Notice bibliographique
Résumé
Abstract Water is becoming an increasingly important issue in the world and particularly for the petroleum industry. The purpose of this paper is to outline some of the challenges before the petroleum industry and their need to play a key role to ensure water sustainability. As an oil and gas industry, we are both producers and consumers of water. In the future, it may become an important license—to—operate issue. On the upstream side, we generally produce more water than oil (worldwide about three times more). Historically it has been a nuisance by-product that we pay to dispose of. With environmental regulations tightening and as we pursue more Enhance Oil Recovery (EOR) projects, water supply and quality cannot be overlooked. On the downstream side of the business, we are primarily a user of water — both as process water in the desalter units and as cooling water. In some communities we do business at, we are already starting to compete with the domestic and agriculture users of water. Therefore, we must focus on water much more than we have in the past. ConocoPhillips Global Water Sustainability Center in Doha (Qatar) will coordinate our effort to develop and evaluate innovative solutions. Any solutions that benefit ConocoPhillips (COP) will also benefit Qatar Petroleum (QP) and the industry in general. Introduction Two third of our planet is mainly water and 97.5% of it is salty (sea water); only 2.5% is fresh water and not all of it can be used for direct human uses. From the 2.5% of fresh water, approximately 70% is frozen as glaciers or polar ice; leaving only less than 1% of the total volume of fresh water accessible for human uses (e.g. rivers, lakes and groundwater) and most of the times, water sources are not clean enough.(1) Moreover, the availability of fresh water in the world is not evenly distributed. In places like deserts, the rainfall is very low and the water sources are scarce, so new water treatment strategies as well as water conservation programs are required. Water uses can be classified in three main categories: agriculture, industrial and domestic. Around the world, on average, 8% of water is used for domestic, 22% is used for industrial applications, and 70% is used for agriculture. However, the developed world uses more water for industries than for agriculture as shown in Figure 1. (2)
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 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 | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».