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Record W2185533385 · doi:10.14288/1.0053580

Permeability of Limestone-Dolomite Composite Fracture Surfaces

2009· article· en· W2185533385 on OpenAlexaboutno aff
Van de Reep

Bibliographic record

VenueOpen Collections · 2009
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsDolomiteComposite numberGeologyFracture (geology)Permeability (electromagnetism)MineralogyGeotechnical engineeringComposite materialMaterials scienceChemistry

Abstract

fetched live from OpenAlex

The transport properties of fault zones are critically important in predicting subsurface fluid behavior in deformed rocks and understanding fold and thrust belts. Limestone-dolomite composite rocks are common components of fold and thrust belts and are also productive reservoir rocks for hydrocarbons. This study develops a methodology for measuring the permeability of experimentally induced fracture surfaces in limestone-dolomite composite rocks. Cores of rocks from the Mount Head Formation and the Fairholme Group of southern Alberta were deformed at a confining pressure of 25 MPa in a triaxial rock press. Fluid ports were drilled in the deformed cores to ensure direct fluid access to the fracture surface. The transient pulse decay method was used to determine the permeability of the fractures. Experimental difficulties resulted in only two permeabilities being derived from experimentation. Modeling of the transient pulse decay for the fracture surfaces shows permeabilities of 4•10-17 m2 for predominantly dolomite and 8•10-17 m2 for dolomitic limestone. Lower permeability in the dolomite end member is thought to be due to very fine comminution in the fracture gouge caused by highly focused strain with little distributed strain outside of the fracture itself. Increasing heterogeneity in a rock increases the distributed strain, causing an increase in permeability. This study serves as an initial development in the ability to measure the permeability of fracture surfaces. Increasing precision and accuracy of measurements is expected with further research.

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 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 score0.301
Threshold uncertainty score0.359

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)0.0000.000

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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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.

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

Citations1
Published2009
Admission routes1
Has abstractyes

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