Microbial community analyses of produced waters from high‐temperature oil reservoirs reveal unexpected similarity between geographically distant oil reservoirs
Bibliographic record
Abstract
As a preliminary investigation for the development of microbial-enhanced oil recovery strategies for high-temperature oil reservoirs (~70 to 90°C), we have investigated the indigenous microbial community compositions of produced waters from five different high-temperature oil reservoirs near Segno, Texas, U.S. (~80 to 85°C) and Crossfield, Alberta, Canada (~75°C). The DNA extracted from these low-biomass-produced water samples were analysed with MiSeq amplicon sequencing of partial 16S rRNA genes. These sequences were analysed along with additional sequence data sets available from existing databases. Despite the geographical distance and difference in the physicochemical properties, the microbial compositions of the Segno and Crossfield produced waters exhibited unexpectedly high similarity, as indicated by the results of beta diversity analyses. The major operational taxonomic units included acetoclastic and hydrogenotrophic methanogens (Methanosaetaceae, Methanobacterium and Methanoculleus), as well as bacteria belonging to the families Clostridiaceae and Thermotogaceae, which have been recognized to include thermophilic, thermotolerant, and/or spore-forming subtaxa. The sequence data retrieved from the databases exhibited different clustering patterns, as the communities from close geographical locations invariably had low beta diversity and the physicochemical properties and conditions of the reservoirs apparently did not have a substantial role in shaping of microbial communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".