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Record W2593544023 · doi:10.11113/mjfas.v12n3.423

The application of MWCNT to enhance the rheological behavior of drilling fluids at high temperature

2017· article· en· W2593544023 on OpenAlexaff
Abdul Razak Ismail, Wan Rosli Wan Sulaiman, Mohd Zaidi Jaafar, Adnan Aftab, A. A. Razi, Z. H. Ibupoto

Bibliographic record

VenueMalaysian Journal of Fundamental and Applied Sciences · 2017
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsEngineering Link (Canada)
FundersUniversiti Teknologi MalaysiaMinistry of Higher Education, Malaysia
KeywordsDrilling fluidRheologyDrillingViscosityPetroleum engineeringMaterials scienceVolume (thermodynamics)Composite materialGeologyMetallurgyThermodynamics

Abstract

fetched live from OpenAlex

Drilling fluid is the key component to drill oil and gas wells. The rheological behavior of drilling fluid will be affected when drilling deep wells especially at high temperature and high pressure reservoir. This research was conducted to study the effect of the nanoparticles over the rheological properties of the drilling fluid when aging at high temperature condition. Several drilling fluids were prepared using synthetic based fluids (Sarapar and Saraline) to study the effect of multi-walled carbon nanotube (MWCNT) at different concentrations. The rheological properties of drilling fluid were analyzed after aging at 250 °F and 350 °F for 16 hours. The results revealed that the addition of MWCNT improved the 10-sec gel strength by 33% and filtrate loss volume was reduced to 10% after aging at 250°F in Saraline drilling fluid. Moreover, the plastic viscosity of Saraline and Sarapar drilling fluid after addition of MWCNT was enhanced by 6% and 27% at 350 °F. Filtrate loss volume of Sarapar drilling fluid was reduced by 19 % after aging at 250 °F for 16 hours. The overall results showed that the addition of MWCNT into the drilling fluid have slightly improved the rheological properties of drilling fluids under high temperature conditions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.349

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 head0.008
GPT teacher head0.238
Teacher spread0.231 · 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 designBench or experimental
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

Citations22
Published2017
Admission routes1
Has abstractyes

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