Search Results (6,386)

Unconventional oil and gas reservoirs naturally have low porosity and low permeability, which necessitate reservoir stimulation during production to achieve commercial exploitation. Therefore, to improve reservoir stimulation effectiveness, this study established a thermal–hydraulic–mechanical coupled numerical model suitable for hydraulic fracturing experiment scales based on rock mechanics, elasticity mechanics, damage mechanics, and flow mechanics theories, combined with maximum principal stress and Mohr–Coulomb damage criteria. The model was numerically solved within a finite element framework and used to simulate the reservoir hydraulic fracturing process. The results indicate that the propagation behavior of hydraulic fractures is controlled by reservoir rock mechanical properties, geostresses, reservoir temperatures, fracturing fluid viscosities, and injection rates. Among these, the increase in principal stress difference, reservoir temperature, fracturing fluid viscosity and injection rate promotes the propagation of hydraulic fractures along the direction of the maximum horizontal principal stress, whereas an increase in the rock’s elastic modulus reduces the propagation length of the hydraulic fractures. During fracturing, the fracturing fluid fractures the reservoir rock, significantly improving its porosity and permeability. This not only enhances the mobilization of unconventional oil and gas resources but also provides effective flow pathways for their migration, thereby ensuring the commercial viability of unconventional oil and gas resource extraction. Additionally, selecting a fracturing process that matches the geological characteristics of the study area during fracturing design is a prerequisite for improving the reservoir stimulation effect. The results of this study provide a reference for fracturing design and optimization. Full article

Supercritical carbon dioxide (SC-CO₂) fracturing has been recognized as an effective technology for developing unconventional oil and gas resources. The extent to which natural fractures can be activated is a critical factor controlling overall reservoir stimulation. A thorough understanding of the activation and propagation mechanisms of natural fractures during SC-CO₂ fracturing is therefore essential for elucidating fracture network evolution and optimizing stimulation strategies. In this work, a multiphysics-coupled numerical model for intersecting fracture propagation was developed using the phase-field method, incorporating formation pressure evolution and variations in CO₂ properties (density and viscosity). Based on this model, the influences of fracture approach angle, horizontal stress difference, injection temperature, and injection rate on fracture propagation patterns and pressure diffusion were systematically investigated. To quantitatively describe the stimulated reservoir volume, a “diffuse interface” was defined to represent the region affected by SC-CO₂ injection. The simulation results demonstrate that larger approach angles enhance the activation of natural fractures, with a 60° angle producing the maximum diffuse interface ratio of 72.5%. Although higher horizontal stress differences tend to suppress fracture activation, they promote plastic deformation at fracture tips, enlarging the diffuse interface to 86.72% at 15 MPa. Elevated injection temperatures further facilitate fracture propagation; as the temperature rises from 313.15 K to 403.15 K, the lateral fracture length increases from 2.8 cm to 3.7 cm, accompanied by continuous expansion of the diffuse interface. Under constant injection rate, a greater injection volume also enhances natural fracture activation and drives fractures to extend farther. These results provide theoretical insights for the design and optimization of SC-CO₂ fracturing in naturally fractured reservoirs. Full article

Reinjection represents a fundamental strategy for sustainable geothermal reservoir development. During reinjection, reservoirs are subjected to pronounced physicochemical disequilibrium, under which complex water–rock interactions render long–term behavior difficult to predict. This review synthesizes mineral reactions and reservoir dynamic responses from a geochemical perspective. The interplay between reaction kinetics and fluid transport is examined using the Damköhler number, elucidating the spatiotemporal evolution of reactive transport. The dissolution–precipitation behaviors of silicate, carbonate, and sulfate minerals are evaluated, highlighting their distinct roles in governing long–term structural reorganization, short–term permeability variability, and rapid clogging. The influence of mineral reactions on pore structure evolution and the development of nonlinear porosity–permeability relationships is examined, alongside commonly used constitutive models and their inherent limitations. Multiscale characterization approaches for porosity–permeability evolution and the distinct responses of different reservoir types are reviewed. The chemo–mechanical coupling induced by water–rock interactions and its implications for reservoir stability are addressed. This work establishes a unified conceptual framework linking mineral reactions, fluid transport, and reservoir evolution, providing a basis for optimizing reinjection strategies and improving long–term geothermal system performance. Full article

Mycogenic nanomaterials, nanoparticles (NPs) biosynthesized through fungal enzymatic and metabolic activity, have emerged as a compelling alternative to chemically synthesized nanomaterials, offering fundamental biocompatibility, green production conditions, and biologically functional surface coatings. Fungi, acting as natural “nanofactories,” harness reductases, oxidoreductases, secreted proteins, and secondary metabolites to reduce metal ions into stable NPs under ambient conditions, simultaneously capping the particles with biomolecules that enhance colloidal stability, biocompatibility, and secondary biological activity. Unlike previous reviews that have addressed either biosynthesis mechanisms or applications in isolation, this review uniquely adopts a structured “Promise vs. Barrier” framework across six interconnected thematic pillars, offering the first comprehensive critical synthesis that simultaneously maps mechanistic frontiers, biodiversity gaps, and translational barriers within mycogenic nanotechnology. The present review critically examines both the extraordinary promise and the persistent barriers facing mycogenic nanotechnology across biosynthetic mechanisms, fungal biodiversity, nanomaterial portfolio expansion, biomedical applications, environmental and agricultural utility, and industrial scalability. We highlight how emerging multiomics approaches, integrating transcriptomics, proteomics, and metabolomics, are beginning to decode the molecular blueprints of fungal NP synthesis, while acknowledging that mechanistic knowledge gaps, limited genetic toolkits for non-model fungi, and the absence of standardized protocols continue to impede progress. The fungal kingdom represents a vast, underexplored reservoir of nanofactory potential, with fewer than 1% of known species evaluated to date; strategic bioprospecting using genome mining and machine learning is beginning to unlock this diversity. Mycogenic NPs demonstrate broad-spectrum antimicrobial activity against multidrug-resistant pathogens, selective anticancer activity, biosensing capacity, and applications in wound healing, sustainable agriculture, environmental remediation, and smart food packaging. However, critical deficits persist in clinical validation, long-term toxicity data, manufacturing reproducibility, and regulatory clarity. The review concludes with a tiered roadmap, spanning immediate mechanistic priorities through to long-term synthetic biology and AI-integrated commercialization, and calls for coordinated international action on standardization, reference material development, and harmonized regulatory frameworks to bridge the gap between laboratory promise and real-world application. Full article

Supercritical carbon dioxide (SC-CO₂) fracturing has emerged as an environmentally friendly alternative to conventional water-based hydraulic fracturing; however, its inherently low viscosity restricts proppant-carrying efficiency and reduces fracture conductivity. To address this limitation, this study systematically investigates the rheological behavior and sand-carrying mechanisms of CO₂ dry fracturing fluid under various thermodynamic and compositional conditions. Rheological measurements were conducted to evaluate the effects of thickener concentration, temperature, and pressure on viscosity, while visualized experiments were performed to examine the influence of injection rate, sand ratio, thickener concentration, and temperature on proppant migration and deposition. A numerical model developed in Fluent was further employed to simulate the temporal evolution of proppant transport within the fracture. The results show that higher thickener concentrations and injection rates significantly enhance proppant transport distance and uniformity, whereas elevated temperature and sand ratio promote localized settling. The simulation results agree well with the experimental observations, validating the model’s reliability. This study elucidates the coupled effects of rheology and operating parameters on CO₂ dry fracturing behavior and provides theoretical and experimental guidance for optimizing CO₂-based fracturing fluids in low-permeability reservoirs. Full article

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

Offshore oil and gas exploitation is one of the riskiest businesses to invest in and is dominated by various uncertainties: high deepwater pressure, low temperatures, remote operation, long-distance tiebacks and transportation, as well as environmental factors such as wind, waves and ocean currents. Serving as a profitability threshold, the minimum economic field size is defined as the economic recoverable reserve level that an oilfield must exceed to achieve economic returns. This paper develops an approach for determining the minimum economic field size of offshore oil and gas reservoirs. It categorizes the capital expenditure into four major components: drilling and completion costs, platform costs, pipeline costs, and subsea production system costs. The regression models of drilling costs and subsea production costs are developed respectively, with water depth and recoverable reserves as key influencing factors. The pipeline costs are estimated using the unit pipeline cost per mile and pipeline length. A profit model for the offshore field is established under the constraints of the contract, which allocates the oilfield’s production profits between the contractor and the government according to the contractual fiscal terms. Finally, taking the Lucius oilfield in the Gulf of Mexico as a case study, the paper simulates its investment, operating costs, and oilfield revenues. The minimum economic field size is calculated, accompanied by the derivation of the sensitivity boundaries for the primary parameters. Full article

Geothermal energy extraction, hydrocarbon recovery, and CO₂ geological sequestration are frequently hindered by interfacial barriers and slow mass transfer. While high-power ultrasound offers a sustainable, purely physical method for reservoir stimulation, its field effectiveness remains debated because traditional macroscopic experiments fail to isolate mechanisms like acoustic streaming and cavitation. This review systematically examines acoustic mechanisms in porous media via microfluidic visualization, focusing on pore-scale fluid dynamics during enhanced oil recovery, hydrate dissociation, and CO₂ sequestration. Microscopic evidence reveals that fluid transport mechanisms depend heavily on pore geometry and local acoustic intensity. In wider channels, nonlinear acoustic flow provides sustained, directed convection to strip away concentration boundary layers; in narrow throats, microjets and pulsed stresses generated by transient cavitation are responsible for physically breaking capillary barriers. The spatiotemporal synergy of these mechanisms is critical for multiphase fluid transport in tight porous networks. Pore geometry serves not only as the application context but also as a core physical variable. To translate microfluidic results into reservoir-scale applications, future research must address two-dimensional simplifications, thermodynamic discrepancies under high-temperature and high-pressure conditions, and bubble cluster interactions, alongside the development of adaptive frequency-modulated control and multiscale computational models. Full article

Dominant flow channels formed during the late stages of waterflooding can severely reduce sweep efficiency and intensify ineffective interwell circulation. Conventional identification approaches, including tracer testing, well testing, and numerical simulation, often suffer from high operational cost, long execution time, or limited adaptability to heterogeneous interwell connectivity. Although ant colony optimization (ACO) is suitable for path-search problems in reservoir networks, its performance depends strongly on hyperparameter settings, and sample-by-sample parameter tuning introduces substantial online computational overhead. This study proposes a structure-informed GIN–ACO framework for adaptive dominant flow channel identification in connection-element reservoir graphs. A physics-constrained benchmark model is first established using Darcy’s law and the connection element method to provide reference flow paths. A geometry-based surrogate model is then developed to approximate flow splitting coefficients efficiently while preserving the main physical trends. Based on graph topology and geometric descriptors, a graph isomorphism network is trained to predict task-specific ACO parameters, replacing iterative online search with direct parameter inference. Experiments on 1000 synthetic reservoir graphs show that the proposed method achieves a 100% success rate with an average online computation time of 143.5 ms, outperforming fixed-parameter ACO, PSO-ACO, and BO-ACO. On 20 semi-realistic SPE10 reservoir models, GIN–ACO achieves a success rate of 92 ± 1% with an average runtime of 160.3 ± 5 ms. Ablation studies further confirm that graph-structure learning, combined topology–geometry features, and GIN-based parameter prediction are essential for robust performance. The proposed framework provides a promising and computationally efficient route for structure-aware dominant channel identification in connection-element reservoir models. Full article

Seismic inversion is a key technique for quantitative characterization of subsurface elastic parameters and detailed reservoir description. However, due to the limited bandwidth of seismic signals and the strong heterogeneity of complex reservoirs, conventional inversion methods struggle to simultaneously achieve high vertical resolution and lateral continuity. To address these challenges, an intelligent elastic parameter inversion method based on kernel density estimation within a Bayesian framework is proposed. First, kernel density estimation is introduced to augment the training samples, thereby alleviating data scarcity. Second, a hybrid architecture integrating convolutional modules, Mamba, and cross-attention mechanisms is constructed to achieve collaborative modeling of local spatial features and long-range temporal dependencies. The cross-attention mechanism is further employed to adaptively weight and fuse multi-source features, thus enhancing the representation capability of the model. Subsequently, by designing a joint loss function, the strengths of deterministic inversion and data-driven approaches are effectively integrated, ensuring physical consistency while enhancing data adaptability, thereby improving the stability and accuracy of the inversion results. Furthermore, the neural network outputs are used as the initial model for Bayesian inversion to construct a probabilistic inversion framework for elastic parameter inversion. Finally, experimental results demonstrate that the proposed method improves the R² values of inversion results by more than 8.0% and 5.0% compared with conventional methods in thin interbedded models and real data experiments, respectively. Full article

Accurate precipitation forecasting is essential for water resource management, flood early-warning systems, and agriculture, but remains difficult because of the nonlinear and highly variable spatiotemporal nature of rainfall. This paper compares four deep learning architectures—a standalone LSTM, a standalone CNN, a hybrid CNN–LSTM, and a Transformer encoder—against three classical baselines (persistence, day-of-year climatology, and per-grid-point ARIMA) for daily precipitation forecasting over Washington State at lead times of one to four days. A 40-year ERA5 dataset (1985–2024) of near-surface air temperature, mean sea-level pressure, and total precipitation is split into training (1985–2012), validation (2013–2015), and test (2016–2024) periods, with the test years held out completely. Each (model, horizon) is trained with three random seeds and evaluated in physical units (mm/day). On the held-out test period, the hybrid CNN–LSTM achieves the lowest RMSE at every horizon $h\ge 2$, with $R^2=0.576\pm 0.007$ and RMSE $=15.08\pm 0.07$ mm/day at $h=4$. Diebold–Mariano tests, paired t-tests, and bootstrap 95% confidence intervals confirm that the CNN–LSTM advantage over the LSTM is statistically significant at horizons 2–4 (but not at $h=1$), while CNN–LSTM is significantly better than every classical baseline and the Transformer at every horizon. The headline result is reproduced under a rolling-origin temporal cross-validation across three non-overlapping splits ($R^2\in [0.576,0.590]$). Practically, the sub-millisecond inference cost of the CNN–LSTM makes it directly deployable in operational forecasting pipelines used for flood early-warning, irrigation scheduling, and reservoir management, where even modest improvements in 3–4-day-ahead RMSE translate into measurable risk reduction and improved decision lead time for water managers and emergency planners. Full article

Due to the strong heterogeneity of the reservoir, early gas breakthrough and low CO₂ displacement efficiency are common issues in the CO₂ flooding process of domestic gravel reservoirs. This study focuses on a gravel reservoir in Xinjiang, proposing a quantitative evaluation method that combines early gas breakthrough identification and the inversion of gas channel characteristic parameters. The aim is to provide theoretical support and technical guidance for gas breakthrough risk warning, injection-production system optimization, and control measures during the CO₂ flooding process. The research method includes the following several key steps: first, clarifying the criteria for determining the time of gas breakthrough and proposing a classification method for early gas breakthrough types based on CO₂ concentration levels; second, adopting a “matrix-dominant gas channel” dual-medium model, considering the geometric and physical characteristics of inter-well gas channels, and deriving a theoretical calculation formula with gas breakthrough time and CO₂ concentration in the produced gas as the target; third, using actual gas breakthrough time and CO₂ concentration as constraints, constructing a method to invert the characteristic parameters of gas channels, quantitatively representing key parameters such as gas channel thickness ratio, permeability variation, and equivalent permeability; finally, through the combined analysis of CO₂ concentration and gas channel characteristic parameters, establishing a method for identifying gas channel types suitable for domestic gravel reservoirs. The practical application results show that the test area has formed localized dominant gas channels, but the overall stage is still in the early phase of weak gas breakthrough. Most gas breakthrough phenomena are weak, with only a few well groups experiencing severe gas breakthrough issues. The gas channel thickness ratio is generally less than 0.05, and the permeability variation mainly ranges from 2 to 20. The gas channels are primarily of the fracture type, with some areas also containing ordinary fractures and main control fractures. The method proposed in this study, which combines early gas breakthrough identification with the inversion of gas channel characteristic parameters, not only provides a new approach to revealing the characteristics of gas breakthrough during CO₂ flooding but also offers solid theoretical and technical support for optimizing CO₂ flooding technology and controlling gas breakthrough risks. Full article

HIV-associated neurocognitive disorders (HAND) arise from HIV infection of the central nervous system, resulting in chronic neuroinflammation and progressive neuronal damage that impair cognitive, motor, and behavioral functions. Clinically, HAND encompasses a spectrum of neurological impairments ranging from asymptomatic neurocognitive impairment to severe HIV-associated dementia. Despite the widespread use of combination antiretroviral therapy (cART) and significant improvements in the life expectancy of people living with HIV, HAND remains prevalent and continues to pose a major clinical challenge. One of the primary limitations of cART is the limited penetration of many antiretroviral drugs across the blood–brain barrier (BBB), thereby allowing the persistence of viral reservoirs within the CNS and contributing to sustained neuroinflammation and neuronal damage. To address these challenges, novel nanotherapeutic strategies have been developed to enhance the delivery of antiretroviral agents to the brain. These approaches include targeted delivery systems and the co-delivery of therapeutics across the BBB through mechanisms such as receptor-mediated transcytosis and other transport pathways. In this review, we discuss the pathophysiological challenges associated with HAND and recent advances in nanotherapeutic approaches designed to improve treatment efficacy. We also discuss the current state of the art in vitro and in vivo models used to test the efficacy of these advanced therapeutics. Finally, we outline the remaining challenges and future prospects for the development of nanotherapeutics to improve the treatment of HAND. Full article

With the continuous development of complex marine hydrocarbon reservoirs, broadband seismic data have shown growing advantages in revealing abundant stratigraphic information. Affected by acquisition conditions and stratigraphic attenuation, the acquired seismic data commonly suffer from narrow bandwidth, and conventional broadband processing techniques are incapable of optimizing the overall frequency band. This study proposes a coordinated high- and low-frequency broadband compensation method based on adaptive weight fusion to effectively extend the frequency bandwidth of seismic data. Firstly, wavefield separation is used to suppress ghost reflections, compensate low-frequency effective signals, and restore the continuity of the low-frequency spectrum. Then, based on the spectrum extrapolation method of maximum entropy spectrum estimation, a spectrum prediction model is established to achieve the continuation of high-frequency effective signals. Finally, in combination with the signal-to-noise ratio of each frequency band, the adaptive weight fusion algorithm is applied for weighted summation. The acquired broadband seismic data feature a continuous spectrum and balanced energy, greatly improving seismic imaging quality. Comparative results obtained using conventional processing methods verify that the proposed method can significantly improve stratigraphic continuity and wave group characteristics. Full article

Accurate three-dimensional (3D) reconstruction of digital rocks from limited data remains a significant challenge in digital rock physics. While Multiple-Point Geostatistics (MPS) offers a powerful solution, its multi-scale performance, particularly regarding extrapolation from small training images to larger domains, lacks a comprehensive evaluation framework that connects structural fidelity to functional equivalence. This study proposes an integrative multi-dimensional quantitative evaluation system that incorporates macroscopic statistics, microscopic topology, complex morphology, and seepage properties. Utilizing an improved Single Normal Equation Simulation (SNESIM) algorithm and a 60 × 60 × 60 voxel sandstone Training Image, 3D models were reconstructed across five scales ranging from 40 × 40 × 40 to 120 × 120 × 120 voxels. To ensure statistical robustness and mitigate stochastic uncertainty, ten independent realizations were performed for each scale. Quantitative analysis reveals that while SNESIM maintains high accuracy in macroscopic parameters and second-order spatial statistics, it exhibits systematic deviations in microscopic topology and surface complexity. Specifically, as the scale expands, the coordination number decreases while intrinsic anisotropy is progressively lost, yet permeability does not drop proportionally. This paradox is attributed to structural homogenization driven by the loss of long-range directional correlations. These findings indicate that the algorithm tends toward structural homogenization during scale extrapolation, systematically weakening the directional transport properties of the original rock. This study provides a standardized benchmarking methodology that promotes the evolution from visual similarity toward functional equivalence, thereby enhancing the reliability of reservoir characterization and seepage prediction. Full article