Thermal conductivity is defined as the ability of a material to conduct the heat. Rock thermal conductivity is influenced by several parameters such as mineral composition, geometrical factors, porosity, and saturation condition. Value of the rock thermal conductivity is necessary in all thermal processes in petroleum engineering such as thermal methods of enhanced oil recovery. Laboratory measurement of the thermal conductivity of rock samples is time-consuming and expensive. Therefore, a large number of correlations and models have been presented to predict the rock thermal conductivity. These correlations and models are divided into three categories, i.e. mixing models, empirical and semi-empirical correlations, and theoretical models.

In this paper, it was attempted to investigate 15 different predictive mixing models of rock thermal conductivity and examine their applications for different rock types and different saturation conditions using 159 collected data points. Validity and applicability of these predictive models were discussed using graphical and statistical error analysis. Results indicated that geometric mean model and Albert model can provide an accurate estimation of rock thermal conductivity with average absolute relative deviation (AARD) of 11.58% and 13.87%, respectively. Moreover, the applicability of each model was evaluated for different conditions of rock type and saturation. This evaluation revealed that Walsh, Alishaev, and Zimmerman models are more accurate than geometric mean model and Albert model for some specific conditions of rock type and saturation. Indeed, Walsh model is the best predictive thermal conductivity model for air saturated crystalline rocks, Alishaev model is the best model for predicting thermal conductivity of water saturated crystalline rocks and Zimmerman model provides the best estimation of the thermal conductivity of water saturated dolomite rocks. It should be noted that structural properties, which affect the rock thermal conductivity, are not considered in the mixing models which is the main limitation of this type of predictive models.

导热率定义为材料传导热量的能力。岩石的导热系数受几个参数的影响，例如矿物组成，几何因素，孔隙度和饱和条件。岩石导热系数的值在石油工程的所有热过程中都是必需的，例如提高采油率的热方法。实验室测量岩石样品的热导率既费时又昂贵。因此，已经提出了大量的相关性和模型来预测岩石的热导率。这些相关性和模型分为三类，即混合模型，经验和半经验相关性以及理论模型。

本文尝试研究15种不同的岩石导热系数预测混合模型，并使用159个收集的数据点研究其在不同岩石类型和不同饱和度条件下的应用。使用图形和统计误差分析讨论了这些预测模型的有效性和适用性。结果表明，几何均值模型和阿尔伯特模型可以准确估计岩石的导热系数，平均绝对相对偏差（AARD）分别为11.58％和13.87％。此外，针对岩石类型和饱和度的不同条件，评估了每种模型的适用性。评估显示，沃尔什，阿里谢耶夫，在某些岩石类型和饱和度的特定条件下，Zimmerman和Zimmerman模型比几何均值模型和Albert模型更准确。的确，Walsh模型是空气饱和晶体岩石的最佳预测导热系数模型，Alishaev模型是预测水饱和晶体岩石的导热系数的最佳模型，而Zimmerman模型则提供了对水饱和白云岩岩石导热系数的最佳估计。应该注意的是，在混合模型中没有考虑影响岩石导热性的结构特性，这是这类预测模型的主要限制。Alishaev模型是预测水饱和结晶岩热导率的最佳模型，Zimmerman模型提供了对水饱和白云岩岩石热导率的最佳估计。应该注意的是，在混合模型中没有考虑影响岩石导热性的结构特性，这是这类预测模型的主要限制。Alishaev模型是预测水饱和结晶岩热导率的最佳模型，而Zimmerman模型提供了对水饱和白云岩岩石热导率的最佳估计。应该注意的是，在混合模型中没有考虑影响岩石导热性的结构特性，这是这类预测模型的主要限制。