Emission Factors for Hydraulically Fractured Gas Wells Derived Using Well- and Battery-level Reported Data for Alberta, Canada
Bibliographic record
Abstract
A comprehensive technical analysis of available industry-reported well activity and production data for Alberta in 2011 has been used to derive flaring, venting, and diesel combustion greenhouse gas and criteria air contaminant emission factors specifically linked to drilling, completion, and operation of hydraulically fractured natural gas wells. Analysis revealed that in-line ("green") completions were used at approximately 53% of wells completed in 2011, and in other cases the majority (99.5%) of flowback gases were flared rather than vented. Comparisons with limited analogous data available in the literature revealed that reported total flared and vented natural gas volumes attributable to tight gas well-completions were ∼ 6 times larger than Canadian Association of Petroleum Producers (CAPP) estimates for natural gas well-completion based on wells ca. 2000, but 62% less than an equivalent emission factor that can be derived from U.S. EPA data. Newly derived emission factors for diesel combustion during well drilling and completion are thought to be among the first such data available in the open literature, where drilling-related emissions for tight gas wells drilled in Alberta in 2011 were found to have increased by a factor of 2.8 relative to a typical well drilled in Canada in 2000 due to increased drilling lengths. From well-by-well analysis of production phase flared, vented, and fuel usage natural gas volumes reported at 3846 operating tight gas wells in 2011, operational emission factors were developed. Overall results highlight the importance of operational phase GHG emissions at upstream well sites (including on-site natural gas fuel use), and the critical levels of uncertainty in current estimates of liquid unloading emissions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".