industry
关键词
曲流河
沉积微相
测井相
模糊数学
特征
解释
meandering river,sedimentary microfacies,electrofacies,fuzzy mathematics,feature,interpretation
作者简介
常文会，1966年生，高级工程师，博士研究生；现任中国石化集团华北石油局测井公司经理，从事测井解释方法研究及管理工作。地址：（453700）河南省新乡市洪门。电话：（0373）5795601。E-mail：hbsjchangwh@vip．163．com
