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Studies on the Stability of the Foamy Oil in Developing Heavy Oil Reservoirs

2017· article· en· W2590493674 on OpenAlexaff
Bashir Busahmin, Brij Maini, Rama Rao Karri, Maziyar Sabet

Bibliographic record

VenueDefect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forum · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsViscosityPetroleum engineeringVolume (thermodynamics)Oil viscosityAsphalteneGas oil ratioMaterials scienceEnhanced oil recoveryHigh pressureChromatographyChemistryComposite materialThermodynamicsGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

In the process of natural energy depletion, foamy oil is characterized of low production Gas Oil Ratio, high oil viscosity, high daily production rate and high primary recovery factor. The stability of the foam turns out to be the prevailing factor that governs the life of the ‘foamy oil’. To enumerate the main factors affecting the stability of the foam, a high-temperature–high-pressure visualized experiment model for foamy oil stability test was developed. A serial of experiments was conducted to evaluate the performance of the foam stability. The effects of oil viscosity, height of the oil column, dissolved gas content and dispersed gas were investigated and recorded. These experiments were conducted using a Hele-Shaw, a high pressure cell. The volume of foamy oil produced, either by a step reduction in pressure or by a gradual (linear) reduction in pressure, and its subsequent decay was observed, visually. The experimental results show that foamy oil stability increases with higher oil viscosity, higher oil column, higher dissolved gas content and higher pressure decline rate. Asphaltene content was not observed to increase the foamy oil stability significantly. The results also show that the foam quality of foamy oils is much lower than aqueous foams.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.009
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 head0.060
GPT teacher head0.314
Teacher spread0.253 · 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 designNot applicable
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

Citations37
Published2017
Admission routes1
Has abstractyes

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