MétaCan
Menu
Back to cohort
Record W4412146682 · doi:10.1029/2024gl114388

Capillary‐Driven Transport and Precipitation of Salt in Heterogeneous Structures During Carbon Sequestration

2025· article· en· W4412146682 on OpenAlexaff
Tiancheng Ji, Amir Haghi, Peixue Jiang, Rick Chalaturnyk, Ruina Xu

Bibliographic record

VenueGeophysical Research Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsCarbon sequestrationPrecipitationCarbon fibersEnvironmental scienceCapillary actionGeologyEarth scienceAtmospheric sciencesCarbon dioxideMaterials scienceChemistryMeteorology

Abstract

fetched live from OpenAlex

Abstract In the Aquistore deep saline CO 2 storage project, researchers observed that most of the salt distribution is associated with perforations in a zone with low injectivity. However, the impact of salt precipitation in heterogeneous porous media has not been fully clarified. In this paper, we conducted laboratory experiments on subsurface cores from the Aquistore project to investigate the influence of heterogeneous porous structures with varying permeabilities on salt precipitation. The results show that due to capillary‐driven transport, salt precipitation primarily occurs at the front end of low‐permeability structures and within high‐permeability structures, consistent with observations in the Aquistore injector. We first found that salt precipitation within fracture led to self‐sealing of fracture. When CO 2 was injected into a core containing horizontal fracture at a low flow rate, fracture closure was more likely to occur. Intermittent injection of low‐concentration brine was found to alleviate salt precipitation.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207