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Record W4416159453 · doi:10.1016/j.fuel.2025.137450

Experimental study on CO2 sealing capacity of salt-gypsum caprock

2025· article· en· W4416159453 on OpenAlexaff
Beibei Jiang, Jiahuan Liu, Jiabo Liu, Qianlong Yang, Yongsheng Tan, Hongwen Luo, Ying Li

Bibliographic record

VenueFuel · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsPetro-Canada
FundersNational Natural Science Foundation of China
KeywordsCaprockPermeability (electromagnetism)Supercritical fluidUltimate tensile strengthPore water pressureRheology

Abstract

fetched live from OpenAlex

CO 2 geological storage is a pivotal technology for climate mitigation, and the sealing capacity of the caprock is paramount for its long-term safety. Salt-gypsum caprocks, known for their ultra-low permeability and ductility, are ideal candidates, yet their sealing mechanisms under multiphase CO 2 conditions are not fully understood. This study systematically investigates the sealing capacity of salt-gypsum caprock from the Wolonghe Gas Field through integrated experiments, including transient pulse decay permeability tests, phase-controlled breakthrough pressure tests, and triaxial compression tests. The results demonstrate that the permeability decreases exponentially with effective stress, with a 40 % irreversible loss post-unloading. The breakthrough pressure, governed by CO 2 phase state (sc-CO 2 > liquid > gaseous), increases linearly with burial depth, reaching 15.64 MPa at 1000 m. The caprock transitions into the plastic domain beyond 2600 m, exhibiting a tensile strength of 5.15 MPa sufficient to inhibit micro-fracture propagation. This research validates the long-term containment potential of salt-gypsum caprocks and identifies a synergistic “low permeability, high breakthrough pressure, self-healing” triple-sealing mechanism, providing a theoretical basis for site selection in CO 2 geological storage projects.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.999

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)0.0020.000

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 head0.031
GPT teacher head0.305
Teacher spread0.274 · 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 designObservational
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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