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Record W2999724424 · doi:10.1139/cgj-2019-0094

Measuring while drilling in Florida limestone for geotechnical site investigation

2020· article· en· W2999724424 on OpenAlexvenueno aff
Michael Rodgers, Michael McVay, David Horhota, José Ignacio Hernando, Jerry M. Paris

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersFlorida Department of TransportationU.S. Department of Transportation
KeywordsCoringDrillingGeotechnical engineeringDrillGeologyDrill cuttingsMeasurement while drillingPetroleum engineeringRock mass classificationDrilling fluidMining engineeringEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Florida limestone can be challenging to recover during coring operations, as the rock generally is soft, porous, and often highly weathered. Low recoveries coupled with poor rock quality are common in Florida, which limits the data available for geotechnical design. However, the low recoveries and poor rock quality may be attributable to coring techniques and not the rock’s in situ condition. This paper explores integrating measuring while drilling (MWD) into standard coring procedures to provide in situ strength assessment and optimize core recoveries and rock quality to improve rock mass characterization. Six drilling parameters were monitored during the research, and a controlled drilling environment was developed to investigate each monitored parameter’s effects. Variable drill bit configurations were also explored to investigate the effects of bit geometry. Interdependent relationships between the drilling parameters were discovered and a new concept of operating within optimal drilling parameter ranges based on these relationships is introduced. The coring practices developed in the controlled environment were then tested in natural Florida limestone. It was concluded that operating within the optimal ranges allows in situ strength assessment and improves core recoveries and rock quality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.190
Teacher spread0.158 · 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 teacher head, not a consensus.

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
Published2020
Admission routes1
Has abstractyes

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