MétaCan
Menu
Back to cohort
Record W3117246593

Countercurrent Flow Enhances CO2 Accumulation During Upward Migration in Storage Aquifers

2018· article· en· W3117246593 on OpenAlexaff
Bo Ren, Larry W. Lake, Steven L. Bryant

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCapillary actionCountercurrent exchangeSaturation (graph theory)Permeability (electromagnetism)AquiferTrappingCapillary pressureMechanicsGeologyRelative permeabilityFlow (mathematics)Geotechnical engineeringMaterials scienceChemistryGroundwaterThermodynamicsPorous mediumComposite materialMembranePhysicsPorosity
DOInot available

Abstract

fetched live from OpenAlex

Using high-resolution numerical simulation, we model the purely buoyant flow of CO2 in a two-dimensional (2D) closed aquifer domain. The domain is composed of two heterogeneous layers with different average permeabilities. The high permeability layer is overlain by the low permeability layer, and each layer has heterogeneous capillary pressure functions correlated with geostatistically-assigned permeabilities. Initially, CO2 is emplaced at the bottom with a given column height and then allowed to rise. During CO2 upward migration, two types of CO2 accumulations are observed: one accumulation is capillary-barrier trapping, and the other is permeability-retarded. More importantly, capillary-trapped CO2 interacts with the permeability-retarded CO2. The former CO2 trapping exhibits a much higher saturation, which causes the latter CO2, under countercurrent flow (i.e., CO2 rises and while water falls), to move upward much slowly. Thus, accurate evaluation of CO2 upward migration should consider the interaction between capillary-trapping and permeability-retardation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.013
GPT teacher head0.282
Teacher spread0.269 · 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 designSimulation or modeling
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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207