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Record W4313007395 · doi:10.1115/omae2022-78848

Viscoplastic Fluid Placements in a Confined Geometry With Applications in the Dump Bailing Method in the Plug and Abandonment of Oil and Gas Wells

2022· article· en· W4313007395 on OpenAlexaff
Soheil Akbari, Seyed Mohammad Taghavi

Bibliographic record

VenueVolume 10: Petroleum Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSpark plugCasingPetroleum engineeringAquiferGeotechnical engineeringOil wellGeologyViscoplasticityDrilling fluidMaterials scienceMechanicsEngineeringGroundwaterMechanical engineeringDrillingStructural engineeringMetallurgyFinite element method

Abstract

fetched live from OpenAlex

Abstract The plug and abandonment (P&A) operations of oil and gas wells are conducted by placing cement plugs at crucial positions, to preserve the atmosphere and underground water aquifers from the oil and gas migrations. The cement plug should be placed in the wellbore with minimum mixing with the in-place fluid. There are several methods of the cement placement in P&A operations, among which the dump bailing method is one of the most common ones. In this technique, a relatively small amount of cement slurry is released on a bridge plug inside the well and it replaces the in-place wellbore fluids. The dynamics of such a fluid placement is governed by several parameters. In this study, the influence of the yield stress of the injected viscoplastic fluid is analyzed. To do so, the injection of a viscoplastic fluid with low yield stresses into an inclined closed-end pipe (representative of well casing) filled with a light Newtonian fluid is examined. The results show that increasing the yield stress of the injection fluid results in suppressing the mixing between the fluids. The outcomes of this study can be used for enhancing the cementing process in the P&A of the oil and gas wells.

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: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.003
GPT teacher head0.192
Teacher spread0.188 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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