A novel oil-in-water drilling mud formulated with extracts from Indian mango seed oil
Bibliographic record
Abstract
Abstract Drilling muds with less environmental impact are highly desired over conventional diesel-based mud systems, especially in light of the emerging strict environmental laws. In this article, a novel oil-in-water (O/W) emulsion drilling fluid formulated with a methyl ester extracted from Indian mango seed oil was evaluated. The effect of the weight percent of different constituents of the emulsion/suspension including the oil phase, bentonite, and polyanionic cellulose polymer on the rheology and the fluid loss was examined. The methyl ester oil phase/mud system displayed superior physical, chemical, rheological and filtration properties relative to the diesel and the mango seed oil. Eco-toxicity of the methyl ester and diesel (O/W) emulsion mud systems was assessed using the acute lethal concentration test. The Indian mango methyl ester (O/W) emulsion mud displayed much less impact on fish population. Flow characteristics collected from the flow model at 85 °C suggested excellent shear thinning behavior of the Indian mango methyl ester (IMME) (O/W) emulsion mud. Moreover, the IMME (O/W) emulsion displayed strong pseudoplastic behavior, an attractive feature in a drilling mud, with increasing clay content and polymer concentration. The methyl ester mud was thermally stable over a wide range of the constituent concentrations. Furthermore, a particle size analysis revealed that engineered drilling muds targeting suspension of particles with certain size range can be formulated by changing the volume fraction of the methyl ester in the mud system.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".