The provenance of microorganisms adapted to extreme salinity, extreme temperature, and toxic metals within the Montney shale formation.
Bibliographic record
Abstract
Introduction Shale oil reservoirs are hypothesized to be sterile due to the extremely high temperature, pressure and salinity within these formations (Evans et al. 2018). High concentrations of toxic metals also pose challenges that demand specific microbial adaptions (Boyd and Barkay 2012, White and Gadd 1998, Ben Fekih et al. 2018). While some microorganisms are introduced into and are selected for within shale formations during hydraulic fracturing, the possibility that certain microorganisms are pre-existing inhabitants of these formations is less clear. Here, we followed the microbial diversity of input and output fluids injected into a Montney formation shale reservoir to assess the distribution and transport of microbial populations during hydraulic fracturing. Enrichment cultures distinguished various metabolisms in the microbial populations found in different sample types, and adaptations allowing them to colonize such niches. Material and methods Fracturing fluid, drilling muds (3302 m, 3350 m and 3400 m depths), shale cuttings (rinsed from the drillings muds), shale core plugs and produced water samples (12-month period) were sampled from a Montney shale oil reservoir. Microbial community compositions were analyzed by amplicon sequencing. Metal content was analyzed by inductively coupled plasma-mass spectrophotometry. High salinity enrichments at 90°C of the drilling muds or rinsed shale samples were set up in triplicate and amended with glucose and guar gum (a mannose/galactose-based polymer used during hydraulic fracturing). Sugars were measured through spectrophotometric assays. Metagenomic analyses were performed to assess microbial gene content. Results/Discussion Provenance of microorganisms from the Montney shale formation Provenance of microorganisms from the Montney shale formation Input fluids (fracturing fluid, drilling muds) were revealed to be the likely source of most of the microbial diversity. However, some microorganisms were only detected in the subsurface samples. ASVs affiliated with Aurantimonas , Caminicella , BRH-c8a (Family Desulfallas ) and Geotoga exhibited occurrence patterns consistent with being derived from subsurface shale formations. Geotoga has only ever been reported from oil reservoirs (Semenova et al. 2020). Analysis of produced water revealed ASVs from these groups increasing in abundance during hydraulic fracturing operations, suggesting selective pressure from oil reservoir conditions (e.g., toxic metal presence, input of saline water, temperature and pressure fluctuations). Incubations set up from drilling muds showed a preference for glucose while incubations of the rinsed shale cuttings showed a microbial preference for guar gum (i.e., mannose production; Fig. 0), reinforcing the presence of different populations being derived from surface and subsurface samples. Adaptations for life in Montney shale Adaptations for life in Montney shale When considering adaptations of microorganisms for environmental conditions found in oil reservoirs, it is relevant to note the presence of toxic metals such as arsenic, cadmium and mercury. Levels of all three metals were found to vary over time within the 28-day shale microbial enrichments and 12-month produced water time course analyses (Suppl. material 1). Metagenomics revealed various genes for the internalization and metabolism of all three metals within the shale microbiome (i.e., arsenate reductases, arsenite transporters, metallothioneins, mercuric reductases). In conclusion, the results of this study suggest that shale reservoirs thus might not be sterile environments, and host microorganisms are able to contend with major perturbations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".