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Record W2088756493 · doi:10.2118/86548-pa

Characterization of Crosslinked Gel Kinetics and Gel Strength by Use of NMR

2008· article· en· W2088756493 on OpenAlexafffund
Laura Romero‐Zerón, F. Manalo, Apostolos Kantzas

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of CalgaryUniversity of New Brunswick
FundersCanada Research Chairs
KeywordsRheologyMaterials sciencePermeability (electromagnetism)BottleRheometryCharacterization (materials science)PolymerPorous mediumComposite materialPorosityChemical engineeringChemistryNanotechnologyMembrane

Abstract

fetched live from OpenAlex

Summary Highly crosslinked gels are used in high-permeability reservoirs to achieve appropriate fluid-loss control during well completion and workover operations. Crosslinked gels are also used to shut off unwanted gas and/or water influx into production wells and to improve the conformance of the near-wellbore injection profile in naturally fractured or high-permeability reservoirs. In all these applications, the appropriate design of the gel treatment is critical to ensure an efficient gel placement. Important variables of gel systems are gel rheology and gel strength during and after the gelation reaction is completed. The rheology of gels and gelation rates is commonly determined by rheometry or, in a qualitative mode, through bottle testing with well-known gel-strength codes (i.e., Sydansk's code). Rheological measurements can be time-consuming, while bottle testing can lead to an inconsistent gel description as a result of the subjective nature of the gel-strength code. This paper evaluates the use of low-field nuclear magnetic resonance (NMR) as a nonin-trusive technique to monitor gelation rates and to characterize gel strength. Because of the nonintrusive nature of this technique, it could be considered to be a better alternative to conventional rhe-ological measurements and common qualitative methods, such as gel-strength codes. In addition, NMR could offer faster and more accurate gel-strength characterization and gelation monitoring compared to rheological methods. Furthermore, it can be used in porous media. NMR parameters are predicted and calibrated conducting concentration sweeps of polymer, crosslinker, and brine, as well as gelation-time sweeps. This then allows for a standardized method for gel characterization. The findings of this work include a preliminary assessment of the use of different techniques, such as low-field NMR, rheometry, and bottle testing, for monitoring the gelation reaction and gel strength of partially hydrolyzed polyacrylamide chromium [(HPAm)/Cr(III)] acetate gel. The experimental results also include the initial identification of the gel point for different formulations of the gel system using low-field NMR.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.033
GPT teacher head0.254
Teacher spread0.221 · 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

Citations57
Published2008
Admission routes2
Has abstractyes

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