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Record W4312217144 · doi:10.29169/1927-5129.2022.18.14

Experimental Study on Early Polymer Injection Timing of Heavy Oil Reservoir in Bohai Sea

2022· article· en· W4312217144 on OpenAlexvenueno aff
Shijie Zhu, Xue Zeng, Rui Wang, Lei Fu, Zhiyuan Tu, Jiachun Su, Leiting Shi

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2022
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersChongqing University of Science and TechnologyNatural Science Foundation of ChongqingChongqing University
KeywordsPetroleum engineeringSubmarine pipelinePolymerEnhanced oil recoveryWater injection (oil production)Injection wellWater floodingFlooding (psychology)Environmental sciencePetroleum reservoirMaterials scienceGeologyGeotechnical engineeringComposite material

Abstract

fetched live from OpenAlex

The polymer flooding of ordinary heavy oil reservoirs in Bohai Sea can improve the crude oil recovery by advancing the injection time of polymer flooding. The better the injection time is, the higher the enhanced recovery is, and the greater the income is. Based on Bohai Oilfield, the polymer application system was characterized in the laboratory, and then polymer flooding experiments were carried out at different times using one-dimensional core model. The results show that: 1) polymer AP-P4 can establish good mobility control ability (RF=107, RRF=28.5) under the target reservoir conditions; 2) Under the experimental conditions, the best time for polymer injection is to switch to polymer injection after 0.203PV water injection, and the oil recovery can be increased by 27.73%. Early polymer injection technology is very beneficial to polymer flooding in offshore oil fields.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.281
Teacher spread0.256 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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