<i>Bacillus subtilis</i> ‐based microbial enhanced oil recovery ( <scp>MEOR</scp> ) in polymer microfluidic chip
Bibliographic record
Abstract
Abstract Microbial enhanced oil recovery (MEOR) is a tertiary oil recovery process that manipulates the microbial environment inside oil reservoirs to modify the physical/chemical properties of the reservoirs to enhance the oil recovery. Up to now, the detailed MEOR mechanism is still not entirely clear due to the multiple influence factors (e.g., pH, nutrients, temperature, porosity, and permeability) on microbial growth and reproduction, as well as the lack of understanding of microbial's influencing mechanism on the oil recovery process. In this study, a Bacillus subtilis ‐based MEOR process was conducted in a polymethyl methacrylate (PMMA)‐based microfluidic device to mimic the MEOR process in the reservoir. The porous microstructure based on real sandstone slice images was fabricated with laser ablation on a PMMA substrate. Two different MEOR approaches were conducted in the PMMA‐based microfluidics devices: the direct injection of displacing reagent (biosurfactant produced by bacteria) into the microfluidic chip for the oil recovery (ex‐situ), and the incubation of bacteria solution inside the chip followed with brine flooding (in‐situ). The result indicates the ex‐situ MEOR process with B. subtilis can reach a recovery rate of 38.56%, while the in‐situ MEOR process with B. subtilis reached a recovery rate of 40.27%. The proposed study provides a new tool for understanding the MEOR process, with advantages in visibility and accurate fluid control during the MEOR process.
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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 | 0.000 | 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 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".