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Record W2757419778

Formulation and Evaluation of Synthetic Drilling Mud for Low Temperature Regions

2017· article· en· W2757419778 on OpenAlexaboutno aff
Adesina Fadairo

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDrilling fluidPetroleum engineeringRheologyDrillingOffshore drillingEnvironmental scienceSubmarine pipelineWaste managementPulp and paper industryEngineeringGeotechnical engineeringMaterials scienceMechanical engineeringComposite material
DOInot available

Abstract

fetched live from OpenAlex

The temperate world such American and Canada are extensively increasing environmental legislations against oil-based muds and increasing exploratory activities in the offshore, it imperative to develop an oil based drilling muds that have resistance to the weather and relatively stable rheological properties at low temperature of -5oC to 20oC. This study investigates the use of non-edible algae oil to formulate ethyl biodiesel as based fluid for drilling mud that can perform the same function as convectional oil based drilling fluid and as well comply with the HSE (Health, safety and environment) standard in the temperate region and offshore environment. Experimental tests were performed at temperature condition of -5oC to 20oC on the synthetic ethyl biodiesel oil based mud samples so as to evaluate the rheological properties of the drilling mud formulations. The synthetic oil based was obtained from offshore drilling company and was used as control experiment. The following tests were run on these muds including; viscosity pH, gel strength, density and filtration tests at varied temperature and constant pressure and toxicity test to determine their usability in the defined 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)0.001
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 score0.111
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.203
GPT teacher head0.504
Teacher spread0.301 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicDrilling and Well EngineeringFrench-language works237,207