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Record W2341452530 · doi:10.2118/179888-ms

Enhancing Scale Inhibitor Squeeze Retention With Additives

2016· article· en· W2341452530 on OpenAlexaff
Louise Sutherland, M. M. Jordan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsNalco (Canada)
Fundersnot available
KeywordsAdsorptionPhosphonatePolymerDesorptionChemical engineeringPrecipitationChemistryMineral oilPhosphateCalciumOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The practice of scale squeeze treatments to oil/gas production wells to prevent inorganic scale formation has been applied for over 30 years and during that period of time different mechanisms to retain the inhibitor chemical have been evaluated. The simple mechanism of inhibitor retention adsorption/desorption has been complemented over the years by enhanced adsorption via mutual solvent and full precipitation of the active inhibitor as calcium salt onto the mineral surface of the reservoir. This study has been conducted to understand if the retention of phosphonate and phosphate ester inhibitors can be improved by adsorption of a charged polymer onto the rock prior to application of the scale inhibitor or if application of the same polymer enhancer after adsorption of the scale inhibitor will extend squeeze life. These tests have been carried out using pack floods at 85°C with synthetic North Sea produced water and the details of the extension in treatment life observed are correlated to the inhibitor type tested and the sequence of application of the polymer enhancer. This study shows how the different functional groups within the scale inhibitors interact with the mineral surface and polymer enhancer to extend treatment lifetimes and so potentially reducing the frequency of squeeze treatments and therefore total cost of operations.

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 categoriesInsufficient payload (model declined to judge)
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 score0.087
Threshold uncertainty score0.994

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)0.0070.000

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 head0.010
GPT teacher head0.213
Teacher spread0.204 · 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.

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
Published2016
Admission routes1
Has abstractyes

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