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Record W4304194006 · doi:10.1038/s41598-022-19577-8

Application of microtomography and petrography techniques for the characterization of porosity of synthetic carbonatic rock minerals before and after acidification processes

2022· article· en· W4304194006 on OpenAlexfundno aff
Katia Botelho Torres Galindo, Cecília Maria Mota Silva Lins, Leonardo José do Nascimento Guimarães, Analice Lima, Katarina Silva, Abraão Nova

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersPetrobrasEnergi Simulation
KeywordsPetrographyPorosityDissolutionX-ray microtomographyMineralogyGeologyCharacterization (materials science)Materials scienceGeotechnical engineeringChemistry

Abstract

fetched live from OpenAlex

The objective of this work is to physically characterize and analyze synthetic carbonate rocks through microtomography and petrography techniques, focusing on a comparative analysis before and after degradation with a reactive fluid. For this study, physical characterization analysis with computerized microtomography and petrography on the samples before and after the acidification procedure was performed. The petrographic analysis verified an increase in both intergranular and intragranular porosities after dissolution. The microtomography analysis quantified the maximum increase in porosity, from 11.8 to 41.3% in the two-dimensional analysis and 31.6-52% in the three-dimensional analysis of the porous structures. Furthermore, the pores were quantified according to their area, and data was obtained on the orientation of the pores, providing insight into the preferred paths of fluid flow. It was also observed that the microtomography technique was an effective tool for characterizing fractures in the samples before and after dissolution. Such analyses are crucial for the extraction and injection of fluids at high depths due to the mechanical and physical risks arising from the dissolution of minerals as well as changes in pressure, temperature, and saturation, all of which affect the stress state of the reservoir rock.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.224

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 head0.003
GPT teacher head0.182
Teacher spread0.179 · 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 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

Citations12
Published2022
Admission routes1
Has abstractyes

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