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凝析油气藏在条件变化时可转化为高含天然气的轻质油。由于烃类的扩散特性会对测井密度和测井声波产生较大的影响，常规方法（即各种回归方法或体积模型等方法）未考虑岩心分析数据、测井数据本身的特点，强行在两种数据之间建立关系。因此，所建立的解释模型存在着误差，这种误差在凝析油气藏的条件下表现为测井中确定的孔隙度偏高，而有时这种偏高具有随机性，因此，很难用系统偏移式或其他常规方法进行校正。应用“岩心刻度测井”技术，用岩心分析密度或地面自然伽马对此测井密度或测井自然伽马进行深度归位；对岩心分析孔隙度进行加权平滑滤波，使其分辨率与测井孔隙度曲线相匹配，对岩心分析数据和测井数据进行预处理之后，在灰色理论模型ＧＭ（Ｉ，Ｎ）技术支持下，充分利用了多条测井曲线提供的地质信息，以岩心分析数据为基础，建立了测井解释孔隙度模型，针对气藏特点，采用了合适的校正方法，实用效果表明，测井解释孔隙度满足储量计算的要求。
Condensate reservoirs can be transformed into light oil with high natural gas content when conditions change. Since the diffusion characteristics of hydrocarbons will have a great influence on logging density and logging sound waves, conventional methods (ie, various regression methods or volumetric models) do not consider the characteristics of core analysis data and logging data, The relationship between the two kinds of data. Therefore, there is an error in the established interpretation model, which shows that the porosity determined by logging is high in the case of condensate reservoirs, but this kind of height is sometimes random. Therefore, it is very difficult to use the system Offset or other conventional methods of correction. Application of “core scale logging” technology, using core analysis density or natural gamma ray on the logging density or logging of natural gamma depth of the home; analysis of core porosity weighted smoothing, the resolution and measurement After the core analysis data and the well logging data are preprocessed, with the support of the gray theory model GM (I, N), the geological information provided by multiple well logs is fully utilized. Based on the core analysis Based on the data, a well log interpretation porosity model is established. According to the characteristics of gas reservoirs, a proper calibration method is adopted. The practical results show that the well logging interpretation porosity meets the requirements of reserve calculation.