Learning’s from Applying the API Process Safety Incidents (PSI) Metric to Upstream Operations
Notice bibliographique
Résumé
Learning's from Applying the API Process Safety Incidents (PSI) Metric to Upstream Operations David Kehn; David Kehn Chevron Global Upstream and Gas and Energy Technology Company Search for other works by this author on: This Site Google Scholar Ben Wischmeier Ben Wischmeier Chevron Global Upstream and Gas and Energy Technology Company Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Conference on Health, Safety and Environment in Oil and Gas Exploration and Production, Rio de Janeiro, Brazil, April 2010. Paper Number: SPE-127015-MS https://doi.org/10.2118/127015-MS Published: April 12 2010 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Get Permissions Search Site Citation Kehn, David , and Ben Wischmeier. "Learning's from Applying the API Process Safety Incidents (PSI) Metric to Upstream Operations." Paper presented at the SPE International Conference on Health, Safety and Environment in Oil and Gas Exploration and Production, Rio de Janeiro, Brazil, April 2010. doi: https://doi.org/10.2118/127015-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Conference and Exhibition on Health, Safety, Environment, and Sustainability Search Advanced Search Abstract Catastrophic incidents in the oil and gas industry have the potential to result in serious injury or death to the workers, the public and/or harm to the environment. The desirability of "dual assurance" lagging and leading process safety metrics was strongly communicated in the BP US Refineries Independent Safety Review Panel ("Baker Panel")i and the U.S. Chemical Safety Board ii recommendations on the 2005 BP Texas City refinery explosion. The objectives of industry metrics were to provide an indicator to monitor performance and to set process safety performance targets, drive continuous improvement, and provide a mechanism for useful industry benchmarking.The significant industry guidance for process safety performance monitoring includes:UK Health and Safety Executive: "Step-by-Step Guide to Developing Process Safety Performance Indicators, HSG254", Sudbury, Suffolk, UK, 2006 [Ref. iii ]Center for Chemical Process Safety (CCPS): "Process Safety Leading and Lagging Metrics", American Institute of Chemical Engineers, New York, 2008 [Ref. iv ]American Petroleum Institute: "API Guide to Report Process Safety Incidents, Version 1.2", Washington, D.C. 2008 [Ref. v ]International Association of Oil & Gas Producers (OGP): "Asset Integrity – the key to managing major incident risks", Report 415, London, UK, 2008 [Ref. vi ]In 2007, one company (the "Company") globally adopted a Loss Of Containment (LOC) metric and an enhanced vapor release metric titled Inadvertent Release of Hazardous Vapor/gas (IRHV) based upon the thresholds and definitions within API Guide [Ref. v]. API Guide [Ref. v] was primarily written to facilitate benchmarking of process safety performance among refineries and petrochemical plants. Keywords: process safety incident, normalization, metric, process safety, process safety performance target, threshold, performance indicator, operation, hsse reporting, benchmarking Subjects: HSSE & Social Responsibility Management, Safety, Strategic Planning and Management, HSSE reporting, Benchmarking and performance indicators Copyright 2010, Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».