Abstract: It is a new subject to determine reservoir sensitivity by well logging.Based on the collected data from the thin slice observation, cast slice observation, granularity analysis, mercury injection, micro scanning, porosity and permeability analysis, X-ray diffraction, core fluid sensitivity tests ect., this paper deals with the determination of reservoir sensitivity by well logging for the first time.14 parameters such as quarts, feldspar, clay volume, montmorillonite, kaolinite, illite, chlorite, medium granularity value, formation water salinity, composite poro-perm parameter, porosity, permeability, medium capillary radius value have been obtained by well logging information.The relations between logging data and reservoir sensitivity factors have been established for the studied oilfield and the results are also obtained by using these models.Based on the analysis of reservoir sensitivity and its factors, a new theoretical table is put forward, and a new single variable regression coefficient weighted method is also put forward at the same time, which are used to predict reservoir sensitivity by logging datd.The logging processing results show that the fluid velocity sensitivity, fresh water sensitivity, salt water sensitivity, acid sensitivity, and alkali sensitivity are consistent with the results of the core fluid sensitivity test of the studied area, which is of importance to mud content design, finding of commercial oil and gas layers and reservoir protection.

Key words: reservoir sensitivity, natural Gamma Ray spectrometry logging, formation damage, clay mineral, clay volume

摘要： 应用测井确定储层敏感性是一个全新的课题.在收集薄片、铸体薄片、粒度、压汞、X-衍射、扫描电镜、物性、敏感性流动实验等各种岩心分析资料的基础上,首次探讨了应用测井确定储层敏感性的问题.从测井信息中提取的石英骨架、长石骨架、岩屑骨架、泥质、蒙脱石、绿泥石、伊利石、高岭石、粒度中值、地层水总矿化度、综合物性参数、孔隙度、渗透率、毛细管半径中值等14个参数,经过岩心标定(即测井建模)后,都可由测井信息连续处理获得.以这些参数为基础进行了储层敏感性与各参数的单相关分析,提出了理论排序表,并提出了单相关系数加权的方法来实现由测井预测储层敏感性.应用该方法对我国西部某油田进行了实际处理分析,结果表明由测井连续处理得到的速敏、水敏、盐敏、土酸敏和碱敏结论与该地区的敏感性流动实验结果基本吻合.它对指导探井泥浆配方设计、及时发现有工业价值的油气层和及时进行油气层保护有重要意义.

关键词: 储层敏感性, 自然伽马能谱测井, 地层伤害, 粘土矿物, 泥质含量