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Record W4401629653 · doi:10.1093/gji/ggae278

Laboratory constraints on the anisotropic dynamic-to-static ratios for shale's elastic constants: an example from the Duvernay unconventional reservoir

2024· article· en· W4401629653 on OpenAlexaff
Luyi Shen, Douglas R. Schmitt, Yu‐Yong Jiao

Bibliographic record

VenueGeophysical Journal International · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsPetrophysicsAnisotropyHydraulic fracturingOil shaleGeologyContext (archaeology)Tight gasMineralogyPorosityGeotechnical engineeringPhysicsOptics

Abstract

fetched live from OpenAlex

Summary Dynamic material constants obtained by wave-based methods are different from their static counterparts. Constraining rock's elastic constants’ dynamic-to-static ratios (Rij) are important for understanding the geomechanical properties of earth's materials, particularly in the context of hydraulic fracturing that requires the knowledge of shale's static elastic constants. Conducting experiments with dynamic and elastic constants’ anisotropy, on top of their pressure dependency, properly accounted for is challenging. Here, we measure suites of dynamic and static elastic constants, with anisotropy fully accounted for, on the shale samples extracted from the Duvernay unconventional reservoir; a comprehensive set of geochemical/petrophysical measurements are obtained too. We observe that the dynamic-to-static ratios are generally not sensitive to the increasing pressures at σ > 50 MPa; we do not find a correlation with the samples’ mineral contents either. However, we find that Rij strongly correlates to the dynamic elastic constants except for the R11. The correlation between Rij, particularly Ri3, and the dynamic elastic constants can be explained by the sedimentary rocks’ compactness and the horizontal void spaces parallel to the rock's laminated bedding planes.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.997

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)0.0040.000

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 head0.027
GPT teacher head0.267
Teacher spread0.240 · 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 designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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