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Record W4413391069 · doi:10.1115/omae2025-155566

Visualization and Quantitative Analysis of Shale Properties Under CO2 Fluid-Rock Interaction: Implication for CO2 Geological Storage

2025· article· en· W4413391069 on OpenAlexaff
Lianhe Sun, Haizhu Wang, Zelong Mao, Peichun Amy Tsai, Bin Wang, Yaochen Zhang, Xu Cheng, Huan Li, Mingsheng Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVisualizationOil shaleGeologyPetroleum engineeringShale gasData visualizationComputer sciencePetrologyData mining

Abstract

fetched live from OpenAlex

Abstract CO2 flooding technology effectively enhances shale oil production while enabling CO2 sequestration. Changes in pore structure and wettability during the flooding process can significantly influence CO2 storage safety. This study investigates the effects of supercritical CO2 (SCCO2) treatment on the pore structure and wettability of shale oil reservoirs. Advanced techniques, including AMICS Automated Mineralogy System (AMICS), Scanning Electron Microscopy (SEM), and Fourier Transform Infrared Spectroscopy (FTIR), were employed to evaluate alterations in mineral composition, pore structure, chemical functional groups, and water/shale contact angles using a contact angle goniometer. Results show that SCCO2 treatment reduces dolomite content, increasing average pore size and pore connectivity. Furthermore, increased hydrophilic functional groups decrease water contact angles, thereby enhancing hydrophilicity and improving sequestration efficiency. These findings offer insights for shale oil recovery and CO2 storage safety evaluation.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.047
GPT teacher head0.348
Teacher spread0.301 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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