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页岩储层中裂缝的发育程度直接关系到页岩气产量的高低,控制着储层的含气性。然而,页岩裂缝的有效识别和评价一直是一个难点问题。为了更好地完善页岩裂缝的测井识别与评价技术,通过大量文献调研,梳理了页岩裂缝的基本分类,系统归纳了页岩裂缝的测井识别方法和评价方法。在常规测井方法识别中,页岩裂缝在不同测井响应上的敏感程度不同,双侧向对页岩裂缝识别最为灵敏,既可以识别出裂缝发育层段,也可以识别出裂缝倾角,自然伽马、声波时差等常规测井对页岩近水平或低角度裂缝较灵敏;页岩裂缝的声电成像、阵列声波以及核磁测井响应特征也不尽相同,应结合各种测井方法对裂缝进行综合识别和评价;一些基于常规测井资料的数学方法,如小波多尺度分析、BP神经网络及灰色关联等识别方法改善了常规测井中的不足,具有一定的应用前景。
The development of fractures in shale reservoirs directly affects the production of shale gas and controls the gas content of the reservoir. However, the effective identification and evaluation of shale fractures has been a difficult issue. In order to improve logging identification and evaluation technology for shale fractures, a large number of literature surveys are used to sort out the basic classification of shale fractures and systematically summarize logging identification methods and evaluation methods for shale fractures. In the conventional logging identification, the sensitivity of shale fractures to different logging responses is different. The two-sided identification is most sensitive to shale fracture identification, which can not only identify the fracture development section, but also identify the dip angle and natural Conventional logging such as gamma and acoustic time lag is sensitive to shale horizontal or low angle fractures. The acoustic imaging, array acoustic wave and NMR logging response characteristics of shale fractures are also different. Combining various well logging methods And the fractures are comprehensively identified and evaluated. Some mathematical methods based on conventional well logging data, such as wavelet multiscale analysis, BP neural network and gray relational recognition, can improve the deficiencies of conventional logging and have certain application prospects.