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Record W1988866743 · doi:10.2118/124673-ms

Fracture Mapping in the San Juan Basin, New Mexico

2009· article· en· W1988866743 on OpenAlexaff
Erick Estrada, Neale Roberts, Leen Weijers, Tom Riebel, Steven Logan

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2009
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsTiltmeterMicroseismGeologyDrillingFracture (geology)SeismologyInfillStructural basinAzimuthGeotechnical engineeringGeomorphologyEngineeringStructural engineeringAmplitudeGeometry

Abstract

fetched live from OpenAlex

Abstract An infill drilling pilot test in the Lewis, Mesaverde and Dakota formations of the San Juan Basin, New Mexico, included microseismic and tiltmeter monitoring of fracture stimulations. This study looks at microseisms from eight stages in two wells using an 18-tool stacked toolstring and surface tiltmeter measurements of 23 stages in six wells, (including the microseismic wells), using a 63-site tiltmeter array. A pressure observation well also situated within the pilot area, provided key information to the interpretation of data from the Lewis fracture stages. Complex (dendritic) fracture growth observed in the Lewis formation may be controllable through modification of the injection rate. Lower injection rates encourage simple planar growth and control fracture-height growth. Shorter fracture half-lengths were observed in the lower Mesaverde stages, leading to a redesign that is currently being tested in the field. The average fracture azimuth measured in this project, N21°E, confirmed the current understanding of the stress orientation in this part of the San Juan Basin and provided confidence regarding additional infill well placement.

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 score0.174
Threshold uncertainty score0.345

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 communication0.0000.000
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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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