Bioremediation and Ecotoxicity of Drilling Fluids Used for Land-based Drilling
Bibliographic record
Abstract
Concerns about the environmental and health risks associated with drilling wastes generated with diesel oilbased muds stimulated the consideration of alternative fluids for the production of drilling muds. Thus, research was conducted at the University of Calgary to determine the biodegradation and ecotoxicity of several fluid types including linear alpha olefin, isomerized olefin, mineral oil, isomerized paraffin and diesel oil. Each fluid was added to loam subsoil at a rate of 2 g/100 g dwt soil and microbial respiration was monitored for 3 months until activity had stabilized. Total extractable hydrocarbons (C11-C60) and toxicity were measured immediately following fluid application and again after 3 months bioremediation. Toxicity bioassays included seed germination and root elongation by lettuce, canola and barley; earthworm survival; and luminescent bacteria response (Microtox®). Olefins demonstrated the fastest and most complete (90-96%) biodegradation and the least ecotoxicity, while mineral oil and iso-paraffin degraded more slowly (39-45%) and developed extreme toxicity during bioremediation. Although 71% of diesel fluid disappeared through volatilization and biodegradation, extreme toxicity persisted after bioremediation. A separate study determined that olefins and paraffin fluids degraded more rapidly in organic loam than in clay soil. In field performance tests, olefin-based drilling muds delivered excellent drilling performance. Currently, studies are in progress to evaluate the remediation potential of olefin mud drilling wastes by land farming and co-composting techniques.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".