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Record W4233795567 · doi:10.2523/90532-ms

Integration of Time-Lapse Seismic Data into a Flow Model Study of CO2 Injection into the Weyburn Field

2004· article· en· W4233795567 on OpenAlexaboutno aff
Hirofumi Yamamoto, John R. Fanchi, Tom Davis

Bibliographic record

VenueProceedings of SPE Annual Technical Conference and Exhibition · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringGeologyCarbon sequestrationOil fieldEnhanced oil recoveryInjection wellProcess (computing)Field (mathematics)Reservoir modelingEnvironmental scienceCarbon dioxideComputer science

Abstract

fetched live from OpenAlex

The Reservoir Characterization Project at the Colorado School of Mines has conducted a time-lapse seismic survey in a section of the Weyburn Field, Saskatchewan, Canada. The section is being subjected to a CO2 injection process that began in October 2000. The seismic monitoring process is of interest for both enhanced oil recovery and geologic carbon sequestration. This paper describes a study that integrated both static and dynamic data in a history matched (calibrated) model of CO2 injection performance. An objective function was used to quantify study results.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.378

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)0.0000.000

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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2004
Admission routes1
Has abstractyes

Explore more

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