Insights into GHG emissions from faulty oil and gas wells in the Western Canada Sedimentary Basin
Bibliographic record
Abstract
Understanding the source of fugitive methane is key to any mitigation effort. Unwanted emissions from oil and gas wells are significant contributors to greenhouse gas (GHG) emission budgets in petroliferous regions. Here we examine in detail, parameters that may be controlling GHG emission rate of individual, faulty wells in the Western Canada Sedimentary Basin (WCSB). For several hundred wells, we compared the source depth of the leaks determined by isotope fingerprinting to publicly available surface casing vent shut-in pressures and gas emission flow rates in three different oil and gas fields of WCSB. About seventy-five percent of the leaks are from shallower and intermediate formations rather than the targeted formations in most areas. The depth of leaks does not vary between horizontal and vertical wells in a given region. The source depth of the leaking gas is not correlated with the age of the well. Most of the leaks in a region come from specific gas-charged intermediate formations. We observe that smaller leaks come from both the shallower intermediate and the target zones. Surprisingly, the higher shut-in pressure and larger surface casing flows tend to come from shallower depths. In these cases, it was observed that the drillers had used comparatively less cement. There are many thousands of faulty wells in the WCSB, and our observations can guide the prioritization of remediation to most quickly and economically reduce GHG 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".