A Techno-Economic Analysis of Methane Mitigation Potential from Reported Venting at Oil Production Sites in Alberta
Bibliographic record
Abstract
Abstract The technical and economic potential for reducing methane emissions from reported venting and flaring volumes in 2015 at 9422 upstream oil production sites in Alberta, Canada was evaluated in a comprehensive site-by-site analysis. For each site, up to six different technologies for mitigation were considered, based on conserving gas into pipelines, combusting gas on site, or using gas for on-site fuel. Economic viability of mitigation was calculated using current economic parameters and gas price projections on a net present cost basis. Monte Carlo simulations suggest that a 45% reduction in methane emissions (consistent with current federal and provincial targets) from reported flaring and venting is technically and economically feasible at overall average costs ranging from $–2.98 CAD/tCO2e (i.e., a profit) to $2.51 CAD/tCO2e with no one site paying more than $11.02 CAD/tCO2e. If the reported baseline emissions are augmented to reflect results of recent airborne measurements, overall economics of mitigation generally improve due to larger available gas volumes at many sites. Considering federal carbon price targets of $50 CAD/tCO2e by 2022, there are relevant economic opportunities for mitigating methane from reported venting and flaring volumes well beyond a 45% reduction. This could partially offset the challenge in addressing the additional methane emissions from fugitive and unreported venting sources.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".