摘要
总结了F井油页岩地层的测井响应特征,并利用测井资料计算油页岩的含油率;将含油率分为有机质低温干馏后的有机含油率和游离态的含油率两部分求取,利用不同方法计算有机质低温干馏后的含油率,选择与岩心数据吻合较好的计算结果作为有机质低温干馏后的含油率,再通过油页岩导电方程计算油页岩孔隙中的游离态含油率,两者相加求得总含油率。F井的油页岩地层表现为高声波时差、高自然伽马和高电阻率的测井响应特征,油页岩层段基本能准确识别;建立了F井的测井资料和含油率之间的定量关系式,计算出的油页岩含油率值与岩心数据基本一致。利用测井资料识别油页岩与计算含油率对于油页岩的深入研究有着重要意义。
In this paper,logging response characteristics of oil shale formation in Well F are summarized,and logging data are used to calculate oil yield of oil shale.Oil yield is classified into organic oil yield,which organic matter is carbonized under low temperature,and free oil yield.Different methods are used to calculate the oil yield of organic matter after low-temperature carbonization.The calculated results conforming to core date were selected to be the oil yield of organic matter after low-temperature carbonization,and the conductive equation of oil shale was used to calculate the free oil yield,both of which were combined to acquire total oil yield.Logging response of oil shale in Well F features in high acoustic travel time,high natural gamma and high resistivity,and oil shale intervals could be accurately identified in general.The quantitative formula between logging date and oil yield in Well F is set up,and the calculated oil yield of oil shale is almost the same as core date.The identification and calculation of oil yield of oil shale by logging date was significant to further study of oil shale.
出处
《国外测井技术》
2016年第5期29-32,3-4,共4页
World Well Logging Technology
基金
国家潜在油气资源(油气勘探开发利用)产学研用合作创新项目--油页岩资源综合勘探技术与仪器创新平台(OSP-02)资助
关键词
油页岩
有机碳
体积方程
含油率
神经网络
oil shale
organic carbon
volume equation
oil yield
neural network