Yunpeng Fan

Stable emulsified oil droplets are difficult to separate because of persistent interfacial films and colloidal stability. Garlic-extract-functionalized Fe₃O₄ magnetic colloids, operationally denoted Allicin@Fe₃O₄, were prepared by in situ coprecipitation as organic–inorganic building blocks for gel-like interfacial capture. The sample name identifies the allicin-containing garlic-extract route and does not imply that allicin was proven to be the predominant surface species. SEM, FTIR, and XRD supported deposition of an organic, sulfur/oxygen-containing surface layer and retention of crystalline Fe₃O₄, but these methods are not species-specific. The colloids removed more than 95% of emulsified oil within 20 min and reached an experimental equilibrium apparent uptake of approximately 380 mg·g⁻¹. Tests across pH 3–11, 0–50 g·L⁻¹ NaCl, 15–55 °C, and representative coexisting ions showed robust but condition-dependent removal. Removal remained above 90% after five reuse cycles and was approximately 84% after ten cycles. Calculations using allicin as a representative garlic organosulfur molecule suggest how polar sulfur/oxygen regions and allyl segments could favor oil–droplet anchoring and association; they do not establish the surface composition of the extract-derived coating. The capture behavior is consistent with transient, gel-like particle–droplet association coupled to magnetic recovery. These results connect bio-derived surface functionalization, gel-related colloidal structuring, and magnetic separation under the tested batch conditions.

Fluid type and content directly control fluid mobility in tight reservoirs. At present, there are two ways to classify fluid types. One is to classify fluids into movable fluids (MF), capillary-bound fluids (CAF), and clay-bound fluids (CLF). The other is to classify fluids into free fluids and adsorbed fluids. However, the intrinsic relationship between the two fluid classification schemes is still unclear. In order to investigate the pore structure and fluid type characteristics, a series of experiments were performed on the Chang 7 tight sandstone in the Longdong area. The full-scale pore size distribution (PSD) can be obtained by combining low-temperature nitrogen adsorption (LTNA) with nuclear magnetic resonance (NMR). The PSD of Chang 7 tight sandstones primarily ranges from 1 nm to 20 μm. Based on the fractal characteristics, pore system is divided into macropores (mainly >150.8 nm), and micropores (mainly <150.8 nm). MF, CLF, and CAF constitute 11.1–49.7% (avg. 34.6%), 17.3–40.1% (avg. 26.7%), and 32.9–48.8% (avg. 38.7%) of total fluid, respectively. Additionally, macropores are positively correlated with MF and negatively correlated with CAF and CLF, whereas micropores show the opposite trend. By integrating NMR data with theoretical modeling, a clear correspondence between the two fluid classification approaches was established: MF and CAF closely correspond to free fluids, while CLF is strongly associated with adsorbed fluids. Notably, in samples with low porosity and extremely fine pore throats, the combination of centrifugation and theoretical modeling may underestimate the actual free fluid content. MF shows a weak negative correlation with quartz and clay minerals; CAF and CLF are weakly positively correlated with quartz and clay but negatively correlated with feldspar. The occurrence patterns of different fluid types within various pore-throat structures were established, revealing the relationships among mineral composition, pore size, pore-throat structure, and fluid distribution. These findings provide valuable insights into the pore structure and fluid distribution of tight sandstone reservoirs, enhancing the understanding of fluid behavior in unconventional systems.

Accurately predicting permeability is important to elucidate the fluid mobility and development potential of tight reservoirs. However, for tight sandstones with the same porosity, permeability can change by nearly three orders of magnitude, which greatly increases the difficulty of permeability prediction. In this paper, we performed casting thin section, scanning electron microscopy and high-pressure mercury injection experiments to analyze the influence of pore structure parameters and fractal dimensions on the permeability of Chang 7 tight sandstones. Furthermore, the key parameters affecting the permeability were optimized, and a new permeability prediction model was established. The results show that the pore throat structure of Chang 7 tight sandstone exhibits three-stage fractal characteristics. Thus, the pore throat structure was divided into large pore throat, medium pore throat and small pore throat. The large pore throat reflects the microfracture system, whose fractal dimension was distributed above 2.99, indicating that the heterogeneity of the large pore throat was the strongest. The medium pore throat is dominated by the conventional pore throat system, and its fractal dimension ranged from 2.378 to 2.997. Small pore throats are mainly composed of the tree-shaped pore throat system, and its fractal dimension varied from 2.652 to 2.870. The medium pore throat volume and its fractal dimension were key factors affecting the permeability of Chang 7 tight sandstones. A new permeability prediction model was established based on the medium pore throat volume and its fractal dimension. Compared to other models, the prediction results of the new model are the best according to the analysis of root mean square value, average absolute percentage error and correlation coefficient. These results indicate that the permeability of tight sandstones can be accurately predicted using mesopore throat volume and fractal dimension.

Organic geochemistry experiments, X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), N₂ adsorption, CO₂ adsorption, and two-dimensional nuclear magnetic resonance (2D NMR) were performed on ten Chang 7 shale samples (Longdong area, Ordos Basin) to elucidate their pore structure and the characteristics of oil occurrence. Moreover, the factors influencing free oil were discussed, and an occurrence model was established. FE-SEM analysis reveals that the pore types include interparticle pores, intraplatelet pores within clay aggregates, rare fracture pores, and organic matter (OM) pores. The pores are predominantly slit-shaped. The development of micropores was mainly contributed to by OM. Quartz and clay minerals influence the development of macropores and mesopores, feldspar mainly controls macropore development, and pyrite most strongly affects micropore development. Micropores and mesopores constitute the main total pore specific surface area, while mesopores and macropores are the main contributors to the total pore volume. Pores > 2 nm are the main storage spaces for shale oil, and free oil mainly occurs in pores > 20 nm. Adsorbed oil and free oil were assessed by NMR T₁–T₂ mapping. The adsorbed oil signal intensities range from 7.5–23.4 a.u. per g of rock, and the free oil signal intensities range from 4.4–23.2 a.u. per g of rock. The free oil proportions are 15.9–70.6% (average of 44.2%). The free oil proportion is negatively correlated with the clay mineral content and total organic carbon (TOC) content but positively correlated with the saturated hydrocarbon content and volume of pores > 20 nm. The results of this study could help optimize favorable shale oil target areas.

The Baise Basin is a Paleogene pull-apart basin with numerous strike-slip faults which are not favorable for hydrocarbon preservation. The Nadu Formation, research object of this paper, is generally rich in oil and contains a large number of high-angle joint fissures. Analyzing the origin of residual oil in high-angle joint fissures can reveal the hydrocarbon migration and accumulation characteristics of the pull-apart-type basins. Molecular geochemical composition characteristics of crude oil and oil source of the Nadu Formation were discussed based on the saturated hydrocarbon biomarker compound and stable carbon isotope distribution of n-alkanes. The studied samples were selected from four members (E₂n₁, E₂n₂, E₂n_3up, and E₂n_3low) of the Nadu Formation. The results suggested that the average oil content of E₂n₁ fissures is 0.32 mg/cm², and the oil distribution is not uniform. The distribution of oil on the fissures of E₂n₂ and E₂n₃ is uniform and complete, and the oil content reaches 0.53 mg/cm². The oil in the joint fissures of the Nadu Formation is heavy, as the light hydrocarbon is seriously lost during migration. Thus, the oil in the joint fissures is residue after crude oil loses light components during migration. By comparing the molecular biomarker characteristics and stable carbon isotopic compositions, crude oil of the Nadu Formation can be classified into three categories: E₂n₁, E₂n₂ + E₂n_3up, and E₂n_3low. The E₂n₁ oils have the lowest maturity and are sourced from the E₂n₁ source rocks. Moreover, the maturity of E₂n₂ and E₂n₃ samples are relatively high. Biomarker and carbon isotope characteristics of the E₂n₂ and E₂n_3up oils are similar, indicating that they are derived from the E₂n₂ + E₂n_3up source rocks. The E₂n_3low oils are the mixture of the crude oil generated from the E₂n_3up source rocks and the E₂n_3low source rocks. Results presented show that the residual oil of high-angle joint fissures in the Nadu Formation is contributed by adjacent source rocks. The crude oil discharged from the Nadu Formation can only migrate upward along high-angle joints in a short distance, and the migration distance is usually less than 5 m. In conclusion, although the Nadu Formation has developed a large number of high-angle joint fissures, crude oil in the Nadu Formation has not vertically migrated for long distance along the joint fissures. The well-preserved fractures as important shale oil storage spaces indicate that the Nadu Formation has good shale oil exploration potential. The results may provide insights into the origins of hydrocarbons in the Nadu Formation from the Baise Basin and enhanced knowledge for optimizing future exploration and production.

The pore structure and its complexity play a critical role in fluid migration and recovery efficiency. Multiple pore types, broad pore size distribution (PSD), and extremely irregular pore morphology hinder the comprehensive investigation of pore systems and their complexity in low-permeability conglomerates. In this study, the multi-scale pore system and fractal characteristics of the Permian Lower Wuerhe Formation and Triassic Baikouquan Formation conglomerates from the Junggar Basin were investigated, combining physical property analysis, casting thin sections, scanning electron microscopy, and Nuclear magnetic resonance (NMR). The results show that the pore system of conglomerates consists of residual intergranular pores (RIPs), dissolution pores (DPs), clay-related pores (CRPs), and microfractures. Three types of PSD were identified according to the shape of the T₂ spectrum. Based on the fractal characteristics derived from NMR data, pore systems in conglomerates were divided into macropores (mainly RIPs and DPs), mesopores (mainly CRPs), and micropores (reflect adsorption spaces). The fractal dimension of macropores (D3) increases with the increase of clay mineral content and the decrease of contents of quartz and feldspar. Moreover, the volume of macropores decreases with the increase of clay mineral content and the decrease of contents of quartz and feldspar. In addition, the fractal dimensions and volumes of mesopores and micropores have no obvious relationship with mineral composition. D3 and macropore volume control the physical properties and fluid mobility of conglomerates. T_2,gm shows a strong negative correlation with D3 and macropore volume. Meanwhile, the high value of D3 would reduce the volume of macropores. These results demonstrate that D3 is a good indicator to reveal the quality of pore structure in low-permeability conglomerates.

Identifying organic matter in laminae is fundamental to petroleum geology; however, many factors restrict manual quantification. Therefore, computer recognition is an appropriate method for accurately identifying microscopic components. In this study, we used support vector machine (SVM) to classify the preprocessed photomicrographs into seven categories: pyrite, amorphous organic matter, mineral matter, alginite, sporinite, vitrinite, and inertinite. Then, we performed a statistical analysis of the classification results and highlighted spatial aggregation of some categories using the kernel density estimation method. The results showed that the SVM can satisfactorily identify the macerals and minerals of the laminae, and its overall accuracy, kappa, precision, recall, and F1 are 82.86%, 0.80, 85.15%, 82.86%, and 82.75%, respectively. Statistical analyses revealed that pyrite was abundantly distributed in bright laminae; vitrinite and sporinite were abundantly distributed in dark laminae; and alginite and inertinite were equally distributed. Finally, the kernel density maps showed that all classification results, except inertinite, were characterized by aggregated distributions: pyrite with the distribution of multi-core centers, alginite, and sporinite with dotted distribution, and vitrinite with stripe distribution, respectively. This study may provide a new method to quantify the organic matter in laminae.

Establishing a suitable classification and evaluation scheme is crucial for sweet spot prediction and efficient development of shale oil in the Chang 7_1-2 sub-member of the Longdong area. In this paper, a series of experiments, such as casting thin sections (CTS), scanning electron microscopy (SEM), low-temperature nitrogen adsorption (LTNA), high-pressure mercury intrusion porosimetry (HMIP), and nuclear magnetic resonance (NMR), were integrated to classify the pore throats and shale oil reservoirs in the study area. Moreover, the pore structure characteristics of different types of reservoirs and their contributions to productivity were revealed. The results show that the pore-throat system can be divided into four parts: large pore throats (>0.2 μm), medium pore throats (0.08~0.2 μm), small pore throats (0.03~0.08 μm), and micropore throats ( 0.1 mD), type II (Φ ≥ 8%, 0.05 mD < K < 0.1 mD), type III (Φ ≥ 5%, 0.02 mD < K < 0.05 mD) and type IV (Φ < 5% or K < 0.02 mD). From type I to IV reservoirs, the proportion of dissolved pores and intergranular pores gradually decreases, and the proportion of intercrystalline pores increases. The proportion of large pore throats gradually decreases, and the proportions of medium pore throats and small pore throats increase initially and then decrease, while the proportion of micropore throats increases successively. The NMR pore size distribution changes from the right peak to the left peak. The developed section of the type I reservoir corresponds to the oil layer, and the developed section of the type I and II reservoirs corresponds to the poor oil layer. In contrast, the developed section of the type III and IV reservoirs corresponds to the dry layer. The daily production from single wells is primarily attributable to type I and II reservoirs.