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Record W3097319917 · doi:10.1021/acs.est.0c05279

Methane Emissions from Abandoned Oil and Gas Wells in California

2020· article· en· W3097319917 on OpenAlexaff
Eric D. Lebel, Harmony S. Lu, Lisa Vielstädte, Mary Kang, Peter Banner, M. L. Fischer, Robert B. Jackson

Bibliographic record

VenueEnvironmental Science & Technology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcGill University
FundersNatural Environment Research CouncilSight Research UKChevronGDF SuezU.S. Department of Energy
KeywordsMethaneCasingWater wellMethane emissionsOil wellEnvironmental sciencePlumeNatural gasFossil fuelIdleInjection wellPetroleum engineeringEnvironmental engineeringHydrology (agriculture)GeologyChemistryWaste managementEngineeringMeteorologyGroundwaterGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

California hosts ∼124,000 abandoned and plugged (AP) oil and gas wells, ∼38,000 idle wells, and ∼63,000 active wells, whose methane (CH4) emissions remain largely unquantified at levels below ∼2 kg CH4 h–1. We sampled 121 wells using two methods: a rapid mobile plume integration method (detection ∼0.5 g CH4 h–1) and a more sensitive static flux chamber (detection ∼1 × 10–6 g CH4 h–1). We measured small but detectable methane emissions from 34 of 97 AP wells (mean emission: 0.286 g CH4 h–1). In contrast, we found emissions from 11 of 17 idle wells—which are not currently producing (mean: 35.4 g CH4 h–1)—4 of 6 active wells (mean: 189.7 g CH4 h–1), and one unplugged well—an open casing with no infrastructure present (10.9 g CH4 h–1). Our results support previous findings that emissions from plugged wells are low but are more substantial from idle wells. In addition, our smaller sample of active wells suggests that their reported emissions are consistent with previous studies and deserve further attention. Due to limited access, we could not measure wells in most major active oil and gas fields in California; therefore, we recommend additional data collection from all types of wells but especially active and idle wells.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.189
Teacher spread0.185 · 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; both teacher heads agree on what is shown here.

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

Citations83
Published2020
Admission routes1
Has abstractyes

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