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成岩相研究在优质储层预测中具有重要的指示作用,利用常规测井曲线直接识别储层成岩相类型难度很大,本文以五号桩油田桩62-66块沙三下Ⅰ油组储层为例,选取自然伽马(GR)、声波时差(AC)、深侧向电阻率(Rt)、浅侧向电阻率(Rxo)、密度(DEN)、补偿中子(CNL)、自然电位(SP)等7条曲线,基于主成分分析法,构建了F1~F7共7个主成分变量,选取其中累计贡献率大于90%的F1~F4四个主成分建立了有效的成岩相测井识别模型,通过取心井的实际资料处理,验证了方法的准确性,从而为油田下一步进行优质储层预测工作提供地质依据.
The study of diagenetic facies plays an important role in the prediction of high-quality reservoirs. It is very difficult to identify the facies of diagenetic facies by conventional logging curves. In this paper, As an example, we selected natural gamma (GR), acoustic time difference (AC), deep lateral resistivity (Rt), shallow lateral resistivity (Rxo), density (DEN), compensated neutron (CNL) SP). Based on the principal component analysis, a total of seven principal component variables (F1 ~ F7) were constructed. Four main components F1 ~ F4, whose accumulated contribution rate was more than 90%, were selected to establish an effective diagenetic well log identification The model, through the actual data processing of coring well, verified the accuracy of the method and provided the geological basis for the next high-quality reservoir prediction in the oilfield.