Spatiotemporal variability in field characterization of gas migration around energy wells
Bibliographic record
Abstract
Fugitive methane gas migration outside the surface casing (GM) presents potential concerns for atmospheric greenhouse gas emissions, risk of explosion or asphyxiation, and groundwater quality impacts from the energy industry. The surface expression of gas migration, measured as an elevated concentration or efflux of natural gas (mostly methane), is relied upon for detecting well integrity failures resulting in GM. These measurements are complicated by spatiotemporal variation. This thesis presents two related field studies that investigate the presence and cause of this spatiotemporal variability in the surface expression of gas migration, and discuss the effects this may have on detection and impact assessment of wells with GM. Firstly, the issue of variable gas migration test results is demonstrated by comparing historic GM tests conducted by multiple industry parties across six wells over 18 years. Testing method comparison indicated that GM detection is affected by measurement depth and spatially heterogeneous distributions of migrating gas at the well pad scale. Next, field investigation at a case-study well in East-Central Alberta characterized the spatiotemporal variation in GM using repeated soil gas efflux measurements in combination with meteorological and soil parameters. The observed methane concentrations and effluxes were focused along preferential flow pathways within one meter of the wellhead, with temporal variation at second, hourly, and daily time scales. Methane efflux and concentrations were negatively correlated with wind speeds and air temperature. Total well pad scale emissions attributed to GM were estimated to be 350 g CH4 d-1 (or 0.5 m3 d-1). Subsequent high-resolution CH4 concentration measurements recorded temporal variability, both at ground surface and in the soil. Magnitude-order variations in effluxes and surface concentrations indicate potential error when using snapshot measurements to determine GM emissions or conducting risk assessments. Together, these findings indicate that at this case study site, the presence of GM was reliably detected with established methods. Detection was improved with repeated measurements, greater in soil depths, or more sensitive detectors. Quantification of risks, emission rates, and tracking of temporal trends was only reliably assessed by long-term measurement. Consideration of findings may improve methods used in commercial measurement and future scientific study.
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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) | 0.002 | 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".