Biocide inhibition of microbial sulfur reduction in fracing fluids
Bibliographic record
Abstract
Fracing technology has revolutionized the natural gas industry, and currently, it is the most widely used method to extract gas from shale in Western Canada. Microbial activity in fracing fluids can lead to biofouling, corrosion, and gas souring. Biocides are commonly applied to inhibit microbial activity, but in many cases biocide application is partly or even wholly ineffective. This is, in part, because biocides are rarely tested using real environmental communities relevant to fracing systems. To address this problem, I investigated the efficacy of glutaraldehyde, which is one of most commonly used biocides to control microbial activity, on microbial sulfur reduction in fracing fluids. To do this, I collected fracing fluids from the shale gas play in the Fort St. John area of northern British Columbia, Canada. In the lab, I conducted incubation experiments by amending fracing fluids with glutaraldehyde and yeast extract and incubating these fluids for 30 days at room temperature. During the incubation, I measured sulfide and sulfate concentrations to track rates of microbial sulfur metabolisms with and without glutaraldehyde and yeast extract amendments. To link these results to the relevant microbial taxa, I determined the microbial community present in the incubated fluids using 16S rRNA gene amplicon sequencing. Overall, I found that glutaraldehyde is only moderately effective in controlling microbial sulfide production in fracing fluids and that even in the presence of glutaraldehyde, amendment with reactive organic matter stimulates sulfide production.
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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) | 0.000 | 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 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".