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Record W2043822047 · doi:10.1190/tle31121502.1

Combined microseismic and 4D to calibrate and confirm surface 3D azimuthal AVO/LMR predictions of completions performance and well production in the Horn River gas shales of NEBC

2012· article· en· W2043822047 on OpenAlexaff
Bill Goodway, Dave Monk, Marco A. Pérez, Greg Purdue, Paul F. Anderson, Andrew Iverson, V. Khiller Vera, David Cho

Bibliographic record

VenueThe Leading Edge · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsMicroseismGeologyFrench hornHydraulic fracturingNatural gasAzimuthUnconventional oilVolume (thermodynamics)Fracture (geology)Petroleum engineeringSeismologyGeochemistryPetrologyGeotechnical engineeringOil shalePaleontologyEngineeringGeometryAcoustics

Abstract

fetched live from OpenAlex

The ability to high-grade gas shales is essential to optimizing completions and maximizing stimulated rock volume (SRV) in the capital-intensive development of the Horn River resource play in northeast British Columbia (NEBC). To assist in optimizing stimulation efforts, seismic data are used to estimate and map four parameters that influence hydraulic fracture effectiveness: rock properties, in-situ stress, natural fractures, and reservoir geometry.

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)0.000
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 score0.223
Threshold uncertainty score0.443

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.223
Teacher spread0.201 · 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

Citations36
Published2012
Admission routes1
Has abstractyes

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