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Record W2977431836 · doi:10.1038/s41598-019-50426-3

Barometric-pumping controls fugitive gas emissions from a vadose zone natural gas release

2019· article· en· W2977431836 on OpenAlexafffund
Olenka N. Forde, Aaron G. Cahill, Roger Beckie, K. Ulrich Mayer

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of British Columbia
FundersH2020 European Research CouncilNatural Resources CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaGovernment of Canada
KeywordsFugitive emissionsVadose zoneEnvironmental scienceGreenhouse gasNatural gasAquiferSoil gasPetroleum engineeringFossil fuelWater tableContaminationEnvironmental engineeringHydrology (agriculture)GroundwaterSoil waterSoil scienceGeologyWaste managementGeotechnical engineering

Abstract

fetched live from OpenAlex

Subsurface natural gas release from leaking oil and gas wells is a major environmental concern. Gas migration can cause aquifer contamination, explosive conditions in soil gas, and greenhouse gas emissions. Gas migration is controlled by complex interacting processes, thus constraining the distribution and magnitude of "fugitive gas" emissions remains a challenge. We simulated wellbore leakage in the vadose zone through a controlled release experiment and demonstrate that fugitive gas emissions can be directly influenced by barometric pressure changes. Decreases in barometric-pressure led to surface gas breakthroughs (>20-fold increase in <24 hours), even in the presence of low-permeability surficial soils. Current monitoring strategies do not consider the effect of barometric pressure changes on gas migration and may not provide adequate estimates of fugitive gas emissions. Frequent or continuous monitoring is needed to accurately detect and quantify fugitive gas emissions at oil and gas sites with a deep water table.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations71
Published2019
Admission routes2
Has abstractyes

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