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Record W2031494817 · doi:10.2118/160884-ms

A New Viscoelastic Surfactant for High Temperature Carbonate Acidizing

2012· article· en· W2031494817 on OpenAlexaff
Guanqun Wang, H. A. Nasr‐El‐Din, Jian Zhou, Stuart Holt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsAkzoNobel (Canada)
Fundersnot available
KeywordsViscosityViscoelasticityPulmonary surfactantCorrosionCarbonateMaterials scienceChemical engineeringSolventThermodynamicsChemistryComposite materialOrganic chemistryMetallurgyPhysics

Abstract

fetched live from OpenAlex

Abstract Diverting agents have been widely used during matrix acidizing treatments to accomplish the uniform distribution of acid solutions across the target zones. One of the most popular formulas is based on viscoelastic surfactants (VES) which showed good results in the lab and field tests. A major limitation with the conventional VES is temperature. Most of these surfactants do not hold viscosity at temperatures greater than 250°F. A new VES that can be used at higher temperatures was developed. The apparent viscosity of the spent VES-based acids was measured as a function of temperature and shear rate at 300 psi. Various corrosion inhibitors were mixed together with the new VES to find out the most compatible formula. A coreflood test was conducted on a 20 in. long carbonate core to examine the performance of the new surfactant. The core was CAT scanned before and after the experiment to observe the wormholes created by the new system. The acid contained 20 wt% HCl and 4 vol% VES. Spent acids of the new VES had a high viscosity up to 325°F. Several corrosion inhibitors were identified that can be mixed with the new system without affecting the apparent viscosity of the spent acid. Similar to current VES, mutual solvent is recommended in the post flush to reduce the fluid viscosity and remove any gel from the core. CAT scan of the core show wider and tortuous wormholes, indicative of good retardation of the new VES-based acid system.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.574

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 head0.182
Teacher spread0.176 · 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 designSimulation or modeling
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

Citations10
Published2012
Admission routes1
Has abstractyes

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