Methane and Environmental Impacts of Abandoned Oil And Gas Wells in the North American Arctic-Boreal Region
Bibliographic record
Abstract
Arctic and Boreal regions are experiencing major natural and anthropogenic disturbances, leading to significant changes in ecosystem composition, structure and functioning in recent decades. Therefore, it is crucial to understand the main drivers of change, as well as the ecosystem impacts on natural and cultural resources, human health and the climate system. Large numbers of oil and natural gas wells are being drilled in Arctic and Boreal regions; however, the number and distribution of wells drilled in these regions over time is not well documented and understood. Moreover methane emissions and relationship with land cover and land cover change have not been analyzed. Using oil and gas well databases from provincial, territorial and state agencies in Canada and the U.S., we analyze drilled oil and gas wells throughout the study period (1984-2014) and in relation to land cover distribution and change across the Arctic-Boreal region of western North America. We find 254,998 wells, mostly located in Alberta (211,747) and British Columbia (35,012), in Arctic and Boreal regions of Canada and the U.S. We characterize the wells, based on data provided in the database, according to well production type (gas or oil and gas) and well abandonment status (active, abandoned, abandoned and plugged) and find that annual well drilling has increased from 269 to 8599 from 1984 to 2014. We estimate emissions from abandoned oil and gas wells in the study domain to be 40 – 148% of Environment and Climate Change Canada’s national estimate for methane emissions from abandoned oil and gas wells in 2018. Finally, using the annual land cover maps for 1984-2014, we find the number of drilled wells in each land cover class throughout the years. We identify significant increases in number of wells drilled between 1984-1999 and 2000-2014 in evergreen forest, sparsely vegetated and barren land cover classes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".