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Record W2794893004 · doi:10.2118/190320-ms

Suppressing Frontal Instabilities and Stabilizing Miscible Displacements with Time-Dependent Rates for Improved Oil Recovery

2018· article· en· W2794893004 on OpenAlexafffund
Qingwang Yuan, Shuoshi Wang, Jinjie Wang, Fanhua Zeng, Kelvin D. Knorr, Muhammad Imran

Bibliographic record

VenueSPE Improved Oil Recovery Conference · 2018
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)University of Regina
FundersMitacs
KeywordsMechanicsViscosityVolumetric flow rateEnhanced oil recoveryMixing (physics)Materials scienceOil fieldDisplacement (psychology)Petroleum engineeringWater injection (oil production)Front (military)Flow (mathematics)ThermodynamicsEnvironmental scienceGeologyPhysicsComposite materialMeteorology

Abstract

fetched live from OpenAlex

Abstract The frontal instabilities are a key control factor which can significantly affect the sweep efficiency and oil recovery in miscible flooding processes. Under unfavorable viscosity ratio between injection solvent and oil, the frontal instabilities are nearly unavoidable. However, how to suppress the instabilities, especially with low additional costs, should be carefully investigated. The present study examines the time-dependent displacement rates on flow instabilities in miscible flooding. Within the capacity of injection pumps, the injection rates are varied with time in a fast alternating manner. It is found that this kind of variable rates can stabilizing frontal instabilities by enhancing initial uniform mixing of solvent and oil. It therefore suppresses the later development of instabilities. Eventually, a much less unstable front is obtained when compared with the constant injection rate. Other parameters such as the amplitude of rates are also examined. The variations of propagation of front with time are analyzed for the change of rate strength. It is can therefore be concluded that this kind of time-dependent rate can improve oil recovery at very low additional rate within the capacity of pumps for the field EOR processes.

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 categoriesMeta-epidemiology (narrow)
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 score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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 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
Published2018
Admission routes2
Has abstractyes

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