Pore space of tight sandstone samples exhibits fractal characteristics. Nuclear magnetic resonance is an effective method for pore size characterization. This paper focuses on fractal characteristics of pore size from nuclear magnetic resonance (NMR) of tight sandstone samples. The relationship between the fractal dimension from NMR with pore structure and water saturation is parameterized by analyzing experimental data. Based on it, a pore structure characterization and classification method for water-saturated tight sandstone and a water saturation prediction method in a gas-bearing sandstone reservoir have been proposed. To verify the models, the fractal dimension from NMR of 19 tight sandstone samples selected from the gas-bearing tight sandstone reservoir of Shahejie Formation in Nanpu Sag and that of 16 of them under different water saturation states are analyzed. The application result of new methods in the gas-bearing tight sandstone reservoir of Shahejie Formation in Nanpu Sag shows consistency with experimental data. This paper has facilitated the development of the NMR application by providing a non-electrical logging idea in reservoir quality evaluation and water saturation prediction. It provides a valuable scientific resource for reservoir engineering and petrophysics of unconventional reservoir types, such as tight sandstone, low porosity, and low permeability sandstone, shale, and carbonate rock reservoirs.

Micro-pore structures are an essential factor for the electrical properties of porous rock. Theoretical electrical conductivity models considering pore structure can highly improve the accuracy of reservoir estimation. In this study, a pore structure characterization method based on a multi-fractal theory using capillary pressure is developed. Next, a theoretical electrical conductivity equation is derived based on the new pore structure characterization method. Furthermore, a distinct interrelationship between fractal dimensions of capillary pressure curves ($D_v$) and of resistivity index curves (${\mathrm{D}}_t$ and ${\mathrm{D}}_r$) is obtained. The experimental data of 7 sandstone samples verify that the fitting result by the new pore structure characterization method is highly identical to the experimental capillary pressure curves, and the accuracy of the improved rock resistivity model is higher than the Archie model. In addition, capillary pressure curves can be directly converted to resistivity index curves according to the relationship model between fractal dimensions of capillary pressure curves ($D_v$) and resistivity index curves (${\mathrm{D}}_t$ and ${\mathrm{D}}_r$). This study provides new ideas to improve the accuracy of pore structure characterization and oil saturation calculation; it has good application prospects and guiding significance in reservoir evaluation and rock physical characteristics research.

The pore structure of sandstone reservoir, primarily influenced by depositional parameters and diagenetic features, is crucial for petroleum engineering and petrophysics. Core analysis shows that samples from different sedimentary micro-facies can be grouped into distinct areas on a cross-plot of porosity versus permeability, and submerged distributary channels facies have the best pore structure, followed by river mouth bars facies, distal bar/blanket sand facies, and bay facies. In order to quantitatively characterize the influence of sedimentary microfacies and diagenesis on pore structure, seepage factor f (slope of the line through (\(\phi\), \({\text{Log}}_{10}\left(K\right)\)) and (\({\phi }_{max}\), \({\text{Log}}_{10}\left({K}_{max}\right)\)) on porosity–permeability cross-plot) and diagenetic indictor C_d (relative value of f value to the center of the f range) are introduced to quantitatively characterize the effects of sedimentary micro-facies and diagenesis on pore structure, respectively. Results show that the smaller the f value, the more favorable the sedimentary micro-facies are to reservoir pore structure. And within a specific distribution range centered on f, the larger the C_d value, the more favorable the diagenesis is to reservoir pore structure. Application results are consistent with petrographic features from core analysis, which indicates that the method is effective. This method provides a quantitative characterization method of the mapping relationship between macroscopic geological and microscopic pore structure, and can be effectively applied to reservoir analysis and pore structure characterization.

Resistivity anisotropy in bedded shales poses significant challenges for reservoir assessment in high-angle wells, mainly due to discrepancies between logg

Resistivity is a key parameter in shale reservoir characterization. Diverse micro-pore types and complex conduction mechanisms in shale result in poor accuracy when applying existed conductivity models. Establishing a high-precision conductivity response model requires comprehensive consideration of the pore structure and clay-bound water conduction. The primary novelty of this work lies in replacing macroscopic empirical fitting parameters with a mechanistic, multi-dimensional fractal framework. We develop a novel conductivity response characterization model that explicitly couples multi-dimensional fractal pore structure theory with clay-bound water conduction. Experimental data verification demonstrates the new model’s superior characterization accuracy. Results indicate three distinct zones in the shale conductivity-pore water conductivity relationship: a nonlinear zone, a transition zone, and a linear zone. A higher cation exchange rate on clay surfaces leads to an increase in the nonlinear characteristics of the conductivity for both the shale and the pore water in low-salinity regions. An increase in the values of the conduction path fractal dimension, pore morphology fractal dimension, and pore fractal dimension all contribute to reduced shale conductivity. While sharing clay-induced conductivity terms with conventional dual-water and shale volume models, the new model offers advantages in operational simplicity and parameter accessibility. This research provides a physically rigorous and highly accessible approach for conductivity-based reservoir parameter calculation, offering new technical perspectives for complex shale oil/gas evaluation.