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Record W4312404743 · doi:10.1115/omae2022-79290

Plug Cementing Stability

2022· article· en· W4312404743 on OpenAlexaffabout
Anastasia Vogl, Nile Waldal, Parisa Sarmadi, Adam Fershtman, Rodrigo S. Mitishita, I.A. Frigaard

Bibliographic record

VenueVolume 10: Petroleum Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpark plugGeologyDrilling fluidMechanicsBuoyancyGeotechnical engineeringFluid dynamicsPetroleum engineeringFlow (mathematics)SlurryDrillingMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Off bottom plugs are set in both well drilling (kick-off plugs) and in well decommissioning (abandonment plugs). In both cases a dense fluid (cement slurry) is placed over less dense well-bore fluids. In the case of kick-off plugs, viscous pills are commonly used, which can help stabilize this mechanically unstable situation. Abandonment plugs however are often set on top of the freshwater that is used to clean the well prior to abandonment. This is the current practice for many wells in Northwestern Canada. It is a mystery how such cement plugs are able to stay in place for a time sufficient for the cement to thicken and hydrate, but field evidence suggests they do. In this paper we explore the mechanically unstable scenario of a heavy yield-stress fluid placed on top of a less dense Newtonian fluid in a cylindrical pipe, dimensionally scaled to represent an off-bottom plug. We present details of the experimental setup and its calibration. We then explore the buoyancy-driven exchange flows that occur in transitional parametric regimes between flow and no-flow states, by using both computer modelling and physical experimentation. 3D numerical simulation, using a Volume of fluid method, is carried out to capture the interface between the fluids. The 3D model provides us with a more detailed analysis of the concentration and velocity profiles, along with comparisons to snapshots of the experimental results. The aim is to explore the phenomenology of these unstable flows and be able to estimate timescales of the destabilization. Preliminary results are presented.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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
Published2022
Admission routes2
Has abstractyes

Explore more

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