Flammability Limits of Diesel and air Mixture for use in Underbalanced Drilling
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".