MétaCan
Menu
Back to cohort
Record W4392912223 · doi:10.3390/w16060868

Using the Tidal Response of Groundwater to Assess and Monitor Caprock Confinement in CO2 Geological Sequestration

2024· article· en· W4392912223 on OpenAlexfundno aff
Yan Zhang, Bingfei Chu, Tianming Huang, Shengwen Qi, Michael Manga, Huai Zhang, Bowen Zheng

Bibliographic record

VenueWater · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
FundersKey ProgrammeChinese Academy of SciencesInstitute of Geology and Geophysics, Chinese Academy of SciencesNational Natural Science Foundation of ChinaCanadian Institute for Advanced Research
KeywordsCaprockGeologyGroundwaterCarbon sequestrationEnvironmental sciencePetroleum engineeringHydrology (agriculture)Geotechnical engineeringCarbon dioxide

Abstract

fetched live from OpenAlex

Carbon geological storage (CGS) is an important global practice implemented to mitigate the effects of CO2 emissions on temperature, climate, sea level, and biodiversity. The monitoring of CGS leakage and the impact of storage on hydrogeological properties is important for management and long-term planning. In this study, we show the value of passive monitoring methods based on measuring and modeling water-level responses to tides. We review how monitoring can be used to identify time-varying horizontal and vertical permeabilities as well as independently detect time-varying fracture distribution in aquifer–caprock systems. Methods based on water-level responses to Earth tides are minimally invasive, convenient, economic (since they use existing groundwater wells), and time-continuous. We show how measurements can be used to detect aquifer leakage (caprock confinement) and the distribution of surrounding faults and fractures, which are the two most important unsolved quantities in assessing geological CO2 storage strategies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score1.000

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)0.0010.000

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 head0.066
GPT teacher head0.327
Teacher spread0.261 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueWaterSame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207