Evaluating pore structure of unconventional shale reservoirs enables us to determine their productivity, allowing for better operational decisions. Despite extensive studies in this field, considering the complexity of shale plays, pore structure analysis of such formations still requires novelties and further research. In this study, 10 samples from the Qingshankou Formation (from 5 wells) were analyzed with X-ray diffraction (XRD), programmed pyrolysis, N₂ adsorption, and mercury intrusion capillary pressure (MICP). In the next step, several modern intelligent smart models including multilayer perceptron (MLP), radial basis function (RBF), generalized regression neural network (GRNN), cascaded forward neural network (CFNN), and least squares support vector machine (LSSVM), that were optimized by levenberg-marquardt (LM), Bayesian regularization (BR), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DE) algorithms were employed, to estimate the volumes of N₂ adsorbed and desorbed based on the mineralogy and geochemical properties of the samples. Results show that samples are mainly composed of clay (up to 42.3 wt%) and quartz (up to 34.6 wt%), low in total organic carbon (TOC) (up to 2.89%) and in the oil generation window. Complexity of smaller pores was found higher compared to medium and larger ones. In addition, deconvolution of N₂ adsorption pore size distribution (PSD) curve revealed that samples are composed of up to three families in the range of macropore size and different families in mesopore size. We found that LSSVM with applicability to the entire input dataset, outperformed all other models in predicting the amount of nitrogen adsorption and desorption with an average absolute percent relative error (AAPRE) value of 1.94%. Ultimately, clay minerals and potash feldspar had the greatest effect on increasing and decreasing the amount of nitrogen adsorbed and desorbed, respectively. The Leverage technique's findings demonstrate that more than 97% of total data points in the LSSVM model are in the valid domain. This study proves that smart methods if used properly, would enable us to study a large group of samples independent from exhaustive, time consuming and expensive experimental methods.

评估非常规页岩储层的孔隙结构使我们能够确定其生产力，从而做出更好的运营决策。尽管在该领域进行了广泛的研究，但考虑到页岩区的复杂性，对此类地层的孔隙结构分析仍然需要创新和进一步研究。_{在本研究中，采用 X 射线衍射 (XRD)、程序热解、N 2}分析了青山口组的 10 个样品（来自 5 个井）吸附和压汞毛细管压力 (MICP)。下一步，包括多层感知器（MLP）、径向基函数（RBF）、广义回归神经网络（GRNN）、级联前向神经网络（CFNN）和最小二乘支持向量机（LSSVM）在内的几个现代智能智能模型，采用 levenberg-marquardt (LM)、贝叶斯正则化 (BR)、遗传算法 (GA)、蚁群优化 (ACO)、粒子群优化 (PSO) 和差分进化 (DE) 算法进行优化，以估计N _2的体积根据样品的矿物学和地球化学性质进行吸附和解吸。结果表明，样品主要由粘土（最高42.3 wt%）和石英（最高34.6 wt%）组成，总有机碳（TOC）含量低（最高2.89%），处于生油窗口。与中等和较大孔隙相比，较小孔隙的复杂性更高。此外，N _{2的反卷积}吸附孔径分布（PSD）曲线显示，样品由大孔径范围内的三个家族和中孔径范围内的不同家族组成。我们发现适用于整个输入数据集的 LSSVM 在预测氮吸附和解吸量方面优于所有其他模型，平均绝对百分比相对误差 (AAPRE) 值为 1.94%。最终，粘土矿物和钾长石分别对增加和减少氮吸附和解吸量的影响最大。Leverage 技术的研究结果表明，LSSVM 模型中超过 97% 的总数据点位于有效域中。这项研究证明，如果使用得当，聪明的方法将使我们能够研究大量独立于详尽的样本，