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Record W3107466815 · doi:10.1016/j.gete.2021.100233

Ion-species in pore fluids with opposite effects on limestone fracturing

2021· article· en· W3107466815 on OpenAlexfundno aff
Anne Pluymakers, Audrey Ougier‐Simonin, Auke Barnhoorn

Bibliographic record

VenueGeomechanics for Energy and the Environment · 2021
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersHorizon 2020Natural Environment Research CouncilBiogeoscience Institute, University of CalgaryUniversité de StrasbourgSight Research UKUniversiteit UtrechtNederlandse Organisatie voor Wetenschappelijk OnderzoekBritish Geological SurveyCanon Foundation for Scientific Research
KeywordsCarbonateGeologyPore water pressureDeformation (meteorology)Carbonate rockSedimentary rockFracture (geology)MineralogyHydraulic fracturingGeotechnical engineeringGeochemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

At ten percent of sedimentary rocks, limestones are common geo-energy reservoirs. Being highly soluble, limestones are prone to fluid-assisted deformation and their mechanical behaviour likely susceptible to fluid chemistry. In this study, we saturated limestone samples with 0.4 M MgSO4 or 0.4 M Na2SO4 CaCO3-saturated solutions (naturally present in many reservoirs) or a reference CaCO3-saturated solution for 1, 50 or 200 days prior to mechanical testing. Triaxial deformation tests were then performed at 7, 30, and 70 MPa of confining pressure room temperature, under drained conditions. Our results show that exposure to one different cation impacts the strength of this rock (up to 12 of dry rock strength) and its failure dynamics, associated with different microstructural damage distribution. A 200 day exposure to MgSO4 promotes strengthening whilst similar exposure to Na2SO4 leads to weakening. We posit that these strength changes may be related to changes in surface charges on the mineral surfaces. More data on fluid–rock interaction will be key to fully understand fracture propagation in natural carbonate formations.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.641
Threshold uncertainty score0.456

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 head0.004
GPT teacher head0.154
Teacher spread0.150 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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