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Improved Mobility of Magnetite Nanoparticles at High Salinity with Polymers and Surfactants

2016· article· en· W2286565577 on OpenAlexaboutno aff
Anthony A. Kmetz, Matthew Becker, Bonnie A. Lyon, Edward L. Foster, Zheng Xue, Keith P. Johnston, Linda M. Abriola, Kurt D. Pennell

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of HealthHarvard UniversityNational Science Foundation
KeywordsBrineAqueous solutionPolymerEffluentNanoparticleChemistryChromatographyPulmonary surfactantOil in placeChemical engineeringNuclear chemistryPetroleumOrganic chemistryEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Engineered nanoparticles have been proposed for use as contrast agents to enhance geophysical characterization of oil and gas reservoirs. Under saline conditions and in the presence of fine materials, nanoparticle mobility in porous media can be severely limited. To address this issue, a series of column experiments was performed to evaluate the ability of selected polymers and surfactants to enhance the transport of magnetite nanoparticles (nMag) coated with cross-linked polymers in the presence of American Petroleum Institute (API) brine (8 wt % NaCl + 2 wt % CaCl 2 ). Aqueous suspensions containing nMag and API brine were injected at pore-water velocities of 2 ± 0.04 m/day or 10 ± 0.40 m/day through columns packed with either 40–50 mesh Ottawa sand or 60–170 mesh crushed Berea sandstone. When nMag (2500 mg/L) was introduced into Ottawa sand, 97% of the injected mass was recovered in the column effluent, indicating high mobility under saline conditions. However, the injection of nMag (2500 mg/L) into crushed Berea sandstone resulted in >60% nMag retention within the column. In order to improve delivery, nMag (2500 mg/L) was co-injected with 1000 mg/L hydroxyethyl cellulose (HEC-10), which increased nMag mobility 2-fold (78% effluent recovery). Co-injection of nMag with 1000 mg/L Gum Arabic or Calfax 16L-35, an anionic surfactant, resulted in slightly lower effluent recoveries of 72% and 69%, respectively. A preflood with 1000 mg/L HEC-10, followed by the injection of nMag alone (2500 mg/L), yielded an additional 20% improvement in nMag mobility (93% effluent recovery), suggesting that HEC-10 screened nMag attachment sites. A multisite nanoparticle transport model that accounts for heterogeneous mineralogy with variable attachment kinetics was able to accurately reproduce the effluent concentration data. Coupled with the observed 7-fold reduction in maximum retention capacity, the model parameter fits provide further evidence to support a site-blocking mechanism. These findings demonstrate the potential for relatively small additions (0.1%) of commercially available polymers and surfactants to greatly improve nMag mobility in porous media.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.038
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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.

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

Citations29
Published2016
Admission routes1
Has abstractyes

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