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Record W1990736086 · doi:10.2118/97511-ms

Circulating Usage of Partially-Produced Fluid as Power Fluid for Jet Pump in Deep-Heavy-Oil Production

2005· article· en· W1990736086 on OpenAlexaff
S. Chen, Huayu Li, Q. Zhang, Jun He, Daoyong Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsLight crude oilDiluentViscosityGas oil ratioOil productionOil fieldPetroleum engineeringMaterials scienceChemistryComposite materialNuclear chemistryGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Jet pumping driven by light oil is one of the preferred lift methods for producing heavy oil in a deep heavy oil reservoir. Generally, the amount of light oil is too large to be accepted. One solution of reducing the amount of light oil is that partial produced fluid can be combined with the light oil at any reasonable ratio and then the produced fluid-light oil mixture is reinjected into the well as the power fluid. In this case, viscosity of the mixture keeps increasing and eventually reaches its equilibrium value, which is found to be a function of the reservoir oil viscosity, the light oil viscosity (VLO), the ratio of light oil (RLO) in the mixture (volumetric percentage) and the ratio of the well rate to the diluent rate (M ratio). Thus the optimal ratio of light oil in the mixture can be determined by using an iterative algorithm. All of the above-mentioned parameters result in a change of the viscosity of the produced fluid-light oil mixture and the pressure loss in the production string, especially the VLO and the RLO. A field application example shows that the amount of the light oil used can be reduced by more than 50%.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

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