Abstract: It is considered that both lithology and macro-structure directly affect the properties of shale such as physical property, electrical property, oil-bearing property, brittleness, source rock characteristics, and geostress anisotropy, which are closely related with the distribution of shale oil sweet spots. The paper targets at the shale oil in the upper Member of Xiaganchaigou Formation in Yingxiongling structural belt of Qaidam Basin, and proposes a new method of lithofacies identification by combining the macro-structures and mineral facies based on a large number of core descriptions, as well as in-depth processing and interpretation of electrical imaging and elemental full spectrum logging data. The main technical connotation is explained as below. (1)The lamina index method is proposed to quantitatively evaluate the lamina development degree by combining the dynamic and static electrical imaging logging images, and the classification criteria of macro-structures are established. The confidence parameter is introduced to optimize the method, which makes the classification results more consistent with the detailed core descriptions, and the coincidence rate range from 89 % to 92 % . (2)Based on the mineral content precisely calculated using elemental full spectrum logging, a blind source clustering method is used to establish a classification standard of mineral facies, avoiding the subjectivity of mineral content classification standards and improving the rationality of classification results. (3)The quantitative classification standard for lithofacies combining macro-structures with mineral facies is established according to the lithofacies category of the target block. Kerogen organic carbon content and effective porosity are selected as sensitive parameters to evaluate favorable source rock facies and reservoir facies respectively, and favorable lithofacies are selected based on the source-reservoir coupling relationship. The practical application of the above method in Yingxiongling shale oil exploration and evaluation has effectively guided the optimization of oil and gas sweet spots, and the oil test results have also confirmed the optimized target of favorable lithofacies.

Key words: well logging, lamina index, kerogen organic carbon content, effective porosity, macro-structure, mineral facies, favorable lithofacies, Qaidam Basin, Yingxiongling shale oil

摘要： 岩性与宏观结构对页岩的物性、电性、含油性、脆性、烃源岩特性和地应力各向异性等属性有着直接影响,而这些属性又与页岩油甜点的分布密切相关。以柴达木盆地英雄岭构造带下干柴沟组上段页岩油为例,基于大量岩心描述以及电成像测井与元素全谱测井资料的深入处理与评价,提出了宏观结构相与矿物相融合的岩相识别方法,主要技术内涵包括:①结合动、静态电成像测井图像建立量化评价纹层发育程度的纹层指数法,以此为基础提出了宏观结构相的分类标准,并引入置信度参数对该方法进行了优化,使得分类结果更符合客观规律,与岩心精细描述结果对比的符合率达89 % ~92 % ;②基于元素全谱测井精准计算的矿物含量,采用盲源聚类方法建立了矿物相的分类标准,规避了矿物含量分类标准确定的主观性并提高了分类结果的合理性;③针对目标区块的岩相类别,建立了宏观结构相与矿物相融合的岩相量化分类标准,优选干酪根有机碳含量和储层有效孔隙度作为敏感参数,分别评价有利烃源岩岩相和有利储层岩相,并基于二者的耦合关系优选出有利岩相。该方法在柴达木盆地英雄岭页岩油的勘探评价中得到了实际应用,有力指导了油气甜点的优选,试油结果也证实有利岩相优选目标的有效性。

关键词: 测井, 纹层指数, 干酪根有机碳含量, 有效孔隙度, 宏观结构相, 矿物相, 有利岩相, 柴达木盆地, 英雄岭页岩油