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Record W2167990242 · doi:10.1139/t11-065

Optimization of perforation drilling for mitigating punch-through in multi-layered clays

2011· article· en· W2167990242 on OpenAlexvenueno aff
Muhammad Shazzad Hossain, Mark Cassidy, Rachel Baker, Mark Randolph

Bibliographic record

VenueCanadian Geotechnical Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersAustralian Research CouncilGovernment of Western AustraliaAustralian Government
KeywordsDrillingCentrifugeSubmarine pipelinePerforationGeologyGeotechnical engineeringDrilling rigOffshore drillingSeabedPetroleum engineeringMining engineeringEngineering

Abstract

fetched live from OpenAlex

“Spudcan” foundations for mobile drilling rigs continue to exhibit a high failure rate in the offshore oil and gas industry. The more frequent use of larger jack-ups in highly stratified regions, such as the Sunda Shelf in Southeast Asia, contributes to this concerning increase in “punch-through” incidents, which can lead to buckling of a leg or even toppling of the rig. An industry practice known as “perforation drilling” is sometimes used to mitigate the punch-through risk in layered clays, extracting soil from the upper strong layer before the jack-up is installed. This paper reports results from centrifuge model tests exploring the efficiency of perforation drilling. The soil conditions tested simulate offshore strength profiles that have reported punch-through failures. An experimental method for “drilling” sites in an enhanced gravity centrifuge environment was developed and the installation responses of model spudcan foundations penetrating through multi-layered clays with interbedded stiff layers were recorded. The experimental results show that the removal of soil inside the spudcan perimeter, with an area of 9% perforated, eliminated rapid leg run and severe punch-through on the two- and four-layer seabed profiles tested. This confirms the effectiveness of perforation drilling and indicates how the offshore drilling plan may be optimized.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.637

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 integrity0.0000.000
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 head0.031
GPT teacher head0.222
Teacher spread0.191 · 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
GenreMethods

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

Citations17
Published2011
Admission routes1
Has abstractyes

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