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Record W2058525969 · doi:10.2118/89385-ms

Field Chemical Flood Performance Comparison with Laboratory Displacement in Reservoir Core

2004· article· en· W2058525969 on OpenAlexaboutno aff
Kon Wyatt, Malcolm Pitts, H. Surkalo

Bibliographic record

VenueSPE/DOE Symposium on Improved Oil Recovery · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringEnhanced oil recoveryFlood mythOil fieldOil in placeWell stimulationOil wellGeologyDisplacement (psychology)Oil productionPetroleum reservoirReservoir engineeringEnvironmental scienceGeotechnical engineeringPetroleumArchaeology

Abstract

fetched live from OpenAlex

Abstract Field oil recovery performance of a mobility control chemical flood can be predicted by laboratory displacement tests. Four variations comparing field results with laboratory coreflood results are: 1.) A large polymer-flood pilot in S.W. Saskatchewan involving 5 injection wells and 13 production wells. 2.) An alkaline-polymer flood in Alberta with 25 wells of which 7 were injectors. 3.) A secondary application of alkaline-surfactant-polymer in a 6 production well, 1 injection well Wyoming reservoir. 4.) An alkaline-surfactant-polymer flood of 4 inverted 5-spots in a waterflooded Chinese field. The comparison of laboratory and field results is based on oil cut and oil recovery performance. Results of comparisons suggest that radial corefloods physically simulate oil recovery process in the field when properly scaled.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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 designBench or experimental
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

Citations12
Published2004
Admission routes1
Has abstractyes

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