Estimation of the Potential of an Oil-Viscosity-Reducing Bacterium <i>Petrotoga</i> sp. Isolated from an Oil Field for MEOR
Bibliographic record
Abstract
Abstract An anaerobic oil-degrading thermophile, Petrotoga sp. AR80, was isolated from reservoir brine of an oilfield in Japan. This bacterium degrades long-chain hydrocarbons of crude oil into shorter-chain hydrocarbons in brine medium. Therefore, the oil viscosity can be decreased and the enhancement of oil recovery can be expected by this bacterium. The potential of this bacterium as a candidate for MEOR was estimated in this study. Some nitrogen source which is essential for bacteria should be injected into reservoir to stimulate the bacterial growth. To select a suitable brine medium for this bacterial activity on the reduction of crude oil viscosity, the numbers of nitrogen sources were evaluated. The highest growth rate was observed in medium contained yeast extract with maximum cell concentration in this medium was 10 times higher than that in other mediums. The influence of reservoir conditions such as temperature, salinity, and pressure on the bacterium was evaluated. This bacterium can grow and decrease oil viscosity at a temperature between 50 °C and 70 °C. The oil viscosity incubated with Petrotoga sp. AR80 was 46.3 %, 51.6 % and 65.9 % lower than control after 3 weeks incubation at 50 °C, 60 °C and 70 °C. In addition, the oil viscosity was 35.1 % lower than control at 70 °C and 800 psi. The result of GC chromatogram showed that this bacterium can convert and/or degrade various components of oil lead to depletion in heavier oil components and enrichment of light oil components respectively. Oil extracted from Oman, China and Canada can be utilized by this bacterium instead of Japanese oil. These results show that the isolated bacterium can be applied to a wide range of reservoirs for MEOR. In addition, this bacterium achieves economically feasible MEOR because it doesn't need costly nutrients such as molasses which has been used as a nutrient in MEOR.
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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 | 0.000 | 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) | 0.001 | 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".