A New Method for Predicting the Permeability of Sandstone in Deep Reservoirs

• Main Authors: Feisheng Feng, Pan Wang, Zhen Wei, Guanghui Jiang, Dongjing Xu, Jiqiang Zhang, Jing Zhang
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2020-01-01
• Series: Geofluids
• Online Access: http://dx.doi.org/10.1155/2020/8844464

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.