Water droplets in oil are microhabitats for microbial life

Science ◽  
2014 ◽  
Vol 345 (6197) ◽  
pp. 673-676 ◽  
Author(s):  
Rainer U. Meckenstock ◽  
Frederick von Netzer ◽  
Christine Stumpp ◽  
Tillmann Lueders ◽  
Anne M. Himmelberg ◽  
...  

Anaerobic microbial degradation of hydrocarbons, typically occurring at the oil-water transition zone, influences the quality of oil reservoirs. In Pitch Lake, Trinidad and Tobago—the world’s largest asphalt lake—we found that microorganisms are metabolically active in minuscule water droplets (1 to 3 microliters) entrapped in oil. Pyrotag sequencing of individual droplet microbiomes revealed complex methanogenic microbial communities actively degrading the oil into a diverse range of metabolites, as shown by nuclear magnetic resonance and Fourier transform ion cyclotron resonance mass spectrometry. High salinity and water-stable isotopes of the droplets indicate a deep subsurface origin. The 13.5% water content and the large surface area of the droplets represent an underestimated potential for biodegradation of oil away from the oil-water transition zone.

2020 ◽  
Vol 86 (11) ◽  
Author(s):  
M. Pannekens ◽  
L. Voskuhl ◽  
A. Meier ◽  
H. Müller ◽  
S. Haque ◽  
...  

ABSTRACT Most of the microbial degradation in oil reservoirs is believed to take place at the oil-water transition zone (OWTZ). However, a recent study indicates that there is microbial life enclosed in microliter-sized water droplets dispersed in heavy oil of Pitch Lake in Trinidad and Tobago. This life in oil suggests that microbial degradation of oil also takes place in water pockets in the oil-bearing rock of an oil leg independent of the OWTZ. However, it is unknown whether microbial life in water droplets dispersed in oil is a generic property of oil reservoirs rather than an exotic exception. Hence, we took samples from three heavy-oil seeps, Pitch Lake (Trinidad and Tobago), the La Brea Tar Pits (California, USA), and an oil seep on the McKittrick oil field (California, USA). All three tested oil seeps contained dispersed water droplets. Larger droplets between 1 and 10 μl revealed high cell densities of up to 109 cells ml−1. Testing for ATP content and LIVE/DEAD staining showed that these populations consist of active and viable microbial cells with an average of 60% membrane-intact cells and ATP concentrations comparable to those of other subsurface ecosystems. Microbial community analyses based on 16S rRNA gene amplicon sequencing revealed the presence of known anaerobic oil-degrading microorganisms. Surprisingly, the community analyses showed similarities between all three oil seeps, revealing common OTUs, although the sampling sites were thousands of kilometers apart. Our results indicate that small water inclusions are densely populated microhabitats in heavy oil and possibly a generic trait of degraded-oil reservoirs. IMPORTANCE Our results confirmed that small water droplets in oil are densely populated microhabitats containing active microbial communities. Since these microhabitats occurred in three tested oil seeps which are located thousands of kilometers away from each other, such populated water droplets might be a generic trait of biodegraded oil reservoirs and might be involved in the overall oil degradation process. Microbial degradation might thus also take place in water pockets in the oil-bearing oil legs of the reservoir rock rather than only at the oil-water transition zone.


2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.


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