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Record W4410328053 · doi:10.1061/jggefk.gteng-12999

Influence of Overlying Layers of Loose and Dense Sand or Gravel on the Drilling Mud Pressure Causing Mud Loss from Horizontal Boreholes

2025· article· en· W4410328053 on OpenAlexaff
Haitao Lan, Ian D. Moore

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsQueen's University
Fundersnot available
KeywordsGeologyBoreholeGeotechnical engineeringDrillingDrilling fluidDirectional drillingPetroleum engineeringEngineering

Abstract

fetched live from OpenAlex

Stability of horizontal boreholes under the pressure of drilling mud is a key consideration during horizontal directional drilling. Based on the successful application of experimental and numerical studies in uniform sand undertaken by the authors, an experimental study was carried out to investigate the relationship between the maximum measured mud pressure Pmax and influence of sand layers (i.e., a dense sand layer covered by different thicknesses of loose sand or dense sandy gravel). Mud pressure histories from experiments involving boreholes in stratified deposits were explored for the first time. A numerical study was also conducted, and the numerical results were close to the Pmax values observed in the tests. Earth pressure sensors were buried near the boreholes and the measured data were also compared to the results of numerical solutions. For the system involving an overlying layer of dense sandy gravel, it was conservative to use the Pmax calculation for uniform granular layers. However, for the system involving an overlying layer of loose sand, a reduction factor needed to be added; this was developed from a parametric study. Ground surface failure was less dramatic for these layered systems than seen previously for uniform sand.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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