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The existing non-linear flow model does not consider the influence of temperature change on the non-linear flow law. Therefore， based on the capillary flow model， the Hagen-Poiseuille equation， and the boundary layer theory， as well as the influence of the temperature on the thickness of the boundary layer and the yield stress of the fluid， a dynamic non-linear flow model in low-permeability porous media considering the influence of temperature was derived. Then， the fitting verification was carried out according to the experimental data in published literature， and the calculated flow velocity curve and minimum start-up pressure gradient were highly consistent with the experimental data， which revealed the correctness of the dynamic non-linear flow model. Meanwhile， the comparative analysis with the static non-linear flow model and the Darcy flow model illustrated the rationality of the dynamic non-linear flow model. The sensitivity analysis of the flow velocity curve revealed that the increase in coefficients A and B made the flow velocity curve right-shift； the curvature degree of the non-linear flow section was weakened， and the minimum start-up pressure gradient was increased. The increase in the temperature had a much more significant effect on the flow velocity curve， which made the velocity curve move up obviously. The curvature degree of the non-linear flow section weakened， and the minimum start-up pressure gradient increased. The proposal of this dynamic non-linear flow model further enriches the development of the non-linear flow theory and can be extended to the numerical simulation of two-phase flow and high-temperature and high-pressure oil and gas reservoirs.

Aims: The flow of electric current in the root-soil system relates to the pathways of water and solutes, its characterization provides information on the root architecture and functioning. We developed a current source density approach with the goal of non-invasively image the current pathways in the root-soil system. Methods: A current flow is applied from the plant stem to the soil, the proposed geoelectrical approach images the resulting distribution and intensity of the electric current in the root-soil system. The numerical inversion procedure underlying the approach was tested in numerical simulations and laboratory experiments with artificial metallic roots. We validated the method using rhizotron laboratory experiments on maize and cotton plants. Results: Results from numerical and laboratory tests showed that our inversion approach was capable of imaging root-like distributions of the current source. In maize and cotton, roots acted as “leaky conductors”, resulting in successful imaging of the root crowns and negligible contribution of distal roots to the current flow. In contrast, the electrical insulating behavior of the cotton stems in dry soil supports the hypothesis that suberin layers can affect the mobility of ions and water. Conclusions: The proposed approach with rhizotrons studies provides the first direct and concurrent characterization of the root-soil current pathways and their relationship with root functioning and architecture. This approach fills a major gap toward non-destructive imaging of roots in their natural soil environment.

Dynamic fracture propagation significantly affects water flooding efficiency in tight oil reservoirs. This phenomenon, where moderate fracture openings can enhance water flooding volume and alleviate injection challenges, has been underexplored in current literature. Understanding dynamic fracture behavior poses a challenge due to the difficulty in characterizing them within traditional reservoir numerical simulators. In this study, we propose a numerical simulation method that integrates the KGD dynamic fracture model with a two-phase flow model. This approach enables detailed exploration of dynamic fracture evolution in reservoir scenarios featuring one injector and one production well. Our findings reveal that fractures extend from the water injection well to the oil production well, exhibiting rapid initial growth followed by a slower rate. Fluctuations in fracture tip pressure correspond to cycles of opening and closure. We observe that cumulative oil production increases more rapidly when injection pressure exceeds the fracture opening pressure. However, this growth rate diminishes beyond a certain threshold, highlighting the critical role of injection parameters in dynamic fracture efficacy. Optimal water flooding performance is achieved when injecting water slightly above the fracture opening pressure. Furthermore, we compare water cut curves generated by conventional commercial simulators with our fracture propagation model. Our model's water cut curve aligns better with on-site data, indicating improved historical fitting accuracy. In conclusion, our study underscores the importance of dynamic fractures in enhancing water flooding efficiency in tight oil reservoirs and presents a robust numerical simulation framework for better understanding and management of reservoir dynamics. [ABSTRACT FROM AUTHOR]

The microbiomes associated with bee nests influence colony health through various mechanisms, although it is not yet clear how honeybee congeners differ in microbiome assembly processes, in particular the degrees to which floral visitations and the environment contribute to different aspects of diversity. We used DNA metabarcoding to sequence bacterial 16S rRNA from honey and stored pollen from nests of 4 honeybee species (Apis cerana, A. dorsata, A. florea, and A. laboriosa) sampled throughout Yunnan, China, a global biodiversity hotspot. We developed a computational pipeline integrating multiple databases for quantifying key facets of diversity, including compositional, taxonomic, phylogenetic, and functional ones. Further, we assessed candidate drivers of observed microbiome dissimilarity, particularly differences in floral visitations, habitat disturbance, and other key environmental variables. Analyses revealed that microbiome alpha diversity was broadly equivalent across the study sites and between bee species, apart from functional diversity which was very low in nests of the reclusive A. laboriosa. Turnover in microbiome composition across Yunnan was driven predominantly by pollen composition. Human disturbance negatively impacted both compositional and phylogenetic alpha diversity of nest microbiomes, but did not correlate with microbial turnover. We herein make progress in understanding microbiome diversity associated with key pollinators in a biodiversity hotspot, and provide a model for the use of a comprehensive informatics framework in assessing pattern and drivers of diversity, which enables the inclusion of explanatory variables both subtly and fundamentally different and enables elucidation of emergent or unexpected drivers. [ABSTRACT FROM AUTHOR]