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Record W2079671904 · doi:10.2118/144281-ms

Biology Enzyme EOR for Low Permeability Reservoirs

2011· article· en· W2079671904 on OpenAlexaff
He Liu, Zhonghong Zhang

Bibliographic record

VenueSPE Enhanced Oil Recovery Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsPetroleum engineeringEnhanced oil recoveryOil productionSurface tensionVolume (thermodynamics)Permeability (electromagnetism)Oil wellPressure dropMicrobial enhanced oil recoveryDrop (telecommunication)Environmental scienceChemistryGeologyBiochemistryMicroorganismThermodynamicsEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Biology enzyme may change wettablity of rocks, release surface hydrocarbon of formation rock particles, and reduce interfacial tension. These properties may be used to reduce water injection pressure of low-permeability oil reservoirs, to enlarge swept volume and enhance recovery rate. We applied 4 types of biology enzyme solutions with different concentrations ranging from 0.5% to 5.0%, to conduct depressurization experiments on 6 artificial cores and 3 natural cores. When concentration of biology enzyme was 2.0%, injection pressure dropped significantly by 22.6% to 72.7%, averagely a drop of 51.4%. Pilot tests have been carried out based on indoor experiment results, as well as economic factors and operating convenience considerations, and other oilfield experiment parameters references. When volume multiple of injected biology enzyme was chosen to be 0.6%PV, production declination of connected oil wells was controlled and production rose gradually, showing that formation pressure has been restored and biology enzyme has played a part in enhancing the oil recovery rate.

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 score0.001
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 communication0.0000.001
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 head0.031
GPT teacher head0.253
Teacher spread0.221 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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