基于常规测井的变质岩裂缝识别方法研究

Research on fractures identification method of metamorphic rock based on conventional logging

  • 摘要: BZ19-6气田变质岩潜山储层结构复杂、多样,裂缝发育且非均质性强。在储层评价中,识别有效的裂缝是一个紧迫的难题,而这对于该气田的勘探开发具有重要的意义。以岩性主要为花岗片麻岩、夹少量侵入岩的太古界为研究层位,通过对常规测井曲线进行重极标差(R/S)分析,以识别研究层位的裂缝发育程度,并通过计算Lg(R/S)的牛顿二阶差分值预测裂缝的发育位置。进一步将R/S分析结果与岩芯薄片裂缝观察统计和电成像图解释结果进行对比,建立了利用赫尔特指数识别花岗片麻岩储层裂缝发育程度分类标准。研究表明:① 将R/S分析和牛顿差分法相结合改进的裂缝识别方法,在变质岩储层裂缝评价中具有可行性,可识别宽度>0.005 mm的裂缝;② Lg(R/S)曲线二阶差分值能够准确地识别天然裂缝的发育位置,并且K-Rxo与裂缝线密度呈正相关,相关性高;③ 岩性各向异性和裂缝充填情况对常规测井曲线R/S分析方法识别裂缝精度有影响。

     

    Abstract: The structure of the reservoir in the BZ19-6 gas field metamorphic rock sub-salt dome is complex and diverse, with developed and heterogeneous fractures. Identifying effective fractures in reservoir evaluation is an urgent problem, which is important for the exploration and development of the gas field. The study mainly focuses on the granite gneiss and Archean intrusions with a small amount of intrusive rock as the research layer. The degree of fracture development of the research layer is identified by analyzing the R/S (rescaled range) of conventional logging curves. The development position of fractures is predicted by calculating the second-order difference of Lg(R/S). Furthermore, the R/S analysis results are compared with the statistical observation of thin-section fractures and the interpretation results of electric imaging to establish a classification standard for the degree of fracture development in granite gneiss reservoirs using the Hurst index. The study shows that: 1) The improved method of combining R/S analysis and Newton's difference method is feasible for fracture evaluation in metamorphic rock reservoirs and can identify fractures with a width greater than 0.005 mm. 2) The second-order difference value of Lg(R/S) curve can accurately identify the development position of natural fractures, and K-Rxo is positively correlated with fracture line density, with high correlation. 3) Rock anisotropy and fracture filling have an impact on the accuracy of fracture identification using the conventional logging curve R/S analysis method.

     

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