Stochastic reconstruction (SR) is one of the most usable molecular reconstruction approaches applicable to heavy petroleum cuts, using statistical distribution to describe the structure attribute of molecules in a mixture. Suitable values for distribution parameters are estimated during an optimization process on the base of mixture characterization data. This study focuses on searching the connection between the parameter of structure attribute distributions and different types of experimental data, used for hydrocarbon mixture characterization.

An algorithm has been introduced that allows to determine which types of analytical data affect the distribution of structure attributes based on testing statistical hypotheses. The core idea is looking for a significant correlation between calculated properties and distribution parameters, and then comparing the magnitude of parameter impact with measurement precision. It was shown that different types of analytical data can affect different parameters of the reconstructed composition: distillation curve data is the main source of information about distributions variance, while elemental analysis and PNA helps determine mean values for various structure attributes; information concerning the number of branches in molecules are strongly connected with ¹³C NMR data.

随机重建（SR）是适用于重质石油开采的最有用的分子重建方法之一，它使用统计分布来描述混合物中分子的结构属性。在优化过程中，基于混合物特征数据估算分布参数的合适值。这项研究的重点是寻找结构属性分布的参数与不同类型的实验数据之间的联系，以用于烃混合物的表征。

已经引入了一种算法，该算法允许基于测试统计假设来确定哪些类型的分析数据会影响结构属性的分布。核心思想是寻找计算出的特性与分布参数之间的显着相关性，然后将参数影响的幅度与测量精度进行比较。结果表明，不同类型的分析数据可能会影响重构成分的不同参数：蒸馏曲线数据是有关分布方差的主要信息来源，而元素分析和PNA则可以帮助确定各种结构属性的平均值。有关分子中支链数的信息与¹³ C NMR数据密切相关。