Continuous Green House Gas Emission Surveillance, Reporting and Control
Notice bibliographique
Résumé
Abstract The purpose of this paper is to document Shell's experiences and learnings inthe effort to better track and reduce Green House Gases (GHG) and improveEnergy Efficiency in our downstream manufacturing and upstream productionoperations. The paper is based on Case Studies from various Operating Units inShell upstream and downstream operations, as well as outlining furtherdevelopment plans. In Shell operations we seek to minimize GHG emissions by continuouslymonitoring, displaying and reporting associated Key Performance Indicators(KPI's) and quickly alerting operators of changes to trigger remedialintervention. Reduction of GHG emission is also achieved by improving crossvalidated and mass balanced tracking of our process streams. This ensures thatmanufacturing and production processes are operated efficiently andtransparently. Continuous GHG monitoring also allows automatic compilation of emissions bysource which can then be automatically reported as part of the normal dailyreporting cycle. The resulting emissions figures and associated KPI's are thenprominently displayed in the Daily Production Report. The daily emissionstotals are also stored and trended to flag more subtle and/or gradual changes. In this way GHG emissions data and performance information are made availableto operations staff and management to facilitate awareness and correctiveactions when appropriate. Introduction By 2050 the world's energy demand is likely to double - yet more than halfthe energy we generate every day is wasted. At the same time the world isbecoming increasingly more carbon constrained and in order to counter thesechallenges, Shell has defined a number of strategic pathways as part of theiroverall GHG management strategy and policy. The first strategic pathwayfocusses on energy efficiency in our own operations, both for existing and newassets. Energy efficiency is regarded within Shell as good business, yieldingoften attractive and immediate business value, while also providing improvedrobustness in the sustainability and profitability of our operations, especially in a carbon constrained world. The management process starts with understanding and measuring energyconsumption and GHG emissions and embedding this in our daily decision makingprocess while also using the "CO2 lens" for driving and validating the designchoices in our new assets. In order to do this successfully, real-timemeasurement of the key process parameters is required. This will provide theoperator with instant feedback of his process / operational choices re theimpact on energy intensity and GHG emissions. It can be compared with the fuelefficiency indicator in modern cars. By providing this feedback, it stimulatesbehavioural change and positions energy efficiency as a core value to beoptimised using daily operations. The pupose of this paper is to describe a number of case studies as arepresentative subset of our activities, and describe how real-time datameasurement and surveillance can be used to support our drive for operationalexcellence in energy efficiency, thereby focussing on the following of ourglobal businesses:Upstream E&P European operations - derived from real time processmeasurements;Downstream assets - derived from real time process measurements;North American mid-stream operations - derived from a combination ofreal time process measurements and valve flow estimatesInternational upstream E&P assets - derived from a combination ofwell gas flow estimates and real time process measurements Note, Shell is also very active in the area of CO2 sequestration - this outwiththe scope of this paper and is described elsewhere (Ref. 1)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».