Search

Production forecasts are extremely sensitive to the spatial distribution of certain reservoir parameters, especially permeability, which controls fluid flow and efficiency of many recovery methods. Modeling the heterogeneity in permeability distribution is challenging due to various reasons. First, proper sampling is difficult due to biased sampling, dilation effects and sample disturbance. Second, calibration of core-plug measurements with well test data is problematic because of their vast differences in scale; in the case of heavy oil reservoirs, conventional well tests are not feasible due to bitumen's ultra-low mobility at initial conditions. Therefore, in addition to flow-based measurements, alternative static data including image logs and core photographs, which capture detailed high-resolution information regarding heterogeneities at the millimeter scale, may be integrated for reliable permeability estimation along the wellbore. This paper examines the existing technique for permeability prediction from image logs (or core photographs) and proposes a set of improvements. For example, automatic variogram modeling is performed over a set of moving windows to simulate the sand–shale indicators along the well trajectory, capturing the locally varying anisotropy in variogram model parameters. In addition, it presents a unified approach to properly quantify the uncertainty in the histogram of core measurements and well log data: Joint histogram uncertainty considering the correlations between variables is incorporated into the existing methodology. The millimeter-scale model is constructed and upscaled to the centimeter scale via steady-state flow-based upscaling. This step is repeated with the centimeter-scale model until the final flow-simulation scale is attained. The entire methodology is applied within individual facies with distinct spatial correlations. A field case study involving data from the McMurray Formation (Alberta, Canada) is presented, where the permeability distributions in five different facies are modeled. The results indicate that considering automatic variogram modeling may lead to more realistic representation of the orientation of certain heterogeneous features that are strongly influencing the permeability to flow; furthermore, incorporating histogram uncertainty enables the modeling of porosity and permeability with improved uncertainty. [ABSTRACT FROM AUTHOR]

Biosafety has become one of the greatest challenges facing humanity. Outbreaks of infectious diseases caused by bacteria and viruses have had a huge impact on public health. In addition, non-severe polluted air quality has gradually become the norm; however, literature on the impacts of bioaerosols under long-term exposure to low concentrations of PM 2.5 in China is limited. This study analyzed the evolution of the PM 2.5 bacterial community in the Huairou district of Beijing under different pollution conditions. We used high-throughput sequencing to seasonally analyze samples over a year (from July 2018 to May 2019) and winter samples from different years (2015, 2016, 2018, and 2019). The results showed that the bacterial diversity and community composition of PM 2.5 were significantly different in different seasons, whereas under different pollution levels, there were no significant differences. During the observation period, the number of bacterial species decreased with the increase in pollution; however, a high proportion of bacteria can exist as core species under different pollution levels for a long time. Furthermore, bacteria can be relatively stable in the local environment during the same season but in different years. Although the relative abundances of different bacteria change differently with the variation in pollution level, there is no statistical difference. Importantly, there was a higher abundance of opportunistic pathogenic bacteria when the air quality index was 0-100 in winter. This study comprehensively revealed the characteristics of the evolution of bacterial communities under different pollution levels and in different years and emphasized the health effects of non-pollution air quality. This study can provide a theoretical basis for establishing a sound environmental microbial monitoring and defense system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Bacteria and fungi are primary constituents of airborne microbes in fine particulate matter (PM 2.5 ) and significantly impact human health. However, hitherto, seasonal variation and effect of air pollution on microbial community composition and structure are poorly understood. This study analyzed the bacterial and fungal composition of PM 2.5 under different air pollution levels during different seasons in Beijing. We altogether collected 75PM 2.5 samples during four seasons from April 2014 to January 2015, under different air pollution levels and employed high-throughput sequencing methods to analyze microbial composition. The results showed that air pollution decreased species richness and community diversity of bacteria in PM 2.5 . The variation in bacterial and fungal community composition and structure was significantly related to the season but there was no correlation between their abundance and pollution levels. Pathogenic bacteria and fungi were more abundant in winter than other seasons. To best of our knowledge, this is the first study that demonstrates seasonal variation characteristics of bacteria and fungi in PM 2.5 in heavy haze contaminated areas and highlights the effects of air pollution on the atmospheric microbial community. This study would be useful to other bioaerosol studies focusing on the role of the atmospheric particulate matter on human health., (Copyright © 2017 Elsevier B.V. All rights reserved.)