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Record W2062089713 · doi:10.2118/154518-ms

Design and Development of Aqueous Colloidal Gas Aphrons for Enhanced Oil Recovery Applications

2012· article· en· W2062089713 on OpenAlexaff
Shivana R. Samuel, Ergün Kuru, Japan Trivedi

Bibliographic record

VenueSPE Improved Oil Recovery Symposium · 2012
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPulmonary surfactantEnhanced oil recoveryAqueous solutionPolymerChemical engineeringMaterials scienceBubbleColloidResidual oilRheologyChromatographyChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The problems associated with current chemical flooding technologies are based around inadequate sweep efficiencies and unfavorable mobility ratios which leave much of the recoverable oil left untouched in the pores of the reservoir. In order to address the low sweep efficiency and unfavorable mobility ratio issues, numerous formulations of polymer and surfactant base fluids have been used for enhanced oil recovery (EOR) applications with varying degree of success. The use of Colloidal Gas Aphrons (CGA) as an alternative chemical EOR technique is investigated in this study. Colloidal Gas Aphrons (CGA) are described as micro-bubbles which are 10 to 100 microns in size with a gas containing inner core encapsulated by a thin surfactant film. Aqueous CGA fluids are comprised of water, polymer and surfactant solutions. An experimental study was conducted to determine the optimum surfactant and polymer concentrations which would yield stable micro-bubbles. The formulations of stable micro-bubbles were analyzed in terms of rheology, bubble size distribution and time stability. In order to determine the displacement efficiency of CGA fluid in the EOR process, flooding experiments were conducted using a 2D linear model and 3D radial model, both packed with glass beads and saturated with mineral oil. Flooding experiments were performed using a) water, b) aqueous polymer solution, c) aqueous polymer and surfactant solution mixed at low shear rate, d) CGA fluid, e) water followed by CGA fluid, and f) water followed by polymer solution. Efficiency of oil recovery using the CGA fluid was compared to that of other fluids. All experiments were repeated to ensure consistent results. Less than 3 % variation in results was observed in all cases. Pressure drop, ultimate recovery and injected fluid retention time data were measured during the flooding experiments. In addition, time-lapse images taken at regular intervals were analyzed to study frontal displacement patterns observed in 2-D experiments. The results indicated that the CGA fluids showed more stable frontal displacement as compared to water flooding. The cumulative oil recovery performance of CGA fluids was comparable but slightly less than that of aqueous polymer solutions. CGA fluids, however, required significantly lower injection pressure as compared to aqueous polymer solutions. The breakthrough time of CGA fluids was longer than that of any of the other fluids tested indicating that CGAs have longer retention time. Results from preliminary experiments encourage the further investigation of colloidal gas aphrons as an alternative EOR technique. The results will also be useful in designing an EOR process as an alternate to polymer, surfactant-polymer or WAG flood with particular importance to carbon sequestration as CO2 / flue gas can also be used in micro-bubble generation in place of air.

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 score0.002

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 communication0.0000.000
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 head0.227
Teacher spread0.216 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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