Upstream Onshore Oil and Gas Fatalities: A Review of OSHA’s Database and Strategic Direction for Reducing Fatal Incidents
Bibliographic record
Abstract
Abstract According to the OSHA database for the period from 1997 through 2003, one fatality occurred every 10 days in the U.S. upstream (E&P) oil and gas industry. To determine trends and provide insights into the safety failures, as well as potential interventions to eliminate the high frequency of fatal incidents, the seven years of OSHA data were reviewed. This data encompasses over 250 fatalities from the four principal SIC categories that comprise the onshore upstream oil & gas exploration and production industry. Data were sorted initially by region, well drilling or field servicing, rig type, and event. Further analysis was conducted by a diverse team of industry professionals, including representatives from operating companies, well drilling and servicing companies, and industry trade associations. Particular focus was directed at accident type, equipment type and well site location in an attempt to identify causal factors from the limited incident descriptions contained in the OSHA database. The resulting analysis showed nearly half of all fatalities (47%) resulted from "struck by" incidents; fires and explosions accounted for 16% while falls from heights accounted for another 14% of the fatalities. Fatality incident rates from year to year were strongly correlated to overall upstream industry activity level as represented by the U.S. rig count. This fatality data review provides oil and gas industry operating managers, safety professionals, trade associations and others a road map for targeted improvement programs and priorities for reducing onshore oil field-related fatalities.
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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 | 0.040 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.042 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".