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Record W4403915360 · doi:10.1016/j.envres.2024.120254

Characterizing multifaceted environmental risks of oil and gas well leakage through soil and well methane and hydrogen sulfide emissions

2024· article· en· W4403915360 on OpenAlexafffundabout
Khalil El Hachem, Christian von Sperber, Charlotte Allard, Dru Heagle, Darian Vyriotes, Ralf M. Staebler, Élyse Caron-Beaudoin, Mary Kang

Bibliographic record

VenueEnvironmental Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of TorontoEnvironment and Climate Change CanadaNatural Resources CanadaMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Resources CanadaEnvironment and Climate Change CanadaNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsHydrogen sulfideMethaneEnvironmental scienceEnvironmental chemistryLeakage (economics)Fossil fuelMethane emissionsHydrogenPetroleumWaste managementChemistrySulfurEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Oil and gas wells (OGWs) can lead to soil and well emissions of methane (CH 4 ), a potent greenhouse gas, and hydrogen sulfide (H 2 S), a highly toxic gas, both of which reduce air quality and can cause explosions when emitted into confined spaces. Developments have been occurring over OGWs, posing health and safety risks. However, to our knowledge, previous studies have not conjunctively analyzed well and soil emissions while considering development on or near OGWs. In this paper, we characterize 343 CH 4 and H 2 S emission rate measurements from 67 non-producing (abandoned) and 35 producing (active) OGWs, including 205 measurements from soils surrounding 81 OGWs in Ontario and Quebec. We also provide the first emission rate estimates from an abandoned water and OGW-linked explosion and map OGWs in urban and built-up areas in Ontario and Quebec. We estimate the explosion-linked emissions to be 3,000 g CH 4 /hour and 7 g H 2 S/hour. Moreover, we find that 7,264 and 161 OGWs in Ontario and Quebec, respectively, are in urban and built-up areas, with 94% of these wells being abandoned. For the 102 wells we measured, of which 9.7% had H 2 S detections, we find OGW emission rate ranges of −16 to 47,000 mg CH 4 /hour and 0.001 to 3,300 mg H 2 S/hour. Although soil CH 4 emissions at a 1-m distance from the wells are most correlated with well emissions, the highest soil emission rate was observed at a 3-m distance, indicating the potential for OGW-related emissions into buildings to occur away from the well. Overall, our multi-faceted measurement dataset provides a basis for conjunctive analysis of the broad range of environmental risks of OGWs to climate, indoor and outdoor air quality, and explosions. • CH 4 and H 2 S emissions associated with the explosion exceed the highest previously measured emissions from wells in Ontario. • H 2 S emissions associated with the explosion exceed any previously measured H 2 S emissions from active and abandoned wells. • 7,264 wells in Ontario and 161 wells in Quebec overlap urban and built-up areas, increasing safety risks if they emit. • CH 4 and H 2 S emissions are strongly correlated, and high H 2 S emitting wells are high CH 4 emitters. • Soil CH 4 emissions average 90 mg/h and are dominated by one well emitting at a rate of 4,900 mg/h.

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 score0.693
Threshold uncertainty score0.618

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.036
GPT teacher head0.308
Teacher spread0.272 · 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

Citations10
Published2024
Admission routes3
Has abstractyes

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