A series of novel heuristic numerical tools were adopted to tackle the setback of permeability estimation in carbonate reservoirs compared to the classical methods. To that end, a comprehensive data set of petrophysical data including core and log in two wells was situated in Marun Oil Field. Both wells, Well#1 and Well#2, were completed in the Bangestan reservoir, having a broad diversity of carbonate facies. In the light of high Lorenz coefficients, 0.762 and 0.75 in Well#1 and Well#2, respectively, an extensive heterogeneity has been expected in reservoir properties, namely permeability. Despite Well#1, Well#2 was used as a blinded well, which had no influence on model learning and just contributed to assess the validation of the proposed model. An HFU model with the aim of discerning the sophistication of permeability and net porosity interrelation has been developed in the framework of Amaefule’s technique which has been modified by newly introduced classification and clustering conceptions. Eventually, seven distinct pore geometrical units have been distinguished through implementing the hybridized genetic algorithm and k-means algorithm. Furthermore, a K-nearest neighbors (KNN) algorithm has been carried out to divide log data into the flow units and assigns them to the pre-identified FZI values. Besides, a cross between the ε-SVR model, a supervised learning machine, and the Harmony Search algorithm has been used to estimate directly permeability. To select the optimum combination of the involved logging parameters in the ε-SVR model and reduce the dimensionality problem, a principle component analysis (PCA) has been implemented on Well#1 data set. The result of PCA illustrates parameters, such as permeability, the transit time of sonic wave, resistivity of the unflashed zone, neutron porosity, photoelectric index, spectral gamma-ray, and bulk density, which possess the highest correlation coefficient with first derived PC. In line with previous studies, the findings will be compared with empirical methods, Coates–Dumanior, and Timur methods, which both have been launched into these wells. Overall, it is obvious to conclude that the ε -SVR model is undeniably the superior method with the lowest mean square error, nearly 4.91, and the highest R-squared of approximately 0.721. On the contrary, the transform relationship of porosity and permeability has remarkably the worst results in comparison with other models in error (MSE) and accuracy (R²) of 128.73 and 0.116, respectively.

与经典方法相比，采用了一系列新颖的启发式数值工具来解决碳酸盐岩储层渗透率估算的挫折。为此，位于Marun油田的一个完整的岩石物理数据集，包括岩心和测井两口井。井＃1和井＃2均在Bangestan油藏中完井，具有广泛的碳酸盐岩相。鉴于Lorenz系数较高（分别在1号井和2号井中分别为0.762和0.75），储层特性（即渗透率）存在广泛的非均质性。尽管好了＃1，井＃2用作盲井，对模型学习没有影响，只是有助于评估所提出模型的有效性。在Amaefule技术的框架下，开发了一种旨在识别渗透率和净孔隙度相互关系复杂性的HFU模型，该模型已通过新引入的分类和聚类概念进行了修改。最终，通过实现混合遗传算法和k，已经区分出七个不同的孔几何单元-均值算法。此外，已经执行了K近邻（KNN）算法，将日志数据划分为流单位，并将其分配给预先标识的FZI值。此外，使用了ε-SVR模型，监督学习机和Harmony Search算法之间的交叉来直接估计渗透率。为了在ε-SVR模型中选择相关测井参数的最佳组合并减少维数问题，已经在1号井上进行了主成分分析（PCA）。数据集。PCA的结果说明了参数，例如磁导率，声波的传播时间，未闪蒸区的电阻率，中子孔隙率，光电指数，光谱伽马射线和堆积密度，这些参数与第一个导出的PC具有最高的相关系数。与以前的研究相一致，将结果与经验方法，Coates-Dumanior方法和Timur方法进行比较，这两种方法均已在这些井中启动。总的来说，很明显的结论是，ε- SVR模型无疑是一种优越的方法，其均方差最低，接近4.91，R平方最高，约为0.721。相反，与其他模型相比，孔隙度和渗透率的转换关系在误差（MSE）和准确性（R ²）分别为128.73和0.116。