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Record W2889670878 · doi:10.1190/segam2018-2992460.1

Kinked magnitude distributions for hydraulic fracturing

2018· article· en· W2889670878 on OpenAlexaffabout
Nadine Igonin, David W. Eaton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMagnitude (astronomy)Hydraulic fracturingGeologyPetroleum engineeringEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

In earthquake seismology, the seismicity magnitude distributions generally follow the well-known Gutenberg-Richter relationship with slope defined by the b-value; yet, observed drops from b≃2 to b≃1 have been interpreted to indicate fault activation during hydraulicfracturing completions. A careful investigation of the seismic magnitudes of events recorded during a hydraulic fracturing program in Alberta, Canada, shows that the distribution is bilinear (or “kinked”), with a distinct change in slope from b≃1.6 for MW<1.0 to b≃0.6 for MW>1.0 when the entire catalog is considered. Analysis of the b-value for distinct clusters of events shows that the kinked distribution arises here from superposition of different magnitude distributions for each cluster. We consider two hypotheses for the spatial variability in b-value, namely that the b-value variations reflect an underlying variability in stress state, or an underlying variability in the fault/fracture size distribution. Start Time: 8:30:00 AM Location: 210C (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)0.001
metaresearch head score (Gemma)0.012
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.238
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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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