Bibliographic record
Abstract
Abstract The oil and gas industry of today requires a social license to operate. As positive public perception diminishes, it demands that the industry adapt to a higher standard of operation to reduce its environmental footprint. There are systems that make it easy to achieve this and are able to fit on any rig, anywhere in the world. Drilling fluid losses total on average more than 5000 gallons per single well. Industry adaptation in Canada is 98% in directing the discharge of fluid during connections. Surprisingly, only 12% of Canadian drilling rigs have systems in place to capture and recycle directed fluid, even though drilling operators can drastically reduce mud costs and remediation expenses by capturing, and recycling fluid before it hits the ground. Common perception is that environmental stewardship and greenhouse gas reduction involves a prohibitive price tag, a misconception this paper will dispel. This paper illustrates the adverse effects of non-containment, introduces proactive technology available and presents several case studies demonstrating benefits of zero spill systems from a financial and environmental perspective. Widespread implementation of zero spill technology is crucial in elevating environmental stewardship, regaining a strong social license and improving profitability during capricious times in the market.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".