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Record W2953175679 · doi:10.1190/geo2018-0399.1

Shock-induced Stoneley waves in carbonate rock samples

2019· article· en· W2953175679 on OpenAlexaff
Ning Li, Kewen Wang, Hongliang Wu, Qingfu Feng, Huajun Fan, David Smeulders

Bibliographic record

VenueGeophysics · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsCarbonateGeologyReflection coefficientFracture (geology)Shock waveMineralogyReflection (computer programming)Annulus (botany)Materials scienceAcousticsGeotechnical engineeringOpticsComposite materialMechanics

Abstract

fetched live from OpenAlex

ABSTRACT To assess the productivity of oil- and gas-bearing carbonate reservoirs, acoustic waves are used to extract information on the reservoir properties. We performed shock-induced broadband linear acoustic wave experiments on fractured carbonate samples with different fracture apertures. Samples were prepared from a full-diameter carbonate rock core in which the aperture of a single fracture could be varied by means of spacers. Microseismograms were constructed from multiple wave experiments. We measured the wave speeds of different wave modes in the annulus along the cylindrical core sample and the transmission coefficient of the Stoneley wave over the fracture. We found that the transmission coefficient of the annulus Stoneley wave can directly be related to the fracture aperture and the fracture length. Comparison with a straightforward quasi-1D theory yielded best-fit values for fracture aperture and length that were in agreement with the actual values, for the fracture aperture determined by the size of the spacers. The values of the fracture length were not accurately predicted probably due to the underestimation of the fracture compliance in the experiment. The inversion method can be optimized for carbonate reservoir conditions to extract fracture data from Stoneley wave transmission and reflection coefficients.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000

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)0.0000.001

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 head0.015
GPT teacher head0.207
Teacher spread0.193 · 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.

Study designObservational
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

Citations4
Published2019
Admission routes1
Has abstractyes

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