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Record W1817939996 · doi:10.3968/5348

Studies on the Scaling of High Pressure and Low Permeability Oil Reservoir Water Injection Well

2014· article· en· W1817939996 on OpenAlexvenueno aff
Zhaomin Li, Dingyong Zhang, Guoshun Qin, Longjiang Guo, Wei Li

Bibliographic record

VenueAdvances in petroleum exploration and development · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsScalingCalcium carbonatePermeability (electromagnetism)CarbonatePetroleum engineeringWater injection (oil production)ChemistryIonCalciumMaterials scienceGeologyMembraneOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Injection wells scaling and scale inhibitor were studied in this paper, in consideration of the severe scaling problem of a high pressure injection wells in low permeability reservoirs. The result shows that the injected water contains some scale ions, such as carbonate calcium and magnesium ions. It also demonstrates that the main component of the scale is calcium carbonate, while the scale at bottom hole contains more silicon dioxide. Besides, the scaling of water injection wells mainly appear in the lower parts of the well and its thickness along the well increases rapidly. The indoor experiments of anti-scaling agent indicate that the anti-scaling agent is of good performance in scale prevention and its best concentration is 15 mg/L. In addition, it is also found that the scale inhibiting efficiency of scale inhibitor 2 is higher when the injected water is at low temperature, while scale inhibitor 1 shows better performance when the temperature of injected water is over 75 ℃. Key words : High pressure and low permeability oil reservoir; Water injection well; Scaling; Scale inhibitor

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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 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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in petroleum exploration and developmentSame topicCalcium Carbonate Crystallization and InhibitionFrench-language works237,207