The Chang 7 member of the Triassic Yanchang Formation in the Ordos Basin is a significant continent shale oil reservoir in China. Therefore, conducting an in-depth investigation into the pore structure and fluid mobility characteristics of the Chang 7 shale oil reservoir holds immense importance for advancing shale oil exploration. This study conducts a detailed analysis of the pore structures and their impact on fluid mobility of the Chang 7 shale oil reservoir using multiple methodologies, i.e., a cast thin section, scanning electron microscopy (SEM), X-ray diffraction (XRD), high-pressure mercury injection (HPMI), low-temperature nitrogen adsorption (LTNA), and nuclear magnetic resonance (NMR). The results show that the sandstone in the Yanwumao area of the Chang 7 shale oil reservoir consists mainly of lithic arkose and feldspathic litharenite, which can be classified into three lithofacies (massive fine-grained sandstone (Sfm), silt-fine sandstone with graded bedding (Sfgb), and silt-fine sandstone with parallel bedding (Sfp)). Moreover, three pore structures (Type I, II, and III), and four pore spaces (nanopores, micropores, mesopores, and macropores) can be characterized. Pore structure Type I, characterized by large pores, exhibits bimodal pore diameter curves, resulting in the highest levels of movable fluid saturation (MFS) and movable fluid porosity (MFP). Pore structure Type II demonstrates unimodal pore structures, indicating robust connectivity, and higher MFS and MFP. Pore structure Type III primarily consists of dissolved and intercrystalline pores with smaller pore radii, a weaker pore configuration relationship, and the least fluid mobility. Furthermore, a correlation analysis suggests that the pore structure significantly impacts the fluid flowability in the reservoir. Favorable petrophysical properties and large pores enhance fluid flowability. Micropores and mesopores with high fractal dimensions have a greater impact on reservoir fluid mobility compared to macropores and nanopores. Mesopores mainly control MFS and MFP, while micropores govern the shift from bound fluid to movable fluid states. Among the lithofacies types, the Sfm lithofacies exhibit the highest fluid mobility due to their significant proportion of macropores and mesopores, whereas the Sfgb lithofacies have lower values because they contain an abundance of micropores. The Sfp lithofacies also dominate macropores and mesopores, resulting in medium fluid mobility levels. This study combines lithofacies types, micro-reservoir pore structure characteristics, and mobile fluid occurrence characteristics to better understand the dominant reservoir distribution characteristics of the Chang 7 shale oil reservoirs in the Ordos Basin and provide theoretical information for further optimization of production strategies.

The successful development of shale gas and oil in North America has created considerable interest in shale. The analysis of genetic types, the sedimentary environment, and the mudstone development mechanism within sequences is critical for evaluating shale gas and oil exploration prospects, exploration favorable zones, and resource potential. This study focused on the shale of Shahejie Formation in Dongying Depression of Bohai Bay Basin. Shale lithofacies division, geochemical analysis, and well-log analysis were performed for a sedimentary environment and its related elemental response characteristics’ identification. Based on the results, we concluded that the sedimentary environment of the lake basin evolved from the saltwater lake to the ambiguous lake and then the open lake to the delta. In response, we observed gradually decreasing Sr/Ba and Ca/Mg ratios and increasing Rb/Ca and Fe/Mn ratios during the whole process during the reduction of the salinity and the decrease in PH value and sediments’ transport distance. The relationship between ratio elements and high-frequency sequences was initially established within the shale strata. Our results show that ratios of Sr/Ba and Ca/Mg ratios near the sequence boundary are relatively low, and ratios of Fe/Mn and Rb/Ca are relatively high, while ratios of Sr/Ba and Ca/Mg near the flooding surface are relatively high, and ratios of Fe/Mn and Rb/Ca are relatively low. Those features can be used as a marker for high-frequency sequence division of shale strata. Our results provided a new theoretical basis and technical method for shale gas and oil exploration and development.

Pore-throat radius is one of the key parameters that characterizes the microscopic pore structure of rock, which has an important impact on oil-gas seepage and the prediction of remaining oil’s microscopic distribution. Currently, the quantitative characterization of a pore-throat radius mainly relies on rock-core experiments, then uses capillary pressure functions, e.g., the J-function, to predict the pore-throat radius of rocks which have not undergone core experiments. However, the prediction accuracy of the J-function struggles to meet the requirements of oil field development during a high water-cut stage. To solve this issue, in this study, based on core experimental data, we established a deep neural network (DNN) model to predict the maximum pore-throat radius R_max, median pore-throat radius R₅₀, and minimum flow pore-throat radius R_min of rocks for the first time. To improve the prediction accuracy of the pore-throat radius, the key components of the DNN are preferably selected and the hyperparameters are adjusted, respectively. To illustrate the effectiveness of the DNN model, core samples from Q Oilfield were selected as the case study. The results show that the evaluation metrics of the DNN notably outperform when compared to other mature machine learning methods and conventional J-function method; the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are decreased by 14–57.8%, 32.4–64.3% and 13.5–48.9%, respectively, and the predicted values are closer to the true values of the pore-throat radius. This method provides a new perspective on predicting the pore-throat radius of rocks, and it is of great significance for predicting the dominant waterflow pathway and in-depth profile control optimization.