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Record W4249993024 · doi:10.2523/108142-ms

Hydraulic Fracture Diagnostics In The Williams Fork Formation, PiceanceBasin, Colorado Using Surface Microseismic Monitoring Technology

2007· article· en· W4249993024 on OpenAlexaff
David H. Abbott, R. Neale, James Lakings, Lynn D. Wilson, Jay Close, Evan S. Richardson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsMicroseismGeologyBoreholeHydraulic fracturingSeismologyFracture (geology)RidgeGeotechnical engineeringPetrologyPaleontology

Abstract

fetched live from OpenAlex

A surface microseismic array was utilized to perform hydraulic fracture diagnostics during stimulation of the Chevron Skinner Ridge (SR) #698-22-1 well, Williams Fork Formation (Late Cretaceous), Garfield County, western Piceance Basin, western Colorado. Production from very low permeability Williams Fork gas sandstones requires fracture stimulation to enhance wellbore-to-reservoir connectivity. The use of surface microseismic monitors without borehole equipment in downhole configurations represents a relatively new and untested technology for hydraulic fracture diagnostics. Analysis of the surface microseismic data was carried out for five (5) hydraulic fracture stages to: (1) determine the applicability of the surface microseismic approach in the absence of an offset observation well; and (2) characterize fracture height, azimuth, length and symmetry with respect to rock properties.Hydraulic fracture stimulations to date at SR have encompassed limited entry

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.551

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 studies0.0010.001
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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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 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

Citations2
Published2007
Admission routes1
Has abstractyes

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