Analytical design method for cold production of heavy oil with bottom water using Bilateral Sink Wells

Few heavy oil reservoirs with strong bottom water drives have been developed successfully because of severe water coning. Water coning tends to cause low ultimate recovery, low well productivity, and high water production. Although thermal and gravity-assisted methods might improve recovery in oil reservoirs, such methods are widely perceived as either economically unfavorable or technologically infeasible. This study proposes a new, cold production technique, called Bilateral Water Sink (BWS), to meet those challenges. The BWS method suppresses water cresting by producing oil and water simultaneously from separate, horizontal wells completed in the oil and water zones; the oil and water completions are parallel, with the oil well directly above the water well. In conventional horizontal well production, water cresting causes water to bypass oil, making the water drive mechanism ineffective. BWS controls water invasion by altering the pressure distribution in the near-well area. With cresting suppressed, the oil completion remains water-free, allowing water to displace oil from the edges of the well drainage area to the oil completion, increasing ultimate recovery. Unlike existing heavy oil recovery methods, BWS exploits the natural reservoir energy in the bottom water drive. This makes BWS economically, technically, and environmentally appealing – especially for offshore applications, where cold production is currently the only option and oil-water separation is a problem. In this study, BWS oil recovery is investigated analytically and numerically. A new mathematical model identifies controlling variables and project design parameters, and describes the relationships among them. The design model is used to select rates of water and oil in BWS wells for best performance. The analytical model is verified by a comparison to numerical simulations. These two approaches together provide the quantitative account of the BWS’s effect on avoiding water cresting and improving oil recovery. The results show that BWS can increase oil recovery from 10 percent to over 40 percent in a conventional case, while avoiding the problem of oil-contaminated water production. As a result, the mathematical model of BWS well behavior is shown to be a practical reservoir management tool to guide development of heavy oil reservoirs with bottom water drives

What Facebook Messages Told Us About How We Handled Disaster Management during the COVID-19 Pandemic?

As COVID-19 continues, social media platforms such as Facebook have become an increasingly important tool for communication and information sharing for public and government agencies. The generic disaster management cycle (mitigation, preparedness, response, and recovery) provides systematic guidance to the public and government agencies to respond to the crisis and suggest appropriate measures for different disaster stages. In this study, we examine various trending topics and themes during the COVID-19 outbreak. Using this generic disaster management cycle as our guiding framework, we examine news topics\u27 evolution during the COVID-19 pandemic on Facebook during each of the four phases. Guided Latent Dirichlet Allocation (Guided LDA) is used for topic modeling to identify topics and themes, and text network analytics is used to understand the connectedness of these news topics during each phase and their evolution

Exploring Roles of Emotion in Fake News Detection

Detecting fake news is becoming widely acknowledged as a critical activity with significant implications for social impact. As fake news tends to evoke high-activating emotions from audiences, the role of emotions in identifying fake news is still under-explored. Existing research made efforts in examining effective representations of emotions conveyed in the news content to help discern the veracity of the news. However, the aroused emotions from the audience are usually ignored. This paper first demonstrates effective representations of emotions within both news content and users’ comments. Furthermore, we propose an emotion-aware fake news detection framework that seamlessly incorporates emotion features to enhance the accuracy of identifying fake news. Future work will include thorough experiments to prove that the proposed framework with the emotions expressed in news and users’ comments improves fake news detection performance

Quantitative variations of CD4+CD25+ cells in Peking duckwhite leghorn chimeras based on bone marrow mesenchymal stem cells

Purpose: To develop a chimera via microinjection of poultry xenogeneic bone marrow mesenchymal stem cells (BMMSCs), and to assess its immune tolerance based on variations in proportion of CD4+CD25+ cells in CD4+ cells (specific CD4+CD25+ cells).Methods: BMMSCs were flush out from femurs and tibias of Peking ducks with phosphate-buffered saline and cultured. Their morphology was determined with a microscope. Several surface markers (i.e., CD44, CD45, CD71, CD73 and CD34) were used to identify the cells.Results: The results indicate successful chimera development. CD4+CD25+ cells derived from the thymus of chimeras were migrated to the spleen and cecal tonsils. This migration was more obvious in chimeras than in the control group, suggesting a more robust immune system in the chimeras. The migration tendency gradually decreased with time. There were significant increases in specific CD4+CD25+ cells, TGF-β and IL-10 in cecal tonsils throughout the experimental period (30 days). However, in thymus and spleen, variations in specific CD4+CD25+ cells were observed only on the 1st day post-hatching.Conclusion: The results suggest a relatively pure BMMSC population without contaminating hematopoietic stem cells. Differentiation of the BMMSCs into osteoblasts and adipocytes was inducible, indicating typical MSC character.Keywords: Bone marrow mesenchymal stem cells, Immune tolerance, Chimera, Specific CD4+CD25+ cells, Cell migratio

Self-regulation and conflict goals management capabilities of ecosystem entrepreneurs: a case study of Haier ecosystem

The inherent dual roles of “follower” and “leader” among ecosystem entrepreneurs inevitably introduce challenges in managing conflicting dependent and independent goals. Ecosystem entrepreneurs’ capabilities in conflict goals management directly influence new venture survival and development. This single-case qualitative study explores how ecosystem entrepreneurs develop conflict goals management capabilities through self-regulation, which is not only a unique practical challenge in ecosystem entrepreneurship, but also a cutting-edge topic in current theoretical research. Through research of entrepreneurs in Haier Entrepreneurship Ecosystem, the paper finds: (1) strategic corresponding and mechanism adapting emerge as the two trigger factors enabling ecosystem entrepreneurs to recognize the equilibrium or disequilibrium between conflicting goals; (2) by leveraging self-control, grit, and metacognition, ecosystem entrepreneurs construct decoupling mechanisms for antagonistic goal recognition and coupling mechanisms for synergistic goal recognition; (3) ecosystem entrepreneurs enhance their conflict goals management capabilities by developing both segregative and synergistic management capabilities. Furthermore, this research explores the self-regulation process underlying ecosystem entrepreneurs’ conflict goals management behaviors, including environmental interaction perception, conflict goals analysis, and delineation of goal relationships. Findings provide insights for ecosystem entrepreneurs on improving their conflict goals management capabilities through self-assessment and skill development

A lightweight identity-based cloud storage auditing supporting proxy update and workload-based payment

Cloud storage auditing allows the users to store their data to the cloud with a guarantee that the data integrity can be efficiently checked. In order to release the user from the burden of generating data signatures, the proxy with a valid warrant is introduced to help the user process data in lightweight cloud storage auditing schemes. However, the proxy might be revoked or the proxy’s warrant might expire. These problems are common and essential in real-world applications, but they are not considered and solved in existing lightweight cloud storage auditing schemes. In this paper, we propose a lightweight identity-based cloud storage auditing scheme supporting proxy update, which not only reduces the user’s computation overhead but also makes the revoked proxy or the expired proxy unable to process data on behalf of the user any more. The signatures generated by the revoked proxy or the expired proxy can still be used to verify data integrity. Furthermore, our scheme also supports workload-based payment for the proxy. The security proof and the performance analysis indicate that our scheme is secure and efficient

Impact of ocean acidification on microzooplankton grazing dynamics

This study examines the potential impacts of projected atmospheric carbon dioxide (pCO2) levels reaching 800 ppm by the end of the century, focusing on ocean acidification effects on marine ecosystems in the coastal areas of Bohai. We investigated how acidification affects the grazing patterns of microzooplankton using dilution techniques and ecophysiological methods. Our findings indicate that acidic conditions shift the phytoplankton community structure, changing dominant species. Elevated CO2 concentrations reduced grazing pressure on phytoplankton, with less steep declines in growth rates at 800 ppm CO2 (spring: 2.43 d−1 vs. 2.16 d−1, summer: −0.46 d−1 vs. −0.73 d−1, autumn: −0.45 d−1 vs. −0.90 d−1) and significant decreases in grazing pressure percentages (%Pp from 0.84 to 0.58 and %Pi from 0.64 to 0.46). Short-term acid exposure significantly increased superoxide dismutase activity in both microplankton (from 0.03 to 0.08 U mg−1, p<0.01) and nanoplankton (from 0.05 to 0.09 U mg−1, p<0.001), indicating an adaptive response to oxidative stress. These results highlight that elevated CO2 levels primarily boost phytoplankton growth by reducing microzooplankton grazing pressure, resulting in higher growth rates and a shift towards smaller-sized phytoplankton, reflecting complex short-term ecological responses to acidification. Further research is needed to understand the long-term effects of ocean acidification on microzooplankton and their role in marine secondary productivity

Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors

Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world. We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR) models, which may be with errors because of the limitation of model capacity. Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly. To address this problem, we propose the sentiment word aware multimodal refinement model (SWRM), which can dynamically refine the erroneous sentiment words by leveraging multimodal sentiment clues. Specifically, we first use the sentiment word position detection module to obtain the most possible position of the sentiment word in the text and then utilize the multimodal sentiment word refinement module to dynamically refine the sentiment word embeddings. The refined embeddings are taken as the textual inputs of the multimodal feature fusion module to predict the sentiment labels. We conduct extensive experiments on the real-world datasets including MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the effectiveness of our model, which surpasses the current state-of-the-art models on three datasets. Furthermore, our approach can be adapted for other multimodal feature fusion models easily. Data and code are available at https://github.com/albertwy/SWRM.Comment: Findings of ACL 202