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Nitrogen Foam Profile Control for Heavy Oil Reservoir

2013· article· en· W1753699342 on OpenAlex
Lin Zhang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnergy science and technology · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringNitrogenOil productionEnhanced oil recoveryEnvironmental scienceOil viscosityViscosityGeologyMaterials scienceChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract In view of the character of heavy oil reservoir in Shengli oilfield under thermal production, the analysis that heterogeneity of oil reservoir, viscosity of crude oil, oil thickness, recovery efficiency of recoverable reserves and distance to the oil-water boundary affect the effectiveness of nitrogen foam profile control was made by reservoir numerical simulation and statistical interpretation of production effect of some wells which had being implemented nitrogen foam profile control. On that basis, a prediction model of nitrogen foam profile control technology was founded by means of the fuzzy comprehensive evaluation, and the reservoir conditions which adapt to nitrogen foam profile are presented. The results The adaptive research of nitrogen foam profile control in steam stimulation reservoir can enhance the pertinence and effectiveness of nitrogen foam profile control technology and improve the efficiency of multi-round steam stimulation in heavy oil reservoir.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.206
Teacher spread0.202 · 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