Microbially Enhanced Oil Recovery by Alkylbenzene-Oxidizing Nitrate-Reducing Bacteria
Bibliographic record
Abstract
Microbially enhanced oil recovery (MEOR) of heavy oil and bitumen is challenging because light hydrocarbons, which can feed resident microbial communities are present in low concentrations, if at all. We have recently shown that increasing the toluene concentration of heavy oil by aqueous injection followed by injection of nitrate boosts the activity of toluene-oxidizing nitrate-reducing bacteria in heavy oil-containing sand pack columns, giving production of residual oil in place (ROIP). In the current work we found that ethylbenzene is as effective as toluene. Microbial community analyses indicated Thauera and Pseudomonas to be main components of nitrate-containing batch and continuous cultures, regardless whether ethylbenzene or toluene was used as the electron donor. Biomass from batch cultures grown with heavy oil amended with ethylbenzene or toluene and nitrate or biomass from continuous cultures grown on ethylbenzene or toluene and nitrate had similar MEOR activity. Increasing the concentration of injected biomass from continuous cultures increased the fraction of ROIP recovered both in the absence and in the presence of nitrate. Nitrate increased the fraction of ROIP recovered by about 2-fold by increasing the concentration of biomass in the columns. Emulsification of oil by surface-adhering biomass and blocking of aqueous flow channels by oil emulsion droplets are proposed as a possible mechanism of hydrocarbon- and nitrate-mediated MEOR. Pure isolates Thauera sp. NS1 and Pseudomonas sp. NS2, which used both ethylbenzene and toluene, were obtained but did not offer improved MEOR compared to the use of batch and continuous cultures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".