摘要
利用岩心和测井资料对GJ地区沉积相进行了深入分析,阜二段油层主要划分出坝砂和滩砂两类微相。针对坝砂、滩砂的不同沉积和测井响应特点,分别建立了油层、水层和干层的精细识别模型。结合油藏类型和油水分布特征,采用常规的短电极测井多参数图版划分有效储层,进而识别薄油层,并将神经网络法应用于薄油层识别。多种识别方法相互验证,提高了油层的识别精度,在老油田深度挖潜中取得了明显的效果。
Using data of logging and core,sedimentary facies in GJ area were analyzed deeply. Thus the second member of Funing Formation was mainly divided into two types of bar sandbodies and beach sandbodies. According to characteristics of sedimentary and logging response of bar and beach sandbodies,fine identification models were established for oil layer,water layer,and dry layer,respectively. Combined with reservoir type and oil- water distribution feature,the effective reservoir was divided by adopting conventional multi- parameter chart of short electrode logging,to further identify thin oil pay. And then the neural network method was applied to identify thin oil pay. Various identification methods were used to improve the accuracy of reservoir identification and obtain significant effects for tapping the potential of matured oilfields.
出处
《复杂油气藏》
2015年第3期1-5,21,共6页
Complex Hydrocarbon Reservoirs
基金
江苏油田分公司"金湖凹陷非主力层油气层识别技术及增储潜力研究"(JS 11001)
关键词
坝砂滩砂
多参数
短电极
薄油层识别
阜二段
GJ地区
江苏油田
the beach bar sandstone
multi-parameter
short electrode
identification of thin oil pay
GJ area
Jiangsu Oilfield
