It is significant to accurately evaluate the relative permeability of oil–water two phase for multiphase seepage in porous media in low permeability and tight oil reservoir. However, stress sensitivity is an important characteristic for low permeability and tight oil reservoir. It is an effective way for fractal theory to describe the complexity and heterogeneity of the microstructure of porous media. To describe the relative permeability of oil–water two phase in porous media with complex and irregularity pores, a new relative permeability model oil–water two phases is proposed by the fractal theory and the stress sensitivity is taken into the established model in this paper. Meanwhile, the effects of effective stress, elastic modulus, porosity, maximum and minimum flow radius on oil–water relative permeability are analyzed. The new model is verified by comparing with the laboratory data and the results demonstrate that irreducible water and residual oil saturation have a negative correlation with effective stress. The relative permeability of the oil–water two-phase will shrink to the middle as the rise of effective stress, and the region of co-infiltration will decrease. The deformation quantity of porous media, irreducible water and residual oil saturation will increase as the elastic modulus decreases. The larger the maximum flow radius is, the lower the irreducible water saturation and residual oil saturation is. Both the porosity and the minimum flow radius have slight influences on the relative permeability of oil–water two-phase. The proposed relative permeability model can effectively predict the relative permeability of oil and water and help to describe and reveal the multiphase flow in porous media.
Injecting CO2 into depleted gas reservoirs can sequester greenhouse gases and simultaneously enhancing gas recovery, which has significant environmental and economic benefits. Natural gas resources in tight sandstone reservoirs are huge, but the gas production decreases rapidly and the gas recovery is low due to poor reservoir properties. When these gas reservoirs are depleted, the implementation of CO2 flooding has greater potential to improve gas production and store CO2. However, the production characteristics of the CO2 flooding process and application potential in tight gas reservoirs at the field scale are not yet clear. To fully understand the production mechanism of the CO2 flooding and evaluate the technical feasibility, based on the geological data of the Sulige gas field in the Ordos Basin, a 3D numerical simulation model under the five-point well pattern is established. The production behavior of enhanced gas recovery and CO2 storage processes is studied through numerical simulation approach. Results indicate that the CH4 production rate is significantly increased after CO2 flooding, and the gas recovery can be increased by up to 19.2%, confirming the feasibility of CO2 injection to enhance CH4 production in depleted tight gas reservoirs. Once the CO2 breakthrough occurs, the CH4 production rate decreases rapidly, and the CO2 distribution is only slightly affected by the gravity difference of the components. These characteristics are significantly different from those of high-permeability gas reservoirs. The CO2 front in the early stage is proportional to the square root or cube root of time, depending on the perforation location and reservoir thickness. However, the CO2 front in the late flood stage shows a linear relationship with the square of time. It is recommended that injection well and production wells are completely perforated because the enhanced gas recovery is higher than other perforation options and excessive bottom-hole pressure in the injection well can be avoided. The new findings of this work can provide some insights into the production mechanism of CO2 storage and enhanced recovery in tight gas reservoirs, which is beneficial for reducing investment risks and improving production efficiency for future large-scale field applications.
This paper discusses the so-called Bakers’ Strike Edict from Ephesus (Ephesos 231 = IK 12.215 p. 27) in light of recent studies on the Roman imperial toolkit to build empire-wide communities. Clifford Ando and Myles Lavan argued that Roman emperors in the first two centuries CE were consciously blurring distinctions between Roman and non-Roman populations, so that there could be a shared sense of an empire-wide community among people in the provinces. This paper argues that, in addition to Lavan’s observations, gubernatorial edicts also show concerns and measures that sought to communicate a sense of the communal at the local level. While the focus of discussion is on the edict responding to a bakers’ strike at Ephesus, several other examples from a corpus of gubernatorial edicts are also used in connection with this example. This paper hopes to contribute to Ando’s and Lavan’s arguments by pointing to a lower register of community building visible in gubernatorial edicts. The governors’ concerns for and efforts to creating communal cohesion and their need to balance parallel and at times competing “common goods” not only adds another nuance to the grander community building project at the imperial level, but demonstrates further complications on how praesidial governors – and in particular proconsuls – can and would react to difficult issues at the local level.
Spontaneous imbibition is an important phenomenon of fluid transports in porous media, particularly in liquid environment with a certain range of interfacial tension and wettability. Meanwhile, the spontaneous imbibition could be enhanced with the additions of surfactants, through modifying the oil–water interfacial tension (IFT) and wettability. However, ultra-low IFT impairs the capillary pressure. Hence, appropriate ranges of the IFT and contact angle (CA), though lacking adequate investigations, are key to optimizing spontaneous imbibition. Here, a series of physical experiments were conducted to evaluate the spontaneous imbibition efficiency of surfactant solutions with wide-range IFTs (10−3–101 mN/m) in the porous media of permeability 11 mD, through designed interfacial property measurements with different surfactant concentrations. Besides, the inverse Bond number was employed to determine the optimized interfacial properties during the imbibition. Overall, the best imbibition-induced hydrocarbon recovery is reached at oil viscosity of 25.26 mPa·s, an IFT of 0.1–0.2 mN/m and a CA of 70–80°.
The infamous numerical sign problem poses a fundamental obstacle to particle- based stochastic Wigner simulations in high-dimensional phase space. Although the existing particle annihilation (PA) via uniform mesh significantly alleviates the sign problem when dimensionality D <= 4, the mesh size grows dramatically when D >= 6 due to the curse of dimensionality and consequently makes the annihilation very inefficient. In this paper, we propose an adaptive PA algorithm, termed sequential-clustering particle annihilation via discrepancy estimation (SPADE), to overcome the sign problem. SPADE follows a divide-and-conquer strategy: adaptive clustering of particles via controlling their number-theoretic discrepancies and independent random matching in each cluster. The target is to alleviate the oversampling problem induced by the overpartitioning of phase space and to capture the nonclassicality of the Wigner function simultaneously. Combining SPADE with the variance reduction technique based on the stationary phase approximation, we attempt to simulate the proton-electron couplings in six- and 12-dimensional phase space. A thorough performance benchmark of SPADE is provided with the reference solutions in six-dimensional phase space produced by a characteristic-spectral-mixed scheme under a 733*803 uniform grid, which fully explores the limit of grid-based deterministic Wigner solvers.
Antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) pose a significant threat to both ecosystems and human health. Owing to the excellent catalytic activity, eco-safety, and convenience for defect engineering, BiOBr with oxygen vacancies (OVs) of different density thus were fabricated and employed to activate H2O2 for ARB disinfection/ARGs degradation in present study. We found that BiOBr with OVs of appropriate density induced via ethanol reduction (BOB-E) could effectively activate H2O2, achieving excellent ARB disinfection and ARGs degradation efficiency. Moreover, this disinfection system exhibited remarkable tolerance to complex water environments and actual water conditions. In-situ characterization and theoretical calculations revealed that OVs in BOB-E could effectively capture and activate aqueous H2O2 into HO· and O2·−. The generated reactive oxygen species combined with electron transfer could damage the cell membrane system and degrade genetic materials of ARB, leading to effective disinfection. The impressive reusability, high performance achieved in two immobilized reaction systems (packed column and baffled ditch reactor), excellent degradation of emerging organic pollutants supported the feasibility of BOB-E/H2O2 system towards practical water decontamination. Overall, this study not only provides insights into fabrication of bismuth-based catalysts for efficient ARB disinfection/ARGs degradation via OVs regulation, but also paves the way for their practical applications.
In the era of a green economy, green innovation has become a way for enterprises to gain competitive advantage, and it is of great theoretical and practical significance to explore the driving force of enterprises' green innovation. This study explores the peer effect of an enterprise's green innovation and conducts an empirical test using data from 3338 Chinese listed companies in 2020. The results show a significant positive peer effect of enterprises' green innovation, and the green innovation of individual enterprises increases by 0.869 for each unit increase in industry-average green innovation. Further research shows that market power is the channel by which peer influence affects an enterprise's green innovation. Moreover, regional heterogeneity exists in the strength of the peer effect, which varies according to firm maturity and board size. These findings provide a reference for enterprises and governments to promote green transformation.
Sulphate-reducing microorganisms, or SRMs, are crucial to organic decomposition, the sulphur cycle, and the formation of pyrite. Despite their low energy-yielding metabolism and intense competition with other microorganisms, their ability to thrive in natural habitats often lacking sufficient substrates remains an enigma. This study delves into how Desulfovibrio desulfuricans G20, a representative SRM, utilizes photoelectrons from extracellular sphalerite (ZnS), a semiconducting mineral that often coexists with SRMs, for its metabolism and energy production. Batch experiments with sphalerite reveal that the initial rate and extent of sulphate reduction by G20 increased by 3.6 and 3.2 times respectively under light conditions compared to darkness, when lactate was not added. Analyses of microbial photoelectrochemical, transcriptomic, and metabolomic data suggest that in the absence of lactate, G20 extracts photoelectrons from extracellular sphalerite through cytochromes, nanowires, and electron shuttles. Genes encoding movement and biofilm formation are upregulated, suggesting that G20 might sense redox potential gradients and migrate towards sphalerite to acquire photoelectrons. This process enhances the intracellular electron transfer activity, sulphur metabolism, and ATP production of G20, which becomes dominant under conditions of carbon starvation and extends cell viability in such environments. This mechanism could be a vital strategy for SRMs to survive in energy-limited environments and contribute to sulphur cycling.
Polyhydroxyalkanoates (PHAs) are a class of microbially synthesized polyesters with diverse structures with renewability, biodegradability, good biocompatibility, and broad application prospects. However, the level of commercialization of PHAs remains low. The high recovery cost is one of the main reasons preventing the widespread use of these "green polymers". For decades, efforts have been made to explore lower-cost, greener, and more economical PHAs recovery strategies, and significant progress has been made. This review presents cell lysis and yeast surface display (YSD)-based bio-recovery strategies for PHAs, and then proposes a model hypothesis for protein-mediated secretion of PHAs drawing on the lipid secretion model to provide essential information for further cost reduction and efficiency in the recovery of PHAs. In addition, this review also highlights the bio-recovery strategy of extracellular PHAs based on synthetic biology and exploring specific PHAs secretion mechanism is a promising strategy for reducing the cost of PHAs recovery in the future.
The plastisphere may act as reservoir of antibiotic resistome, accelerating global antimicrobial resistance dissemination. However, the environmental risks in the plastisphere of field microplastics (MPs) in farmland remain largely unknown. Here, antibiotic resistance genes (ARGs) and virulence factors (VFs) on polyethylene microplastics (PE-MPs) and polybutylene adipate terephthalate and polylactic acid microplastics (PBAT/PLA-MPs) from residues were investigated using metagenomic analysis. The results suggested that the profiles of ARG and VF in the plastisphere of PBAT/PLA-MPs had greater number of detected genes with statistically higher values of diversity and abundance than soil and PE-MP. Procrustes analysis indicated a good fitting correlation between ARG/VF profiles and bacterial community composition. Actinobacteria was the major host for tetracycline and glycopeptide resistance genes in the soil and PE-MP plastisphere, whereas the primary host for multidrug resistance genes changed to Proteobacteria in PBAT/PLA-MP plastisphere. Besides, three human pathogens, Sphingomonas paucimobilis, Lactobacillus plantarum and Pseudomonas aeruginosa were identified in the plastisphere. The PE-MP plastisphere exhibited a higher transfer potential of ARGs than PBAT/PLA-MP plastisphere. This work enhances our knowledge of potential environmental risks posed by microplastic in farmland and provides valuable insights for risk assessment and management of agricultural mulching applications.
The atmospheric aqueous-phase chemistry has received increasing attention in the last decades for its non-negligible environmental significance. Yet, the insufficient experimental data on oxidative reaction rate constants (kaq) obstructs the further analysis and modeling of this system. Predictive models based on machine learning (ML) algorithms have shown potential as an effective estimation tool, however, they are restricted to the lack of training data as well. To overcome this data limitation, we developed multi-task (MT) models that could exploit the common knowledge from reactions in gas- and aqueous-phases simultaneously. Toward kaq of organic compounds with hydroxyl radical (OH), nitrate radical (NO3), and ozone (O3), the MT models showed a notably better predictive ability compared to benchmark models, while obtaining wide applicability on compounds from different chemical classes. By interpreting the models using Shapley additive explanations (SHAP), we evidenced that the MT models utilized the common knowledge in both phases and correctly identified the reaction mechanisms. This study aims to provide new insight into the estimation of necessary kinetic parameters in atmospheric aqueous-phase chemistry, as well as a reference to ML research for other predictive tasks of atmospheric interest.
Mineral scale refers to the hard inorganic solids nucleated on substrates or deposited from the aqueous phase. The formation and deposition of barium sulfate and strontium sulfate in various industries, such as water treatment and oilfield operations, can significantly impact facility operations, posing serious threats. Machine learning (ML) approaches have been adopted recently in scale threat predictions to address the limitations of conventional scaling prediction models. However, there are few reports on collecting sulfate mineral scaling data, employing ML methods for data analysis, and evaluating the modeling results to gain deeper insights of sulfate mineral scaling process and to improve the accuracy of sulfate scaling threat prediction. Despite comprehensive experimental studies, the literature does not provide adequate guidance for identifying the influence on the solubility of barium sulfate and strontium sulfate under different aqueous environments and actual operating conditions. To this end, this study collected 1600 experimental datasets of barium/strontium sulfate from the literature to construct and evaluate the reliability and versatility of a ML-based model for sulfate solubility calculations. Single neural networks, hybrid neural networks, and optimization algorithms were employed to build solubility prediction models for barium sulfate and strontium sulfate across a wide range of temperatures, pressures, and different ions. The model's applicability in predicting sulfate scaling threats in various actual operating environments demonstrated its broad usability, consistent with its actual performance. This study marks the first stride towards constructing a reliable model for identifying the scaling trends of barium sulfate and strontium sulfate across various operating conditions, underscoring the importance of developing robust and accurate prediction models to address challenges in various industrial systems.
