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Record W2066800780 · doi:10.2118/132015-ms

Effective Ways to Avoid Barite Sag and Technologies to Predict Sag in HPHT and Deviated Wells

2010· article· en· W2066800780 on OpenAlexaff
Mohammad Reza Amighi, Khalil Shahbazi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSlumpingDrilling fluidSettlingGeologyWell controlPetroleum engineeringLost circulationDrillingAnnulus (botany)Geotechnical engineeringMaterials scienceEngineeringGeomorphologyComposite material

Abstract

fetched live from OpenAlex

Abstract Barite Sag is an oilfield term used to describe significant density variations while circulating bottoms up after a trip, logging run or other operations that require the mud to remain static for an extended period of time. Sag is caused by static and/or dynamic settling followed by slumping of the weighted material. Static sag, as the name suggests is caused when circulating is stopped for an extended period of time, and the weighting agents begin to settle under the influence of gravity. Due to slumping, dynamic sag occurs frequently in inclined holes. However, the problem has been exacerbated by the increased frequently of high-angle wells with the associated increase in particle settling rate which occurs in an inclined fluid column. The settling of solids is enhanced by convective currents created by density differences in the fluid across the annulus cross section. This effect is frequently referred to as Boycott effect. This paper presents the most effective ways to avoid the occurrence of barite sag in high-pressure high temperature (HPHT) drilling operations, and in high-angle wells. The ways such as replacing barite by other weighted materials such as Ilmenite or Manganese tetraoxide. Furthermore, it is suggested to use a cesium formate based drilling fluid or polymer-coated ultra-fine barite for drilling HPHT wells. Moreover, different technologies to predict dynamic sag such as, flow loop tests and modified rotational viscometer tests, are introduced.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.602

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 head0.002
GPT teacher head0.165
Teacher spread0.163 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

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