Bioremediation Study of Olefins, Mineral Oils, Iso-Paraffin Fluids and Diesel Oils Used for Land-based Drilling
Bibliographic record
Abstract
Abstract Increasing concerns over health and safety, as well as environmental impacts of diesel oil-based muds in land-based drilling applications, has prompted a search for safer drilling mud base fluids. There are two challenges. First, the drilling fluid must satisfy technical requirements. Second, it must reduce environmental health and safety risks. This paper summarizes research aimed at finding a synthetic base fluid that satisfies both requirements. Laboratory studies evaluated the degradability of diesel and synthetic base fluids, and the toxicities of the same fluids to a range of terrestrial flora and fauna. More specifically, biodegradation, seed germination and root elongation of two plant types, earthworm survival, and response of bioluminescent bacteria (Microtox™) were determined in a typical landfarm receiving soil containing the test fluids. Olefins demonstrated the fastest biodegradation and lowest toxicity after bioremediation, whilst iso-paraffin fluids and mineral oil degraded less readily and developed extreme toxicity during the three-month bioremediation period. Although 71% of the diesel oil disappeared through volatilization and biodegradation, it remained extremely toxic after bioremediation.
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 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) | 0.001 | 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".