Spatiotemporal variability of fugitive gas migration emissions around a petroleum well
Bibliographic record
Abstract
Well integrity failure resulting in migration of natural gas outside of the surface casing can cause atmospheric greenhouse gas emissions and groundwater quality impacts from existing and historic energy wells. Spatial and temporal variability in gas migration can result in errors in detection (i.e., presence/absence) and efflux estimations. This field-based case study used automated dynamic closed chambers to record repeated (~ every 18 minutes) CO2 and CH4 efflux measurements over a two-week period around a single petroleum production well in Alberta, Canada. Long-term efflux measurements supplemented soil gas compositional and isotopic characterization, along with surface concentration measurements. Effluxes were spatially concentrated around the wellhead and only occasionally detectable more than a few meters away. Estimated total emissions attributable to gas migration ranged from 48 - 466 g CH4 d-1 (or 0.07 - 0.7 m3 CH4 d-1). Methane effluxes and concentrations were temporally variable on second-to-hourly and diel scales. Multivariate stepwise regression analysis indicates that multiple meteorological factors, particularly wind speed and air temperature, were related to the temporal variability. Despite temporal variability, elevated concentrations and effluxes were consistently detectable around the well. Major soil gas composition suggests that gas migration near the wellhead causes advective displacement of soil gas, while more distal measurements are indicative of episodic and diffusion-dominated transport. Values of 13C-CO2 and 13C-CH4 samples were consistent with CH4¬ oxidation within the unsaturated zone. Although these results reflect a single well, the findings are salient to gas migration detection and emission estimation efforts.
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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 | 0.001 | 0.001 |
| 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 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".