Revisiting past refinery accidents from a human reliability analysis perspective: The BP Texas City and the Chevron Richmond accidents
Bibliographic record
Abstract
Despite the oil industry's efforts in improving safety, it still presents a high rate of serious accidents, many involving human failure events (HFE), which can be identified, modelled, and quantified through human reliability analysis (HRA). The oil industry commonly analyzes process safety by focusing on technical barriers, and thus it could benefit from HRA. Phoenix methodology is an HRA method that uses a human response model and relates the crew failures modes (CFM) to performance influencing factors (PIFs). Based on Phoenix CFMs and PIFs, two refinery accidents, the BP Texas City (2005) and the Chevron Richmond (2012), are analyzed in this paper. The analysis consists of the construction of the accident timeline; identification of the HFEs and assigning them to appropriate CFMs; and, finally analysis of the PIFs. The analysis helped better understand how the operators responded to an abnormal condition of the process, and why they took the actions they did, investigating the contribution of human error to the accidents. The assessment of the role human error played in these accidents is a major contribution to the understanding of why they happened, and a key information to avoid the same happening again in the future. Moreover, the features and limitations of the application of Phoenix HRA, which was developed based mainly on nuclear power plant operations, to Oil Refinery operation scenarios, are discussed and evaluated. This article provides insights on value of investigating the potential impact of human error in the Petroleum Industry accidents.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".