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Record W2192723799 · doi:10.1139/cjce-2015-0030

Predicting the plan annular pressure using the power law flow model in horizontal directional drilling

2015· article· en· W2192723799 on OpenAlexafffundvenue
Ali Rostami, Yaolin Yi, Alireza Bayat, Manley Osbak

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Nebraska-Lincoln
KeywordsDirectional drillingAnnulus (botany)DrillingPower-law fluidShear (geology)Rate of penetrationVolumetric flow rateDrilling fluidPressure dropPressure gradientGeologyShear ratePetroleum engineeringMechanicsGeotechnical engineeringRheologyEngineeringMechanical engineeringMaterials scienceNon-Newtonian fluidComposite material

Abstract

fetched live from OpenAlex

During horizontal directional drilling (HDD), the drilling fluid pressure must not surpass the maximum allowable pressure to minimize the risk of hydraulic fracturing; hence, a plan pressure can be used to manage the drilling fluid pressure. This paper addresses the prediction of plan annular pressure using the power law flow model. The annular pressures were predicted using the power law model with rheological parameters determined from a six-speed viscometer with varying shear rate ranges from 3 to 600 RPM for two HDD projects, and the predicted pressures were compared with the measurements. The results indicated that the annular pressure could be properly predicted, and the best prediction was achieved by the shear rate range of 6–100 RPM due to embracing the real shear rate of drilling fluid inside the annulus; this shear rate range is close to the recommendation (3–100 RPM) by American Petroleum Institute Recommended Practice.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.665
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.175
Teacher spread0.162 · 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.

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

Citations6
Published2015
Admission routes3
Has abstractyes

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