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Record W4413807897 · doi:10.29017/scog.v48i2.1744

Determining The Role of Ion Exchange in Permeability Alteration During Asp Injection: A Laboratory-Scale Study Using Cmg Reactive Transport Modeling

2025· article· en· W4413807897 on OpenAlexaff
Dike Fitriansyah Putra, Mohd Zaidi Jaafar, Tengku Amran Tengku Mohd, Novia Rita, Agus Dahlia, Ichsan Al Sabah Lukman, Made Mahaguna Putra

Bibliographic record

VenueScientific Contributions Oil and Gas · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein purification and stability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsScale (ratio)Permeability (electromagnetism)ChemistryIon transporterIon exchangeBiophysicsIonBiochemistryMembranePhysicsBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Electrokinetic-based Enhanced Oil Recovery (EK-EOR) presents a novel method that applies electric fields to mobilize trapped hydrocarbons in formations with low permeability. This work investigates the impact of ion exchange and mineralogical reactions on permeability behavior during Alkali-Surfactant-Polymer (ASP) flooding, integrating laboratory-scale sand-pack experiments with reactive transport simulation in CMG-GEM. During ASP injection, a marked rise in differential pressure indicated abrupt changes in permeability caused by polymer accumulation, mineral dissolution, and early-stage ion exchange. Two numerical scenarios were assessed: one involving only aqueous-phase chemistry, and another incorporating fluid reactions and solid-surface ion exchange. The latter case required minimal calibration to match experimental data, while the former demanded unrealistic permeability upscaling. The results underscore ion exchange as a vital mechanism influencing fluid transport in EK-EOR. Although wettability alteration is often associated with ASP processes, this study suggests that under short exposure periods, changes in permeability dominate recovery performance. The findings improve reservoir modeling by promoting geochemical integration into simulation workflows.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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