Accident modeling and risk assessment framework for safety critical decision-making: application to deepwater drilling operation
Bibliographic record
Abstract
Rising global energy demand is encouraging oil companies to invest in deepwater drilling. However, there are numerous engineering and safety challenges involved in this activity. The BP Deepwater Horizon accident (Macondo well blowout) has raised serious concerns about the safety of deepwater drilling. The major reasons for such a catastrophic blowout event are the lack of continuous assessment of risk and the lack of risk-based decision making to take timely and adequate preventive actions. The present work proposes an accident modeling and risk assessment framework based on accident precursors (early warnings). This framework uses the system hazard identification, prediction and prevention methodology to model the unwanted situation. The proposed risk assessment framework generates results that can be used to: (1) analyze the dynamic performance of safety barriers, (2) analyze the probability of occurrence of different severity levels, (3) analyze the dynamic risk profile of different severity levels and the aggregated risk profile, and (4) help to make safety-critical decisions based on aggregated risk profile. The present work provides an assessment of offshore deepwater drilling risk assessment and a basis to make timely and precise safety critical decisions. The risk assessment methodology is demonstrated on the Macondo well blowout accident. This case study highlighted the applicability and advantages of using the proposed method in drilling operations.
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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 | 0.007 | 0.024 |
| 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 | 0.001 |
| 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".