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Record W3000357077 · doi:10.2523/iptc-19615-ms

3D Confocal Imaging Methodology Optimized for Pore Space Characterization of Microporous Carbonate Reservoirs

2020· article· en· W3000357077 on OpenAlexaff
Ahmed Hassan, Viswasanthi Chandra, M.P. Yutkin, Tadeusz W. Patzek

Bibliographic record

VenueInternational Petroleum Technology Conference · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsInterconnectivityMicroporous materialCarbonateMaterials scienceConfocalCharacterisation of pore space in soilPorosityCharacterization (materials science)High resolutionMineralogyGeologyNanotechnologyComposite materialOpticsRemote sensing

Abstract

fetched live from OpenAlex

Microporous carbonates host a significant portion of the remaining oil-in-place in the giant carbonate reservoirs of the Middle East. Carbonates host wide range of pore sizes, however the key element influencing hydrocarbon flow is pore interconnectivity. We evaluate the use of confocal microscopy to image fluorescent epoxy pore casts of microporous carbonates. The acquired high-resolution 3D confocal images are used to gain invaluable insights on the interconnectivity between macroporosity and microporosity. We analyzed the sensitivity of quality of epoxy pore cast images to: fluorochrome selection, objective lens, and imaging medium by imaging standard fluorescent spherical beads. Guided by the sensitivity results, we acquired 3D images of the multi-modal pore space in an Indiana limestone sample with lateral- and axial-resolution of 0.36 µm and 2 µm, respectively. And we were able to identify the multi-scale pore types in the studied carbonate sample and highlight their interconnectivity.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.824

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 science0.0010.000
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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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