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Record W2331965473 · doi:10.3997/2214-4609.201412502

Polymer Screening for the Hebron Field, Offshore Eastern Canada - Facing High Salinity Brines

2015· article· en· W2331965473 on OpenAlexaffabout
Luis E. Valencia, Laura James, Karem Azmy, John Walsh

Bibliographic record

VenueProceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSalinityBrineSeawaterViscositySubmarine pipelineChemistryEnvironmental scienceGeologyMaterials scienceOceanographyComposite material

Abstract

fetched live from OpenAlex

Summary The Hebron Project is the fourth major offshore development in the province of Newfoundland and Labrador, with an estimated 2620 MBO in place and 800 MBO recoverable. Hebron Field oil and reservoir properties are similar to previous successful offshore polymer flooding projects. However, the formation water salinity, which is greater than 60,000 ppm, is higher than offshore field analogues used for the EOR screening. This paper reports viscosity variations due to salinity and temperature changes observed in two commercial, partially-hydrolyzed polyacrylamides, FP-3430S and FP-5115, and the biopolymer Guar Gum, using offshore Eastern Canada seawater and synthetic formation water brine. FP-3430S and FP-5115 showed similar viscosity responses in relation to salinity and temperature changes compared with Guar Gum, which was more salinity tolerant over the range of salinity investigated, but showed a greater viscosity decrease at salinity values higher than seawater. Guar Gum was also found to be more unstable at temperatures higher than 62°C. FP-3430S showed a higher viscosifying power, requiring less polymer mass to reach the same viscosity values even in different brine salinities. This indicates that FP-3430S is the most suitable for use with the Hebron Field brines, according to the conditions evaluated in this study.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.983

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 head0.023
GPT teacher head0.232
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.

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

Citations1
Published2015
Admission routes2
Has abstractyes

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