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Record W2045801147 · doi:10.1080/13895260008953328

Control of methane emission from heavy oil wells by gas clustering and utilization

2000· article· en· W2045801147 on OpenAlexaffabout
Min Yang, Anil K. Mehrotra, J. Patrick A. Hettiaratchi

Bibliographic record

VenueInternational Journal of Surface Mining Reclamation and Environment · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCasingMethaneEnvironmental sciencePetroleum engineeringFossil fuelMethane gasMethane emissionsCurrent (fluid)Environmental engineeringWaste managementEngineeringChemistry

Abstract

fetched live from OpenAlex

ABSTRACT A study was undertaken to investigate the means of controlling casing gas (methane) emissions from heavy oil wells in the Lloydminster area in Canada. The study was aimed at providing a better understanding of the current casing gas methane emissions in the area and evaluating the potential for reducing these emissions by alternative technologies. Current casing gas emissions in the area were evaluated based on the best available information, and a database was established. Short-term trends of casing gas availability were forecasted. A Geographic Information System (GIS) was employed to simulate a multi-level gas clustering process. An optimization model was developed to evaluate potential technology applications and to find the least-cost solutions for reducing methane emissions in the study area.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations2
Published2000
Admission routes2
Has abstractyes

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