• 责任者：Wang Yuxi (Oil and Gas Research Center ; Peking University ; Beijing 100871 ; China) ; Tian Chuangbing
• 刊名：Acta Petrolei Sinica
• 出版年：2013
• 卷期：34(6)
• 关键词：测井曲线 ; logging curves ; 碳酸盐岩储集层 ; carbonate reservoirs ; 伊拉克 ; Iraq
• 页码：p.1088-1099
• 图表：11 illus., 1 table, 44 refs.
• 分类号：1600
• issnNo：ISSN0253-2697
• isbnNo：CN11-2128/TE

     Commonly, conventional logging data can provide high-quality cross-plot and fuzzy clustering analysis methods to recognize microfacies of clastic reservoirs with good recognition and prediction. However, these methods meet lots of challenges in the recognition of carbonate microfacies because of strong diagenetic changes, which results in some difficulties in these two methods just based on gamma-ray （GR）, spontaneous potential （SP） and deep/shallow resistivity （DR and SR） logs. As a main reservoir of the Rumaila oil field, the Mishrif Formation is a typical porous carbonate reservoir. Eight wells with complete core, borehole and logging data of the Mishrif Formation from the north Rumaila oil field were selected as standard wells. We extracted several key parameters of gamma ray, neutron and density logs to match the microfacies recognized from the standard wells and established a logging facies-micro facies transformation model based on precise core analysis. Using the Bayes stepwise discriminant, we established a well logging dis- criminating template of microfacies from the Mishrif Formation in the north Rumaila oil field based on conventional well logging data and depicted the microfacies of non-standard wells precisely by applying this template. Compared with the cross-plot and fuzzy clus- tering analysis methods, the Bayes stepwise discriminant can integrate more parameters and adapt to the quantitative microfacies rec ognition of sedimentary carbonates better.