Wellsite Risk Management Improvement Including Human Factors
Bibliographic record
Abstract
Abstract In recent years many companies within the oil and gas industry have begun looking outward, to other high risk and highly reliably industries for benchmarking opportunities to improve safety performance. One of the realizations that has come from research on failures and this benchmarking is the need to improve our understanding of Human Factors and all the aspects that entails. The SPE is working on the publication of a technical report "Getting to Zero and Beyond: The Path Forward" that highlights the need for more work on Human Factors and there is a standing Technical Section on that area as well. With this in mind, a mid-size international oil and gas company operating in the Canadian oil sands (Company) recognized the need to further develop their operational and safety leadership in preparation for an upcoming well campaign. Early in 2017, a multi-day Wellsite Risk Management Improvement Workshop was conducted in advance of a summer Drilling and Completions campaign involving wells in the Alberta oil sands. The objective of the workshop was to provide wellsite leadership personnel with tools and techniques for improving the assessment and management of risk at the wellsite, including those arising from Human Factors and Human Error. The ultimate goal was to decrease the likelihood of at-risk behaviors in the performance of both routine and non-routine tasks. The workshop was conducted in Calgary and was attended by both Company and Contractor personnel. Some aspects of the workshop were challenging to the personnel attending, in particular those involving appropriate handling of Human Error and Human Failure. As the well campaign started up, site visits to the field were undertaken to assess uptake and continued implementation of the tools and techniques trained during the workshop, and to determine what additional coaching and guidance was needed. The first stage of the evaluation process entailed observing work being conducted on site over several days to get a sense of typical work practices. Examples of the full continuum of safe and at-risk behaviors were observed, both non-enabled and enabled. Further actions were developed and implemented to habituate the trained concepts. This paper will describe the training conducted, the monitoring and mentoring processes used and the overall findings and outcomes of the Wellsite Risk Management Improvement project.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".