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Record W2467651808 · doi:10.3968/8364

Numerical Simulation Study on the Technology of Different Nature Polymer Injection in Thin and Bad ReservoirNumerical Simulation Study on the Technology of Different Nature Polymer Injection in Thin and Bad Reservoir

2016· article· en· W2467651808 on OpenAlexvenueno aff
Wenlong Zhang, Liyan Sun, Xiaotong Zhang, Jian Wang

Bibliographic record

VenueAdvances in petroleum exploration and development · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringWater cutComputer simulationPolymerStage (stratigraphy)Enhanced oil recoveryEclipseReservoir simulationOil productionMaterials scienceEnvironmental scienceChemical engineeringGeologySimulationEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

The thin and poor reservoir , as an important replacement of production decline in the later stage of oilfield development, is gradually becoming the target of three oil recovery due to the large proportion of its reserves. For Class Ⅲ and Class Ⅳ of thin and poor reservoirs with large contradiction between the layers, if the same molecular weight polymer flooding is used to drive oil, it is easy to affect the development effect because of low producing degree of poorly matched reservoir. On the basis of the fine geological model, the water cut and cumulative oil production of the simulated area were fitted by the simulation software of eclipse, and  the development effect of different production schemes is analyzed and forecasted. Research results show that the effect of different layer different nature polymer injection is better than that of general polymer injection, and the effect of different stage different nature polymer injection is better than that of the different layer different nature.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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