Capillary pressure curve data measured through the mercury injection method can accurately reflect the pore throat characteristics of reservoir rock; in this study, a new methodology is proposed to solve the aforementioned problem by virtue of the support vector regression tool and two improved models according to Swanson and capillary parachor parameters. Based on previous research data on the mercury injection capillary pressure (MICP) for two groups of core plugs excised, several permeability prediction models, including Swanson, improved Swanson, capillary parachor, improved capillary parachor, and support vector regression (SVR) models, are established to estimate the permeability. The results show that the SVR models are applicable in both high and relatively low porosity-permeability sandstone reservoirs; it can provide a higher degree of precision, and it is recognized as a helpful tool aimed at estimating the permeability in sandstone formations, particularly in situations where it is crucial to obtain a precise estimation value.

中文翻译：

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深部储层砂岩渗透率预测新方法

通过压汞法测得的毛细管压力曲线数据可以准确反映储集岩的孔喉特征；在这项研究中，提出了一种新的方法来解决上述问题，依靠支持向量回归工具和根据斯旺森和毛细管伞参数的两个改进模型。基于前人对两组岩心塞的注汞毛细管压力（MICP）的研究数据，提出了几种渗透率预测模型，包括 Swanson、改进的 Swanson、毛细管伞形、改进的毛细管伞形和支持向量回归（SVR）模型。用于估计渗透率。结果表明，SVR模型适用于高孔渗砂岩储层和相对低孔渗砂岩储层；