Pore Structure Characterization of Eocene Low-Permeability Sandstones via Fractal Analysis and Machine Learning: An Example from the Dongying Depression, Bohai Bay Basin, China

Poroperm analysis, mercury injection capillary pressure (MICP), and nuclear magnetic resonance (NMR) measurements were performed to delineate the pore structures and fractal behaviors of the Eocene low-permeability sandstones in the Dongying Depression, Bohai Bay Basin, China. Three types of pore structures (I, II, and III) have been classified by applying the self-organizing map (SOM) clustering model. Comparative analysis of three different fractal models indicates that the MICP tubular model and NMR model are quite effective for pore structure characterization. The results show that the reservoirs generally exhibit high fractal dimensions, indicative of complex pore structures. The presence of small pore throats is primarily responsible for the heterogeneities and complexities in the Eocene low-permeability sandstones. A modified Winland model was established for the permeability estimation using MICP data. Different from high-permeability reservoirs or unconventional (e.g., shale and tight formation) reservoirs, r10 is the best parameter for permeability estimation, indicating that the permeability of the Eocene low-permeability sandstones is largely controlled by the large pore systems. Additionally, a porosity model derived from movable fluids using NMR data has been established and provided better prediction effect compared with the classic Coates and Schlumberger Doll Research (SDR) models. Fractal analysis and permeability estimation are shown to be quite effective for investigating microscopic behaviors and in predicting the reservoir quality of low-permeability sandstone reservoirs.

Automated summary

Properm analysis, MICP, and NMR were used to investigate the pore structures and fractal behaviors of Eocene low-permeability sandstones in the Dongying Depression, showing high fractal dimensions and complex pore structures. A modified Winland model was established for permeability estimation, with r10 as the best parameter. NMR data provided a more accurate porosity model compared to classic models, contributing to better prediction of reservoir quality. Fractal analysis and permeability estimation proved effective in studying microscopic behaviors and predicting reservoir quality of low-permeability sandstone reservoirs.