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Natural gas hydrates (NGHs) are regarded as a new energy resource with great potential and wide application prospects due to their tremendous reserves and low CO2 emission. Permeability, which governs the fluid flow and transport through hydrate-bearing sediments (HBSs), directly affects the fluid production from hydrate deposits. Therefore, permeability models play a significant role in the prediction and optimization of gas production from NGH reservoirs via numerical simulators. To quantitatively analyze and predict the long-term gas production performance of hydrate deposits under distinct hydrate phase behavior and saturation, it is essential to well-establish the permeability model, which can accurately capture the characteristics of permeability change during production. Recently, a wide variety of permeability models for single-phase fluid flowing sediment have been established. They typically consider the influences of hydrate saturation, hydrate pore habits, sediment pore structure, and other related factors on the hydraulic properties of hydrate sediments. However, the choice of permeability prediction models leads to substantially different predictions of gas production in numerical modeling. In this work, the most available and widely used permeability models proposed by researchers worldwide were firstly reviewed in detail. We divide them into four categories, namely the classical permeability models, reservoir simulator used models, modified permeability models, and novel permeability models, based on their theoretical basis and derivation method. In addition, the advantages and limitations of each model were discussed with suggestions provided. Finally, the challenges existing in the current research were discussed and the potential future investigation directions were proposed. This review can provide insightful guidance for understanding the modeling of fluid flow in HBSs and can be useful for developing more advanced models for accurately predicting the permeability change during hydrate resources exploitation.

This paper concerns an infinite-dimensional observer for manipulation of flexible beam by a rigid arm robot. The complex dynamic of the system is described by distributed parameter model in terms of ordinary differential equations and partial differential equation. A novel infinite-dimensional observer is proposed to estimate the vibration information of the flexible object. In addition, an observer-based independent joint controller is designed to achieve the position control and vibration suppression, which do not need end-point boundary control. The semigroup theory and LaSalle’s invariance principle are adopted to prove the asymptotic stability of the robot system. The efficiency of the observers and the proposed control strategy are demonstrated by numerical simulations.

CO2 enhanced shale gas recovery (CO2-ESGR) draws worldwide attentions in recent years with having significant environmental benefit of CO2 geological storage and economic benefit of shale gas production. This paper is aimed at reviewing the state of experiment and model studies on gas adsorption, competitive adsorption of CO2/CH4, and displacement of CO2-CH4 in shale in the process of CO2-ESGR and pointing out the related challenges and opportunities. Gas adsorption mechanism in shale, influencing factors (organic matter content, kerogen type, thermal maturity, inorganic compositions, moisture, and micro/nano-scale pore), and adsorption models are described in this work. The competitive adsorption mechanisms are qualitatively ascertained by analysis of unique molecular and supercritical properties of CO2 and the interaction of CO2 with shale matrix. Shale matrix shows a stronger affinity with CO2, and thus, adsorption capacity of CO2 is larger than that of CH4 even with the coexistence of CO2-CH4 mixture. Displacement experiments of CO2-CH4 in shale proved that shale gas recovery is enhanced by the competitive adsorption of CO2 to CH4. Although the competitive adsorption mechanism is preliminary revealed, some challenges still exist. Competitive adsorption behavior is not fully understood in the coexistence of CO2 and CH4 components, and more experiment and model studies on adsorption of CO2-CH4 mixtures need to be conducted under field conditions. Coupling of competitive adsorption with displacing flow is key factor for CO2-ESGR but not comprehensively studied. More displacement experiments of CO2-CH4 in shale are required for revealing the mechanism of flow and transport of gas in CO2-ESGR.

In recent years, CO2 flooding has emerged as an efficient method for improving oil recovery. It also has the advantage of storing CO2 underground. As one of the promising types of CO2 enhanced oil recovery (CO2-EOR), CO2 water-alternating-gas injection (CO2-WAG) can suppress CO2 fingering and early breakthrough problems that occur during oil recovery by CO2 flooding. However, the evaluation of CO2-WAG is strongly dependent on the injection parameters, which in turn renders numerical simulations computationally expensive. So, in this work, machine learning is used to help predict how well CO2-WAG will work when different injection parameters are used. A total of 216 models were built by using CMG numerical simulation software to represent CO2-WAG development scenarios of various injection parameters where 70% of them were used as training sets and 30% as testing sets. A random forest regression algorithm was used to predict CO2-WAG performance in terms of oil production, CO2 storage amount, and CO2 storage efficiency. The CO2-WAG period, CO2 injection rate, and water–gas ratio were chosen as the three main characteristics of injection parameters. The prediction results showed that the predicted value of the test set was very close to the true value. The average absolute prediction deviations of cumulative oil production, CO2 storage amount, and CO2 storage efficiency were 1.10%, 3.04%, and 2.24%, respectively. Furthermore, it only takes about 10 s to predict the results of all 216 scenarios by using machine learning methods, while the CMG simulation method spends about 108 min. It demonstrated that the proposed machine-learning method can rapidly predict CO2-WAG performance with high accuracy and high computational efficiency under conditions of various injection parameters. This work gives more insights into the optimization of the injection parameters for CO2-EOR.

ObjectiveTo qualitatively explore the factors that enhance resilience among emergency nurses (ENs).DesignThis study is an exploratory qualitative investigation. Semistructured in-depth interviews were used for data collection, while qualitative content analysis was applied for data analysis.SettingA grade A tertiary hospital in Shanghai, China.ParticipantsThe study subjects comprised 17 ENs, who were selected using a purposive sampling method.ResultsThree main themes and the nine subthemes emerged from the study, that is, individual resources, including competency, personality traits and occupational benefits; family resources, including close parent–child attachment and supportive family dynamics; social resources, including peer support, organisational support, resilient leadership and popular support.ConclusionThis qualitative study explored the factors promoting resilience among ENs and provided a reference for managers to formulate future management strategies. From the perspective of positive psychology, nurses should receive comprehensive support, focusing on improving their professional accomplishment and role ability while prioritising the development of resilient leadership. These efforts are expected to drive progress and growth across the emergency care team.

Recent advances in computer and data sciences have made artificial intelligence techniques a useful tool in tackling the problems in petroleum exploration and production [...]

The geological storage of carbon dioxide (CO2) is a crucial technology for mitigating global temperature rise. Near-depleted edge–bottom water reservoirs are attractive targets for CO2 storage, as they can not only enhance oil recovery (EOR) but also provide important potential candidates for geological storage. This study investigated CO2-enhanced oil recovery and storage for a typical near-depleted edge–bottom water reservoir that had been developed for a long time with a recovery factor of 51.93%. To improve the oil recovery and CO2 storage, new production scenarios were explored. At the near-depleted stage, by comparing the four different scenarios of water injection, gas injection, water-alternating-gas injection, and bi-directional injection, the highest additional recovery of 3.62% was achieved via the bi-directional injection scenario. Increasing the injection pressure led to a higher gas–oil ratio and liquid production rate. After shifting from the near-depleted to the depleted stage, the most effective approach to improving CO2 storage capacity was to increase reservoir pressure. At 1.4 times the initial reservoir pressure, the maximum storage capacity was 6.52 × 108 m3. However, excessive pressure boosting posed potential storage and leakage risks. Therefore, lower injection rates and longer intermittent injections were expected to achieve a larger amount of long-term CO2 storage. Through the numerical simulation study, a gas injection rate of 80,000 m3/day and a schedule of 4–6 years injection with 1 year shut-in were shown to be effective for the case considered. During 31 years of CO2 injection, the percentage of dissolved CO2 increased from 5.46% to 6.23% during the near-depleted period, and to 7.76% during the depleted period. This study acts as a guide for the CO2 geological storage of typical near-depleted edge–bottom water reservoirs.

Magnesium alloys have great potential in biomedical applications due to their unique combination of satisfactory mechanical property and decent biodegradability. However, their poor corrosion resistance limits their applications in biomedical fields. In this work, we employ a chemical conversion deposition method to prepare a Zr-based conversion film on the surface of AZ31 magnesium alloy to serve as a passivation layer. The mechanism for the film formation was studied and it showed the deposition process consists of four steps: substrate dissolution, nucleation, film growth, and film equilibrium. The film is mainly composed of Zr(OH) 4 /ZrO and Mg(OH) 2 /MgO with small amount of MgF 2 and ZrF 4 . The protective performance of the Zr-based film was investigated by electrochemical and immersion tests in simulated body fluid (SBF). Electrochemical results showed a significant decrease in the corrosion current density ( I corr ), a positive shift of corrosion potential ( E corr ), a bigger capacitive loop diameter and higher impedance values for the Zr-coated substrate as compared with an uncoated one. Immersion results indicated the corrosion rate of the Zr-coated sample was ∼20% lower than that of an uncoated one. All above results corroborate the great potential of Zr-based coating in enabling AZ31 alloy for biomedical applications.

CO2-enhanced oil recovery (CO2-EOR) has gained prominence as an effective oil displacement method with low carbon emissions, yet its microscopic mechanisms remain incompletely understood. This study introduces a novel high-pressure microfluidic visualization system capable of operating at 0.1–10 MPa without confining pressure and featuring stratified porous media with a 63 μm minimum throat size to provide unprecedented insights into CO2 and CO2-foam EOR processes at the microscale. Through quantitative image analysis and advanced machine learning modeling, we reveal that increasing the CO2 injection pressure nonlinearly reduces residual oil saturation, achieving near-complete miscibility at 6 MPa with only 2% residual oil—a finding that challenges conventional thresholds for miscibility in heterogeneous systems. Our work uniquely demonstrates that CO2-foam flooding not only mobilizes capillary-trapped oil films but also dynamically alters interfacial tension and the pore-scale fluid distribution, a phenomenon previously underexplored. Support Vector Regression (R2 = 0.71) further uncovers a nonlinear relationship between the surfactant concentration and residual oil saturation, offering a data-driven framework for parameter optimization. These results advance our fundamental understanding by bridging microscale dynamics with field-applicable insights, while the integration of machine learning with microfluidics represents a methodological leap for EOR research.

Background Due to the unique working environment and nature of work in emergency departments, nurses are prone to experiencing compassion fatigue (CF), leading to job burnout and attrition. As more Generation Z (Gen Z) nurses enter the emergency department with distinct personality traits compared to previous generations, studying their experiences with CF will inform future management strategies. Methods The qualitative phenomenological research method was utilised to investigate CF among Gen Z emergency nurses at a hospital in Shanghai, China. Data were collected through face-to-face semi-structured interviews and analyzed using Colaizzi’s seven-step phenomenological analysis method. Study participants were purposively selected. Results Three main themes and nine sub-themes emerged from the study: secondary traumatic stress, including physiological symptoms, psychological symptoms, and behavioral changes; cumulative effects, including impaired empathy, interference with family life, and post-traumatic growth (PTG); coping strategies, including cognitive reconstruction, seeking support, and facilitating action. Conclusions The aim of this study is to investigate the experience of CF among Gen Z emergency nurses, providing managers with a reference for future management strategies. The significance of multi-dimensional support for Gen Z emergency nurses is underscored by our findings. Additionally, interventions that enhance resilience and competency can facilitate their psychological transformation after experiencing CF and promote accelerated personal growth.