Learning’s from Applying the API Process Safety Incidents (PSI) Metric to Upstream Operations
Bibliographic record
Abstract
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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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".