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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. T2,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. [ABSTRACT FROM AUTHOR]

Establishing a suitable classification and evaluation scheme is crucial for sweet spot prediction and efficient development of shale oil in the Chang 71-2 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 (<0.03 μm). Based on the development degree of various pore throats, the reservoir is divided into four types: type I (Φ ≥ 10%, K > 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. [ABSTRACT FROM AUTHOR]

The occurrence and enrichment of shale plays are highly controlled by pore characteristics of the formation. In this study, an immature sample rich in kerogen type III from the Damoguaihe formation, Hailar Basin in China is subjected to hydrous and anhydrous pyrolysis (HP and AHP) across an extensive temperature range (300–450 °C). Next, low-pressure N 2 adsorption analysis was conducted on the pyrolyzate of each maturity stage to study the pore structures' evolution during thermal maturity simulation. Moreover, deconvolution and fractal dimension analyses were implemented to study pore families and the complexity of pores within the shale samples. Finally, radial basis function (RBF) neural networks optimized by four evolutionary approaches were applied for modeling N 2 adsorption data obtained from the HP and AHP samples. According to the results, the original shale sample and all pyrolyzates obtained with HP and AHP scenarios exhibited type IV isotherm with H3 hysteresis loops. As a whole, BET surface area, micro-, meso- and total pore volume of HP pyrolyzates were higher than AHP ones. The unheated shale sample had seven families containing three mesopore and four macropore groups. Although all pyrolyzates obtained from the pyrolysis had families with similar means, their pore volumes were entirely different, which proves that the pore structure of samples undergoes changes during thermal maturation and the presence of water can also enforce these changes. Both fractal dimensions showed a direct relationship with BET surface area and a negative correlation with the average pore diameter of shale samples. The RBF model optimized by differential evolution (DE) delivered a mean absolute percent relative error (MAPRE) value of 4.84% and determination coefficient (R2) of 0.9946 for the total data set, which outperforms other RBF models in predicting N 2 adsorption/desorption tests of pyrolyzates. The outcome of sensitivity analysis suggested that the N 2 adsorption/desorption behavior of the pyrolyzates was mostly affected by relative pressure and the pyrolysis type (HP or AHP). Ultimately, the results clearly revealed that the effect of water on the pores' alteration of shales with type III kerogen is greater than the effect of temperature or thermal maturity itself. • An immature kerogen type III sample from Damoguaihe formation is subjected to HP and AHP tests. • Original and all artificially matured samples exhibited type IV isotherm with H3 hysteresis loops. • BET surface area, micro-, meso- and total PV of HP samples were higher than AHP ones. • The RBF AI model optimized by DE outperformed other RBF models in predicting N 2 adsorption/desorption. • N 2 adsorption/desorption is affected by relative pressure and pyrolysis pathway, the most, among all parameters. [ABSTRACT FROM AUTHOR]

Understanding fluid mobility during spontaneous imbibition (SI) and flooding is important for the enhancement of oil recovery. Many researches on SI, water flooding (WF), and N 2 flooding (NF) have been published, but an in-depth understanding of the oil mobility at the pore scale is still inadequate in low-permeability reservoirs. To solve this problem, SI, WF, and NF experiments were performed on the low-permeability conglomerates of the Baikouquan Formation and the Urho Formation in the Mahu Sag. Moreover, online NMR was introduced to monitor the oil volume in pores with different sizes in real-time. Considering the wettability, mineral compositions, and pore structure, we established a schematic diagram to reveal the fluid mobility mechanism. The results show that the micropores in the low-permeability conglomerates are more hydrophilic, whereas the other pores are more lipophilic. During SI, the movable oil is mainly distributed in the micropores in the form of clay-bound oil (CBO), which is discharged from the rock through the meso/macropores. High hydrophilicity contributes to a high imbibition rate and oil recovery from SI, while the pore structure only affects the imbibition rate. During WF, the movable oil is contributed by CBO in the micropores and free oil (FO) in the pores controlled by throats >0.3 μm. During NF, the movable oil is mainly distributed in the pores controlled by > 0.1 μm throats, existing as FO and part capillary-bound oil (CAO). The results demonstrate that NF is effective to enhance the oil recovery of tight reservoirs, while WF is only effective for tight reservoirs with better pore structure (high V r > 0.3 μm). • A series of simulation experiments were performed to analyze the relationship between the recovered oil and the pore sizes and fluid types combining online NMR. • A schematic diagram were established to reveal the fluid transport mechanism during spontaneous imbibition, water flooding and nitrogen flooding. • The controlling factors of the recovery factor were analyzed with respect to the wettability, pore structure and mineral compositions of low-permeability conglomerates. [ABSTRACT FROM AUTHOR]

Evaluating pore structure of unconventional shale reservoirs enables us to determine their productivity, allowing for better operational decisions. Despite extensive studies in this field, considering the complexity of shale plays, pore structure analysis of such formations still requires novelties and further research. In this study, 10 samples from the Qingshankou Formation (from 5 wells) were analyzed with X-ray diffraction (XRD), programmed pyrolysis, N 2 adsorption, and mercury intrusion capillary pressure (MICP). In the next step, several modern intelligent smart models including multilayer perceptron (MLP), radial basis function (RBF), generalized regression neural network (GRNN), cascaded forward neural network (CFNN), and least squares support vector machine (LSSVM), that were optimized by levenberg-marquardt (LM), Bayesian regularization (BR), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and differential evolution (DE) algorithms were employed, to estimate the volumes of N 2 adsorbed and desorbed based on the mineralogy and geochemical properties of the samples. Results show that samples are mainly composed of clay (up to 42.3 wt%) and quartz (up to 34.6 wt%), low in total organic carbon (TOC) (up to 2.89%) and in the oil generation window. Complexity of smaller pores was found higher compared to medium and larger ones. In addition, deconvolution of N 2 adsorption pore size distribution (PSD) curve revealed that samples are composed of up to three families in the range of macropore size and different families in mesopore size. We found that LSSVM with applicability to the entire input dataset, outperformed all other models in predicting the amount of nitrogen adsorption and desorption with an average absolute percent relative error (AAPRE) value of 1.94%. Ultimately, clay minerals and potash feldspar had the greatest effect on increasing and decreasing the amount of nitrogen adsorbed and desorbed, respectively. The Leverage technique's findings demonstrate that more than 97% of total data points in the LSSVM model are in the valid domain. This study proves that smart methods if used properly, would enable us to study a large group of samples independent from exhaustive, time consuming and expensive experimental methods. [ABSTRACT FROM AUTHOR]

Pore structure plays an important role in controlling the oil migration and accumulation mechanism of subaqueous volcanic rocks (SVRs). The Santanghu Basin in western China is a unique region for providing insight into the pore structure characteristics of SVRs; however, little attention has been given to them. Thin-section analysis, scanning electron microscopy, low-temperature nitrogen adsorption (LTNA), pressure-controlled porosimetry, and nuclear magnetic resonance (NMR) experiments are performed on SVRs in the Santanghu Basin to reveal their pore structure characteristics and pore development patterns. The results show that the SVRs are dominated by ink bottle-shaped pores and have the characteristics of rather low matrix permeabilities (the Swanson parameters are mainly <4.0%/MPa), fine pore throats (r apex mainly <0.5 μm) and high capillary pressures (P 50 > 14.0 MPa). An effective method, namely, integrating NMR and LTNA experiments, is proposed to estimate the full-range pore size distribution (PSD) of the SVRs. In the full-range PSD, three peaks are identified at pore sizes of <1 μm, 1–10 μm and >10 μm, mainly corresponding to intercrystalline pores (IPs), dissolved pores (DPs), and residual vesicles (RVs), respectively. The storage space of SVRs is dominated by IPs, while the contents of DPs and RVs are the key to SVRs becoming effective reservoirs. The content of RVs is mainly controlled by the depth of the water body during deposition and the distance from the volcanic strata boundary. The higher the content of K and Na in the original composition and the closer the distance to the source rock is, the higher the proportion of DPs. In accordance with the original composition and the depth of the water body during deposition, the SVRs are divided into four types. Moreover, we propose the pore development patterns of the four types of volcanic rocks. RVs mainly exist in type I and type II SVRs, while DPs are important storage spaces in type I and type III SVRs. Finally, type IV SVRs only develop IPs and a few nanoscale DPs. • This paper characterized the pore development characteristics of SVRs in detail. • Pore development in SVRs depends on the depth of the water body during deposition and the original composition. • Division of four types of SVRs based on the formation environment and original composition. • Various types of SVRs show distinct differences in pore development pattern. [ABSTRACT FROM AUTHOR]