Analysis of Microbes in Hydraulic Fracturing of Montney Tight Gas Formations in Western Canada
Bibliographic record
Abstract
Abstract Microbes in unconventional gas operations can conceivably contribute to (i) degradation of polymers in hydraulic fracturing fluid, (ii) well plugging, limiting gas flow and (iii) souring and corrosion due to activity of sulfate reducing bacteria (SRB). Flowback waters from a tight gas field in Northern British Columbia were distinct from the fresh water, used for making hydraulic fracturing fluid, by their high salinity, low pH and high ammonium concentrations. Microbial counts of SRB and of acid-producing bacteria (APB) for these flowback waters were significantly lower than for the fresh water used to make the hydraulic fracturing fluid. Determining microbial community compositions by sequencing the 16S rRNA genes in the samples did not indicate the presence of microbes preferring high salt or high temperature conditions in samples of flowback water. These data indicate that these tight gas formations are sterile and that the microbes that are being introduced with the hydraulic fracturing fluid do not thrive downhole. Hence, microbes downhole are unlikely to contribute to well plugging, limiting gas flow. Microbes at or near the surface may cause polymer degradation in hydraulic fracturing fluid, especially when guar gum is used in gel-based fracturing treatments, and may cause souring in the fresh water, used for making hydraulic fracturing fluid, if this contains a significant concentration of sulfate.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".