Accurate prediction of the imbibition of fluid in pores would help to properly estimate the oil/gas recovery. This study is focused on imbibition estimation, considering the influences of various rock matrix properties. We summarized the mathematical models and the impact of parameters that strongly influence the imbibition. Also, it includes imbibition mechanisms that incorporate intrinsic petrophysical parameters. This review highlights the importance of considering different methods for analyzing the effect of imbibition on hydrocarbon production, thereby providing valuable insights into reservoir engineering and petrophysics. Failing to account for the pore interconnectivity in the model can result in significant discrepancies between predictions and historical data. The findings show that a higher volume of oil pores in a formation minimizes the capillary pressure. Integrating various physical and chemical mechanisms into a single numerical model allows for a comprehensive understanding of spontaneous imbibition in clay-rich shale. By addressing complex pore structures and interactions, presented models provide valuable insights for improving hydrocarbon recovery in unconventional reservoirs.

A significant challenge in advancing nanoparticle-based enhanced oil recovery (EOR) is the gap between synthesis and application. Many reviews catalog surface modification techniques but fail to explain why specific molecular features lead to success in EOR mechanisms. Our work addresses this gap by providing a mechanism-centric framework focused on structure-to-function relationships. This approach is vital because while nano-EOR promises to overcome the drawbacks of traditional recovery, the base nanoparticles themselves are often ineffective. Their low stability and poor interfacial performance in challenging reservoir environments necessitate surface functionalization. Instead of focusing on modification methods, we analyze how the deliberate addition of chemical groups like sulfonates, carboxylates, hydroxyls, amines, and alkyl chains directly impacts core EOR functions, including reducing interfacial tension (IFT), altering rock wettability, stabilizing emulsions and foams, and enhancing viscoelasticity. By elucidating the underlying molecular interactions (electrostatic, hydrogen bonding, hydrophobic), we build a clear pathway from chemical design to functional outcome. This review extends to advanced applications, including trigger-responsive smart nanoparticles and asphaltene inhibition. Ultimately, we provide a comparative analysis and a selection guide to empower researchers to rationally design nanoparticles for specific reservoir challenges, thereby accelerating the field's transition from empirical testing to predictive engineering.

Understanding the controls of depositional and diagenetic processes on pore system evolution is essential for predicting fluid flow behavior in sedimentary reservoirs. The sandstones of the Mangahewa Formation were deposited during a marine transgressive phase in the Taranaki Basin, New Zealand, during the Late Eocene. Despite their potential as hydrocarbon reservoirs, the pore system evolution, as well as the extent of reservoir heterogeneity within Mangahewa transgressive facies, remains poorly constrained. This study applies an integrated sedimentological, petrographic, and petrophysical approach to investigate these processes. The transgressive sandstones comprise stratified lithofacies that grade upward into intensely bioturbated sandstones interbedded with siltstones and mudstones. The sandstones are arkosic to subarkosic arenites affected by compaction, feldspar dissolution, and selective carbonate and clay cementation. Reservoir heterogeneity is controlled by the combined influence of depositional facies, sandstone composition, and diagenetic modification, which govern pore system architecture and connectivity. Petrophysical analysis reveals a wide range of porosity (1%–24.9%) and permeability (0.01–10,000 mD), indicating pronounced heterogeneity. Statistical heterogeneity indicators and hydraulic flow unit (HFU) classification identify six HFUs with contrasting porosity–permeability relationships and flow capacities. Stratified sandstones form the main flow conduits, whereas bioturbated and fine-grained transgressive deposits act as flow baffles and barriers due to restricted pore-throat connectivity. These results highlight the critical role of depositional architecture and diagenetic evolution in controlling reservoir quality and fluid flow behavior in transgressive sandstone systems, thereby improving the prediction of high-quality reservoir intervals in the Upper Eocene Mangahewa Formation and analogous settings.

One of the fundamental challenges in drilling systems is the phenomenon of torsional vibrations that can cause system destruction. This paper presents a new control approach to reject stick-slip vibrations in a drill-string. Firstly, a two-degree-of-freedom plant model with friction that represents the interaction between the rock formation and the drill bit is discussed. Secondly, a higher-order sliding mode control (HOSMC) is proposed in conjunction with a cascade structure to reject the vibrations. Thirdly, given the limitation of measuring only the surface top drive velocity, a state observer is designed to estimate system states, including bit-rock interaction torque. Realistic numerical simulations demonstrate the effectiveness of the proposed solution showcasing negligible residual oscillations on the order of 10⁻³ and a better torque estimation, yet still to be improved, further demonstrating the effectiveness of the control strategy, highlighting its robustness, high precision, predefined convergence time, and ease of implementation.

The shale oil in the Fengcheng Formation of the Mahu Sag has become an extremely critical target for large-scale reserve growth in the Junggar Basin. Characterized by great burial depth, complex geology, and pronounced heterogeneity, the effective and economical development of shale oil “sweet spots” remains a key challenge. To clarify the characteristics and controlling factors of this heterogeneity, an integrated analysis combining geological context, well-log responses, and laboratory data was conducted, focusing on three aspects: litho-mineralogical composition, pore structure, and hydrocarbon content. This approach enabled a detailed characterization of the micro-scale heterogeneity of the Fengcheng Formation shale oil and identification of its primary controls. On this basis, a four-parameter coupled mobility index evaluation model was developed, and-by integrating formation test data and fracture intensity-a classification scheme for mobility ranges was established. The study reveals that the strong heterogeneity is intrinsically driven by the complex coupling among lithofacies assemblages, clay mineral distribution, pore structure, and oil saturation, while depositional environment and diagenesis act as the dominant macro-scale controlling factors. Field production data confirm that the proposed mobility evaluation method correlates well with actual performance: the mobility index exhibits a clear exponential relationship with average daily oil production ( R² = 0.90), and the mobility-based classification shows excellent agreement with well productivity, thereby providing a quantitative foundation for the effective development of ultra-deep shale oil sweet spots.

Liquid blockage around the wellbore region can significantly decrease the gas and condensate extraction volume from a condensate reservoir. In these kinds of reservoirs, the rock surfaces are commonly liquid-wet. Treatment to alter rock preference from liquid to gas is a versatile, permanent, and effective technique for recovery enhancement. A novel chemical solution containing fluorosurfactant and nanoparticles is suggested to modify the wettability of carbonate rock. The hydrophobicity of nano silica is achieved using (3-chloropropyl) trimethoxy silane. The chemical solution can provide a gas-wetting tendency in rock samples by inducing a combination of high surface roughness and low surface energy. Modification of wettability by this chemical was confirmed through static contact angle experiments. FTIR and FESEM were used to study the adsorption of the chemical agent on the rock surface, while EDX analysis and EDX map were employed to characterize the elemental composition of the rock surface and determine the distribution of elements on the coated surface before and after treatment. To determine the effective parameters of the chemical treatment process including fluorosurfactant and nanoparticle concentration, treatment time, and temperature, statistical analysis was conducted through analysis of variance (ANOVA) on the modified cubic model for two response variables, water contact angle (R1) and condensate contact angle (R2). The R² values of 0.9973 for the water contact angle and 0.9893 for the condensate contact angle represent a close agreement between measured and estimated values. Process parameter optimization was conducted using a desirability function to maximize the response variables associated with the liquid-repellent state. The optimal parameters for the process were identified as fluorosurfactant concentration of 4.96 wt%, SiO₂ nanoparticle concentration of 0.76 wt%, treating time of 2.49 days, and treating temperature of 23.24^◦C. To validate the optimization method, a confirmatory experiment was conducted. This experiment achieved the highest water and condensate contact angles of 148.10^◦ and 112.82^◦ under the optimized conditions. The EDX analysis and EDX mapping imply that a homogeneous and evenly distributed fluorine-based layer formed on the rock surface through the adsorption of the chemical agent onto the surface of the rock. This layer induces liquid repellency, making the rock surfaces resistant to both water and oil. Also, the FESEM images indicate a substantial level of roughness on the treated rock surface, attributed to the presence of nanoparticles. FTIR analysis confirmed the adsorption of chemical agents onto the surface of the carbonate rock and presence of carbon and fluorine functional groups within the proposed chemical solution agents.

Carbon dioxide (CO₂) injection is a promising strategy for enhancing shale oil recovery while enabling geological carbon storage. In this study, non-equilibrium molecular dynamics (NEMD) simulations were employed to investigate CO₂-driven shale oil transport in kerogen nanopores with complex surface characteristics. The distribution and migration behaviors of multiphase shale oil were analyzed, and the effects of pore size, temperature, and injection pressure on fluid flow and CO₂ sequestration were systematically evaluated. The results show that shale oil forms three distinct adsorption layers within kerogen nanopores, with the first layer exhibiting a peak density of approximately 1.13 g/cm³, which is about 1.85 times higher than that in the free region. The irregular kerogen surface increases the resistance to shale oil desorption, resulting in slower CO₂-driven displacement compared with smooth graphene pores. Increasing pore size, temperature, and injection pressure significantly promotes shale oil desorption and enhances CO₂ storage capacity. For example, when the pore size increases from 2 nm to 5 nm, the shale oil adsorption fraction decreases markedly while CO₂ storage increases significantly. Temperature also plays an important role in regulating adsorption and sequestration behavior. Overall, this study reveals the molecular-scale mechanisms governing CO₂-enhanced shale oil mobilization and carbon sequestration in kerogen nanopores, providing theoretical insights for optimizing CO₂ injection strategies in shale reservoirs.

To address the unclear understanding of the microscopic oil displacement mechanisms, patterns of oil mobilization in pore throats, and optimal displacement methods for gravity-assisted CO₂ miscible displacement in tight conglomerate reservoirs, this study utilized tight conglomerate cores from the Mahu Sag as the research subject. Experiments on gravity-assisted CO₂ miscible displacement were designed for cores with varying physical properties under different displacement conditions. NMR technology was employed for real-time monitoring. The study elucidated the microscopic oil mobilization patterns and displacement mechanisms of gravity-assisted CO₂ miscible displacement and established a recovery factor prediction model. The results demonstrate that gravity assistance enhances the recovery factor of cores with different physical properties to varying degrees, with the most pronounced effect observed in low-permeability cores. The presence of locked pores invalidates the method for determining sweep efficiency based on the lower limit of mobilization; instead, it can be quantitatively assessed through recovery factor and displacement efficiency. The incorporation of gravity assistance helps the miscible system overcome the constraints of some locked pores, thereby improving core recovery. Gravity assistance effectively enhances the degree of oil mobilization in pore throats of different scales and influences the frequency distribution of remaining oil, although it does not alter the distribution pattern. Remaining oil is still predominantly concentrated in pores with radii <1 μm. The mechanism by which gravity-assisted CO₂ miscible displacement improves recovery is not merely a result of simple pressure superposition but arises from complex multi-scale physical field coupling.

Predicting fluid behaviour accurately is crucial for optimising extraction methods in petroleum engineering due to the considerable hurdles posed by the non-Darcian flow of oil through porous surfaces. This work utilises numerical methodology employing COMSOL Multiphysics to model the movement of oil through different types of porous materials. The main aim is to investigate the impact of inlet velocities and material properties on flow behaviour. This research is unique because it thoroughly investigates flow dynamics, using the Forchheimer equation to represent nonlinear flow behaviour accurately. Traditional models generally overlook this aspect. The main objectives were to clarify the connections between flow velocity, pressure drop, and friction factors across various porous materials. The main result is that the friction factor for SiLi beads reached 1.89 when the velocities exceeded 1 m/s, which indicates a shift to turbulent flow. When the inlet velocity was set to 2 m/s, the glass balls exhibited a maximum axial velocity of around 2.0231 m/s, indicating that they offered the least resistance to the flow. The pressure differential over SiLi beads was measured at 7.11 × 10⁸ Pa when the flow velocity was 10 m/s, while gravels exhibited a pressure drop of 0.52 × 10⁸ Pa and glass balls had a pressure drop of 0.15 × 10⁸ Pa. These findings emphasise the crucial significance of considering non-Darcian effects when modelling oil flow through porous surfaces, as they have a major impact on extraction efficiency. Gaining insight into these processes can enhance oil extraction rates and more efficient resource management, rendering this research vital for progress in petroleum engineering and associated disciplines.

Permeability prediction in tight sandstone reservoirs is strongly influenced by diagenesis, multiscale pore structures and multiphase flow effects, resulting in pronounced spatial heterogeneity. Traditional empirical models based on homogenization assumptions suffer from inherent physical limitations, making their predictive accuracy insufficient for practical applications. Although existing data-driven deep learning methods offer advantages in nonlinear modeling, their performance is constrained by few-shot samples, imbalanced distributions, low-density information and the absence of physical constraints, which hinder the extraction of effective representations of inter-features and lead to biased learning and physical deviations. To address these challenges, this study proposes a framework including a data enhancement strategy based on high-order feature construction for quality improving and adversarial generation for quantity expanding, and a deep learning model termed DAFM-PI, which integrates bidirectional attention fusion with physical information constraints. Experimental results show that the test accuracy R² of DAFM-PI improves from 0.444 before enhancement to 0.969 after that, representing a 52.2% improvement. Furthermore, compared with conventional POR-LR, ResNet, and Transformer Encoder models, DAFM-PI achieves accuracy improvements of 61.9%, 18.1%, and 6.2%, demonstrating its effectiveness in enhancing both prediction accuracy and generalization for permeability prediction in tight sandstone reservoirs.

The size of sulfur particle aggregates directly influences the critical sulfur particle carrying velocity, flow resistance, sulfur blockage thickness, corrosion rate, and scaling rate in high-sulfur gas wells. This can significantly impact the normal production of the gas wells. Although considerable research has been conducted on the mechanisms and behaviors of particle aggregation, studies specifically addressing the diameter of sulfur particle aggregates remain limited. In this paper, utilizes numerical simulation and multiphase pipe flow experiments to investigate the gas-particle sulfur two-phase pipe flow. It focuses on the effects of gas velocity, particle flow velocity, initial particle diameter, and initial particle concentration on the size of sulfur particle aggregates. The results indicate that the maximum diameter of sulfur particle aggregates diminishes as gas velocity and particle flow velocity increase, whereas it escalates with larger initial particle diameters and higher particle concentrations. A predictive model for the diameter of sulfur particle aggregates has been established based on the principles of mechanical equilibrium, incorporating factors such as particle collision frequency, aggregation probability, and the influences of van der Waals forces and liquid bridge forces between particles. This model has been validated with experimental data (six sets), numerical simulations (twenty sets), and relevant literature (ten sources), demonstrating high concordance between predicted and measured diameters, with an average absolute error of 6.35%. By predicting sulfur particle aggregate diameters in the wellbore, the model aids in optimizing gas well production, enhancing flow efficiency, and reducing wellbore blockages, providing a strong theoretical basis for optimizing multiphase flow.

For the utilization of low-medium temperature geothermal water, the well injection capacity for tail water disposal is often reduced due to the migration of self-generated particles and the invasion of suspended particles, especially in weakly consolidated sandstone reservoirs. In this paper, a novel mechanism model of formation blockage and recovery by back-pumping during geothermal water reinjection was established based on the classification and bridging principle of movable particles in porous media. The model validity was confirmed by fitting the field tests in Xining Basin, China. Then a comprehensive sensitivity analysis of influencing factors was conducted based on the model using a numerical simulation method. The results show that in a weakly consolidated heterogeneous reservoir, a series of preferential seepage channels and low-permeability zones will be formed between reinjection and production wells due to the migration and precipitation of different types of movable particles. The blockage mainly occurs in the formation within 30 m away from the reinjection well. The self-generated and invasive coarse particles are the dominant factor to cause blockage. Back-pumping is an effective method to relieve the blockage in the formation near the well. The well reinjection capacity can be restored to larger than 70% of the initial. This established mechanism model can be an effective means to investigate the particle movement law in geothermal development.