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Feasibility Study of Secondary Polymer Flooding in Henan Oilfield, China

2010· article· en· W1935792501 on OpenAlexvenueno aff
Feng-lan Zhao, Jirui Hou, Hongsheng Li, Lü Jing

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPolymerPetroleum engineeringFlooding (psychology)Water cutPolymer solutionMaterials scienceEnvironmental scienceEnvironmental engineeringChemical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

After polymer flooding, it is necessary to find relay technology to retain oil yield. In this paper, laboratory experiments were conducted to investigate feasibility of secondary polymer flooding, in which injecting more polymer with higher concentration and relative molecular mass. It is necessary to determine enhanced recovery range and optimum concentration. With microscopic visible glass physical models, further start up of oil drops with secondary polymer can be observed distinctly. In the mean time, macroscopic heterogeneous core tests were carried out with permeability range of 2, 5 and 8. Polymer concentration effective and economical for flooding is optimized. Ii is shown that 3%~8% of further enhanced recovery and 20% decreased water cut can be obtained, and water profile can be improved to some extend after secondary polymer flooding. Thence, it is proved that secondary polymer injection after primary polymer can indeed further improve recovery and the technology of secondary flooding is feasible. Moreover, laboratory optimum concentration of 2200mg/L is determined. On the basis of laboratory results, from 2007 to 2008, filed trial with above optimum parameters were implemented. Up to 2008.12, water cut decreased from 92% to 83%, and cumulative increased crude oil of 5.71×104t.The success of secondary polymer flooding technology provides reference for the development of oil fields after primary polymer flooding in China and other regions in the whole world. Key words: secondary polymer flooding; feasibility study; microscopic mechanism; polymer concentration optimization; after polymer flooding

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.007
Science and technology studies0.0000.002
Scholarly communication0.0000.009
Open science0.0020.000
Research integrity0.0000.002
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 head0.006
GPT teacher head0.298
Teacher spread0.293 · 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.

Study designObservational
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
Published2010
Admission routes1
Has abstractyes

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