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Record W7162125100 · doi:10.82308/8575

Abandoned oil and gas wells in Western Canada: methane measurements and emission estimates

2024· dissertation· en· W7162125100 on OpenAlexaboutno aff
Lauren Bowman

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasMethaneFugitive emissionsClimate changeFossil fuelGlobal warmingMethane emissionsCarbon dioxide

Abstract

fetched live from OpenAlex

Emissions of greenhouse gases such as carbon dioxide, methane and nitrous oxide from human activities contribute to climate change and global mean surface temperature warming. Reductions in near-term climate forcers such as methane, which has a global warming potential 25 times more potent than carbon dioxide on a 100-year time frame, are crucial to limit further global temperature rises in the near term and allow time for long term and large-scale strategies to come into effect. In Canada and the U.S., the oil and gas sector contribute to 41% and 31% respectively of methane emissions annually but has the highest potential for technologically feasible reduction in the short term compared to other sectors such as waste or agriculture. Accurate quantification of methane emissions across all sectors, including oil and gas, are needed to inform national inventories, regulations and reduction strategies that are key to mitigating climate change. Methane leakage from abandoned oil and gas wells not only contributes to methane emissions from the oil and gas sector, but also poses a risk to groundwater through subsurface leakage caused by well integrity issues. Despite this, the number of methane emission measurements from abandoned oil and gas wells is small compared to the total population across U.S. and Canada. Additionally, many provinces and states with current and previous history of oil and gas development still have no available direct point-source based measurements, including Canada’s largest oil and gas producing provinces of Alberta and Saskatchewan. Furthermore, existing measurements do not differentiate between emissions from aboveground well infrastructure leaks and emissions from surface casing vent flows, an indicator of subsurface leakage. We conducted chamber-based methane emission measurements of 238 abandoned oil and gas wells across Alberta and Saskatchewan, Canada. We separately quantified emissions from surface casing vents and other emissions from the wellhead infrastructure (including near well gas migration) to develop component-specific emission factors. By combining our measurement-based emission factors with publicly available datasets on abandoned oil and gas wells, we estimated Canada-wide emissions from abandoned wells including the contribution of emissions from surface casing vent flows associated with subsurface leakage. From our measurements we estimated methane emissions from abandoned wells in Canada to be 85-95 kilotonnes of methane per year, of which surface casing vent emissions represent 75-82% (70 kilotonnes of methane per year). Within our sample set we also measured a super high emitter with a methane emission rate of (5.2x106 mg CH4/h), two to three times higher than the largest previously published measurement from an abandoned oil and gas well. By comparing the occurrence of surface casing vent flows within our sample set to two previous studies based on provincial datasets we found that subsurface leaks are three to five higher than previously estimated. We conclude that subsurface leakage is a major contributor to methane emissions from abandoned oil and gas wells and that additional point-source and component-based measurements are needed to accurately quantify emissions and determine the prevalence of well integrity issues in abandoned and active well populations. Moreover, the impact of well attributes on methane leakage and temporal variability of emissions from abandoned oil and gas wells also need further investigation. Comprehensive studies at oil and gas wells that combine methane emissions measurements with investigations of other environmental impacts such as groundwater contamination are needed to create mitigation strategies that address emissions and broader environmental impacts

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.218
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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