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Record W2890471915 · doi:10.1190/segam2018-2998082.1

Time delays from stress-induced velocity changes around fractures in a time-lapse DAS VSP

2018· article· en· W2890471915 on OpenAlexaff
G. Binder, Aleksei Titov, Diana Tamayo, James Simmons, Ali Tura, Grant Byerley, David J. Monk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsHydraulic fracturingGeologyStress fieldFracture (geology)Stress (linguistics)Field (mathematics)Geotechnical engineeringSeismologyEngineeringMathematicsStructural engineeringPhilosophyFinite element method

Abstract

fetched live from OpenAlex

In a DAS time-lapse VSP survey, P-wave arrival time differences have been observed before and after hydraulic fracturing for each of 78 stages along a horizontal well. The time delays can shed light on fracture geometry and surrounding rock properties, but the mechanism causing the delays is not yet well-understood. Here, P-wave velocity changes caused by changing stress around fractures are investigated as a possible cause. Using an analytic 2D stress field and an empirical relation of velocity and effective stress, it is shown that time delays comparable to observed data can be obtained. The results motivate further work to model stress-induced velocity changes around fractures and maximize the unique information about fracture geometry that can be obtained from DAS VSP surveys. Presentation Date: Tuesday, October 16, 2018 Start Time: 8:30:00 AM Location: 204C (Anaheim Convention Center) Presentation Type: Oral

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2018
Admission routes1
Has abstractyes

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