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Record W4377832522 · doi:10.2118/214577-pa

Curing Hydrodynamic Instability During Balanced Plug Cementing

2023· article· en· W4377832522 on OpenAlexafffund
K. Harestad, Ida Karimfazli, Abdallah Ghazal, M. Harestad, Arild Saasen

Bibliographic record

VenueSPE Drilling & Completion · 2023
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConcordia UniversityPetroleum Technology Alliance CanadaCompute Canada
KeywordsInstabilitySpark plugMechanicsCementPerpendicularPlug flowRheologyViscosityGeologyGeotechnical engineeringMaterials scienceEngineeringMechanical engineeringMathematicsPhysicsComposite materialGeometry

Abstract

fetched live from OpenAlex

Summary It is shown how the flow from pumping cement through an open-ended pipe very quickly changes direction and the cement flows upward. This rapid change in flow direction indicates that a diverter tool, which leads the cement slurry perpendicularly out of a closed-ended pipe, does not have any function. The placement of a balanced plug is feasible. However, a high-density fluid above a lighter fluid is not stable. The phenomenon is known as Rayleigh-Taylor instability. In principle, to be reasonably stable, the interface must be horizontal. The longer the interface is, the more unstable is this case. Thus, it is difficult, or sometimes impossible, to create a stable situation in a deviated well section, especially if the well section diameter is large. Observations show that it is possible to modify density differences, thickening time, and viscosity differences such that the success rate can be between 40% and 60%. Using a floatable cement foundation tool, this success rate increased to more than 95% in North Sea applications. The use of such a tool is described, and its performance is justified by a numerical analysis of cement flow.

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 score0.052
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
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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