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Record W2093600899 · doi:10.2118/165398-ms

Flammability Limits of Diesel and air Mixture for use in Underbalanced Drilling

2013· article· en· W2093600899 on OpenAlexafffund
S. A. Mehta, R.G. Moore, M.G. Ursenbach

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2013
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsDiesel fuelUnderbalanced drillingFlammable liquidFlammabilityEnvironmental scienceIgnition systemPetroleum engineeringWaste managementDrilling fluidMaterials scienceDrillingEngineeringComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract In an underbalanced drilling process, using diesel as an oleic additive to drilling mud increases the chance of explosion if the drilling fluids mix with natural gas or injected air and reach the flammable limits. Although some investigations about flammability limits of drilling fluids have been conducted on the basis of diesel components, further laboratory experimental study needs to be done. Initially, the properties of the two diesel samples, including heating value, molecular weight, densities and simulated distillation characteristics are measured and compared. Then, an experimental study is conducted to investigate the flammability limits of two diesel fuel samples (diesel A and B) at varied conditions of elevated temperature and pressure using a specially designed apparatus. Based on the experimental results, generally, diesel B exhibits wider flammability range than diesel A. However, diesel A is easier to ignite than diesel B. In addition, the effect of pressure on the flammability limit of diesel B is more profound compared to diesel A. This research can help diesel selection for specific conditions in the field. Moreover, it is suggested that, to maintain a low probability of ignition or explosion, the minimum diesel concentration of drilling fluid is 4.29% for both diesels under certain 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.761

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 head0.017
GPT teacher head0.188
Teacher spread0.171 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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