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Record W4321996056 · doi:10.5194/egusphere-egu23-10636

A proposal to use “Isoscapes” of fugitive gases from oil and gas wells to facilitate the reduction and attribution of methane emissions and plugging of faulty wells

2023· preprint· en· W4321996056 on OpenAlexaffabout
Gabriela González Arismendi, Karlis Muehlenbachs

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsGreenhouse gasMethaneEnvironmental scienceFossil fuelNatural gasEnvironmental engineeringPetroleum engineeringChemistryWaste managementGeologyEngineering

Abstract

fetched live from OpenAlex

Canada is the third largest oil and gas producer, with over 500,000 active and inactive wells, mostly located in the Western Canada Sedimentary Basin (WCSB). A large, undetermined fraction of Canada’s GHG emissions emanate from oil and gas infrastructure. Governments and industry are all committed to immediately reducing methane leaks to the atmosphere from surface casing vent flows (SCVF) and ground migration (GM) of both new and old wells. Methane carbon isotopic composition offers insight into the source of unwanted gas emissions. A geospatial tool would help to attribute and reduce GHG emissions from contour maps of ẟ13C of methane and other hydrocarbons of production, SCVF, and GM gases across the WCSB. These “Isoscapes” of production gases vary systematically, reflecting the local geology. SCVF and GM isoscapes are offset from the production ones because the SCVF most often are shallower than the target formations, and the GM gas may be oxidized in soils. The difference between the production and SCVF isoscapes can be used to attribute methane emissions from tanks and production infrastructure, compared to leaks from the wells themselves. The isoscapes directly facilitate the plugging of problem wells. The maps are based on over 3,000 locations where we used isotope fingerprinting (i.e., Rowe & Muehlenbachs, 1999) to identify the source depth of a leak. Regulatory measurements mandate that the leaks are sealed at their source depth, greatly adding to the cost of plugging any well. The SCVF isoscapes suggest the likely source depth of an unsampled leaking well, thus greatly simplifying its remediation. Applying such information beyond a local case study may contribute to accounting for the GH contribution from regional oil and gas activities in Canada and elsewhere. ReferenceRowe, D., & Muehlenbachs, A. (1999). Low-temperature thermal generation of hydrocarbon gases in shallow shales. Nature, 398(6722), 61­-63.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0020.003
Scholarly communication0.0050.004
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.003

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 head0.035
GPT teacher head0.246
Teacher spread0.211 · 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 designTheoretical or conceptual
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
Published2023
Admission routes2
Has abstractyes

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