Wellsite Risk Management Improvement Including Human Factors
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».