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Alteration of Interfacial Properties by Chemicals and Nanomaterials To Improve Heavy Oil Recovery at Elevated Temperatures

2017· article· en· W2765700757 on OpenAlexafffundabout
Tayfun Babadagli

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface tensionWettingContact angleImbibitionNanofluidMaterials sciencePulmonary surfactantChemical engineeringEnhanced oil recoveryComposite materialNanotechnologyNanoparticleThermodynamics

Abstract

fetched live from OpenAlex

Heavy oil containing carbonate and sand reservoirs exhibits reverse wettability characteristics. Dependent upon the temperature, phase of injected steam, and rock type, the wettability may be altered to more water-wet. The addition of chemicals to hot water (or steam) may further change the interfacial properties (more water-wet and less interfacial tension). Surfactants were tested extensively for this process in the past, and their temperature resistance was an obstacle. New-generation chemicals need further investigation from a technically and economically success point of view. The objective was to investigate the alteration of interfacial properties induced by different types of chemical agents under high-temperature conditions. To achieve this, four experimental tools (contact angle measurement, interfacial tension measurement, atomic force microscopy, and spontaneous imbibition tests) were applied. High-pressure and high-temperature contact angle measurements enabled a quick method to identify the suitability of the chemicals for wettability modification. Interfacial tension between oil and different chemical solutions was measured with a variation of the temperature. In the imbibition tests, core samples were exposed to heating for longer time periods, so that the temperature resistance of the chemicals was also tested. Imbibition experiments were conducted at ambient pressure and 90 °C. The combination of the contact angle and interfacial tension provided insight into the recovery enhancement mechanisms. Six different chemicals, including an ionic liquid, three nanofluids (silica, aluminum, and zirconium oxides), a cationic surfactant, and a high-pH solution, were chosen based on our screening study. Heavy oil used was obtained from a field in Alberta (6000 cP). Contact angles were measured on mica, calcite, sandstone, and limestone plates. The experimental temperature ranged from 25 to 200 °C, and the pressure was changed to keep the solution in the aqueous phase. Promising modifiers for different rock types under different temperatures were screened separately. Visual data illustrating the deposition of the chemicals on the surface of mica and well-polished calcite substrates and removal of the existing oil layer after the treatment with different chemicals were obtained by atomic force microscopy. Finally, spontaneous imbibition tests were performed on sandstone and limestone cores with screened promising modifiers. Oil recovery in this phase was continuously monitored to evaluate wettability alteration capability, and the mechanism(s) involved were analyzed for different chemicals. Analysis of wettability alteration mechanisms and interfacial tension reduction capabilities is expected to be useful in the selection of suitable and temperature-resistant chemicals for high-temperature applications in different reservoir rocks.

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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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

Citations13
Published2017
Admission routes3
Has abstractyes

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