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.

Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models.

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 coal reservoir in the Ordos Mizhi block is buried at a depth of over 2000 m. This study aims to obtain the characteristics of the coal reservoir in the Mizhi block through various experimental methods and combine the gas-bearing characteristics obtained from on-site desorption experiments to analyze the gas content and logging response characteristics of the study area. On this basis, a reservoir parameter interpretation model for the study area is established. This provides a reference for the exploration and development of coal-rock gas in the Mizhi block. The research results show that: (1) The study area is characterized by the development of the No. 8 coal reservoirs of the Benxi Formation, with a thickness ranging from 2 to 11.6 m, averaging 7.2 m. The thicker coal reservoirs provide favorable conditions for the formation and storage of coal-rock gas. The lithotypes are mainly semi-bright and semi-dark. The coal maceral is dominated by the content of the vitrinite, followed by the inertinite, and the exinite is the least. The degree of metamorphism is high, making it a high-grade coal. In the proximate analysis, the moisture ranges from 0.36 to 1.09%, averaging 0.65%. The ash ranges from 2.34 to 42.17%, averaging 16.57%. The volatile ranges from 9.18 to 15.7%, averaging 11.50%. The fixed carbon ranges from 45.24 to 87.51%, averaging 71.28%. (2) According to the results of scanning electron microscopy (SEM), the coal samples in the Mizhi block have developed fractures and pores. Based on the results of the carbon dioxide adsorption experiment, the micropore adsorption capacity is 7.8728–20.3395 cm³/g, with an average of 15.2621 cm³/g. The pore volume is 0.02492–0.063 cm³/g, with an average of 0.04799 cm³/g. The specific surface area of micropores is 79.514–202.3744 m²/g, with an average of 153.5118 m²/g. The micropore parameters are of great significance for the occurrence of coal-rock gas. Based on the results of the desorption experiment, the gas content of the coal rock samples in the study area is 12.97–33.96 m³/t, with an average of 21.8229 m³/t, which is relatively high. (3) Through the correlation analysis of the logging parameters of the coal reservoir, the main logging response parameters of the reservoir are obtained. Based on the results of the logging sensitivity analysis of the coal reservoir, the interpretation model of the reservoir parameters is constructed and verified. Logging interpretation models for parameters such as industrial components, microscopic components, micropore pore parameters, and gas content are obtained. The interpretation models have interpretation effects on the reservoir parameters in the study area.

Many studies have shown that the thermal evolution degree is the main factor affecting the micropore structure of coal reservoirs. However, within the same thick coal seam, the R_o,max of the entire coal seam is not much different, which affects the determination of the main controlling factors of pore structure heterogeneity. Therefore, No. 8 coal collected from Benxi Formation in the eastern margin of Ordos was taken as an example, and 16 samples were selected for low-temperature liquid nitrogen, carbon dioxide adsorption, and industrial component tests. Based on heterogeneity differences of R_o,max, industrial components and pore volume distribution of adsorption pores (pore diameter is less than 100 nm), the main controlling factors affecting the micropore structure of ultra-thick coal seams, were discussed. Then, the surface free energy theory was used to study the influencing factors affecting surface free energy variations during coal adsorption. First of all, R_o,max is not the main controlling factor affecting the micropore-fracture structure, as the effects of industrial components on the micropore structure are obvious, which indicates that industrial components are the main factors affecting vertical differences in the micropore structure within the same thick coal seam. Second of all, R_o,max and industrial components affect the adsorption process. When the adsorption pressure is lower, the adsorption volume and adsorption potential increase rapidly. When the adsorption pressure is higher (pressure is larger than 15 Mpa), the adsorption capacity and potential tend to be stable. Moreover, the maximum surface free energy increases with the increase in coal rank, which indicates that the degree of thermal evolution is the core factor affecting the adsorption free energy, but it is also controlled by the influence of industrial components (ash content). Lastly, micropores affect the adsorption capacity, and mesopores have little effect on the adsorption capacity, since micropores restrict the adsorption capacity and change the adsorption process by affecting surface free energy variations. The refined characterization of pore-fracture structures in deep coal reservoirs plays a crucial role in the occurrence and seepage of coalbed gas. This research can provide a theoretical basis for the efficient development of deep coalbed gas in the target area. This study aims to identify the primary factors controlling micropore structures in No. 8 coal from the Benxi Formation and to analyze the role of industrial components, which has been overlooked in previous research.

The Lower Cambrian Qiongzhusi formation and the Lower Silurian Longmaxi Formation are the two most important shale strata. Although differences between these two shales have become the focus of current research, a comparative study of the depositional environments has not been performed. Using cores of both Longmaxi and Qiongzhusi formations of well W201, the in situ comparison of the sedimentary environment was realized, and the interference of other factors was eliminated, which made the results more reliable. In this study, 72 samples from both formations were collected from well W201, Weiyuan shale gas field, Sichuan Basin. A systematic study, including total organic carbon (TOC) content, mineral composition, and major/trace elemental analyses, was conducted to elucidate the paleoenvironments of the Qiongzhusi and Longmaxi formations. The results show both formations were deposited in non-sulfidic environments. The depositional conditions of the Longmaxi formation varied from reducing to oxidizing from bottom to top. The detrital flow happened during the deposition of the Qiongzhusi formation, which resulted in three stages of the redox conditions, from anoxic to oxic and then to anoxic from bottom to top of the Qiongzhusi formation. The anoxic conditions of the Qiongzhusi formation were considerably stronger than those of the Longmaxi formation. Both formations were deposited in warm and humid climates. Ratios of Eu/Eu*, Y/Y*, La_N/Yb_N, light rare earth element (LREE) and heavy rare earth element (HREE) revealed that the Longmaxi formation was primarily controlled by seawater, whereas the Qiongzhusi formation was jointly influenced by seawater and hydrothermal fluid. The organic matter enrichment for the Longmaxi and Qiongzhusi formations was controlled by paleoproductivity and redox conditions. Due to the slightly lower paleoproductivity and influence of detrital input, the degree of organic matter enrichment in the Qiongzhusi formation was lower than that in the Longmaxi formation.