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Record W3093945403 · doi:10.2118/203848-pa

Characterizing Returning Polymers in Hydraulic-Fracturing Flowback and Produced Water: Implications for Colloid Formation (includes associated erratum)

2020· article· en· W3093945403 on OpenAlexaffabout
Konstantin von Gunten, Katherine N. Snihur, Ryan T. McKay, Michael J. Serpe, Janice P.L. Kenney, Daniel S. Alessi

Bibliographic record

VenueSPE Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsMacEwan UniversityUniversity of Alberta
Fundersnot available
KeywordsColloidHydrolysisFourier transform infrared spectroscopyPolymerPolyacrylamideReducerChemistryProduced waterChemical engineeringEnvironmental engineeringEnvironmental scienceOrganic chemistryPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

Summary Partially hydrolyzed polyacrylamide (PHPA) friction reducer was investigated in produced water from hydraulically fractured wells in the Duvernay and Montney Formations of western Canada. Produced water from systems that used nonencapsulated breaker had little residual solids (<0.3 g/L) and high degrees of hydrolysis, as shown by Fourier-transform infrared (FTIR) spectroscopy. Where an encapsulated breaker was used, more colloidal solids (1.1–2.2 g/L) were found with lower degrees of hydrolysis. In this system, the molecular weight (MW) of polymers was investigated, which decreased to <2% of the original weight within 1 hour of flowback. This was accompanied by slow hydrolysis and an increase in methine over methylene groups. Increased polymer-fragment concentrations were found to be correlated with a higher abundance of metal-carrying colloidal phases. This can lead to problems such as higher heavy-metal mobility in the case of produced-water spills and can cause membrane fouling during produced-water recycling and reuse. NOTE: An erratum has been issued for this paper and added to the PDF. A copy is also available under the Supplementary Data section.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.578

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 communication0.0000.001
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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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