Methods for Recovery of Microorganisms and Intact Microbial Polar Lipids from Oil−Water Mixtures: Laboratory Experiments and Natural Well-Head Fluids
Bibliographic record
Abstract
Most of the world's remaining petroleum resource has been altered by in-reservoir biodegradation which adversely impacts oil quality and production, ultimately making heavy oil. Analysis of the microorganisms in produced reservoir fluid samples is a route to characterization of subsurface biomes and a better understanding of the resident and living microorganisms in petroleum reservoirs. The major challenges of sample contamination with surface biota, low abundances of microorganisms in subsurface samples, and viscous emulsions produced from biodegraded heavy oil reservoirs are addressed here in a new analytical method for intact polar lipids (IPL) as taxonomic indicators in petroleum reservoirs. We have evaluated the extent to which microbial cells are removed from the free water phase during reservoir fluid phase separation by analysis of model reservoir fluids spiked with microbial cells and have used the resultant methodologies to analyze natural well-head fluids from the Western Canada Sedimentary Basin (WCSB). Analysis of intact polar membrane lipids of microorganisms using liquid chromatography-mass spectrometry (LC-MS) techniques revealed that more than half of the total number of microorganisms can be recovered from oil-water mixtures. A newly developed oil/water separator allowed for filtering of large volumes of water quickly while in the field, which reduced the chances of contamination and alterations to the composition of the subsurface microbial community after sample collection. This method makes the analysis of IPLs (or indirectly microorganisms) from well-head fluids collected in remote field settings possible and reliable. To the best of our knowledge this is the first time that IPLs have been detected in well-head oil-water mixtures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".