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Record W2249345478 · doi:10.2118/174624-ms

Characterization of Ultra-High Molecular Weight Oilfield Polyacrylamides Under Different pH Environments Using Asymmetrical Flow FFF and Multi-Angle Light Scattering Detector

2015· article· en· W2249345478 on OpenAlexafffund
Yogesh Dalsania, Ankit Doda, Japan Trivedi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicField-Flow Fractionation Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMolar massField flow fractionationPolymerFractionationMolar mass distributionMultiangle light scatteringPolystyreneMacromoleculeAnalytical Chemistry (journal)Dynamic light scatteringAdsorptionChemistryLight scatteringChemical engineeringColloidSmall-angle X-ray scatteringMaterials scienceChromatographyScatteringNanotechnologyOrganic chemistryOpticsNanoparticle

Abstract

fetched live from OpenAlex

Abstract Various types of ultrahigh molar mass polyacrylamides (PAMs) or HPAMs and their co- and ter-polymers used not only in enhanced oil recovery, but also in drilling, fracturing, water treatment and tailing applications require an accurate description of polymer molar mass (Mw) and hydrodynamic size for their optimal design. The range of Mw for various types of available HPAMs is between 4 and 30 million g/mol and is typically determined using intrinsic viscosity measurement. Molecular weight distribution (MWD or PDI) cannot be determined since neither standards with low PDI nor GPC/SEC techniques exist today for such ultrahigh molar mass polymers. Moreover, the solution environment in underground reservoirs, characterized by high temperatures, pH and the presence of monovalent and divalent ions, may often lead to changes in polymer macromolecular conformation. Current techniques, such as light scattering or microscopy, SEC, ultraviolet visible measurements and liquid chromatography, are not capable of accurately investigating these macromolecular complex structures for various reasons. In this paper the Asymmetrical Flow Field Flow Fractionation system was utilized to fractionate four different ultrahigh molecular weight HPAM samples, varying in molar mass and commercially used for oilfield applications, in different carrier pH values ranging from 12 to 3 (pH 12, pH 7.4 and pH 3). The system uses field flow fractionation a family of analytical techniques developed specifically for separating and characterizing macromolecules, colloids and particles. Other advantages over conventional GPC/SEC include minimum shear degradation, mild operating conditions and no sample loss due to adsorption. The flow system was equipped with a multiangle light scattering and refractive index detectors to measure molar mass and radius of gyration. The results show that the samples molecular weights increased substantially as the pH (or the ionic strength) of the carrier solution decreased from 12 to 3, especially for higher molar mass polymers. The samples radius of gyrations showed the opposite trend decreasing as the pH of the carrier solution changed from basic to acidic. For ultrahigh molecular HPAM at high pH, a narrower molar mass and radius distribution was observed with disaggregated molar mass and increased branching or swelling (therefore higher hydrodynamic radius). Use of this direct separation and measurement technique can improve understanding of polymer macromolecular structure and respective changes in the reservoir environments to enable optimal chemical dosage in oilfield applications.

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)0.0010.000

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 head0.016
GPT teacher head0.216
Teacher spread0.200 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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