Developing a Prototype for Determining Alternative Sources of Natural Gas Supply
Notice bibliographique
Résumé
ABSTRACT The emerging area of management and distribution of global energy resources presents new challenges to businesses, industries, and researchers. This paper describes a prototype used to evaluate the situation and to facilitate rapid decision-making for alternative natural gas supply sources given an unprecedented scenario. It provides a step-by-step approach to determining the best alternative based on cost and risk reduction. It incorporates data about the top exporters and importers of natural gas, their rank order, and the relationship between the importers and exporters of natural gas, as well as the factors guiding such relationships. Results of these relationships are incorporated into a database and examples are chosen to analyze for alternative selection. The solution obtained demonstrates the feasibility of developing an integrated system and its useful implementation for determining alternative sources of natural gas supplies given critical scenarios in the value chain. INTRODUCTION Despite its rapid growth in recent years, Liquefied natural gas (LNG) remains a relatively small contributor to world gas demand (under 7% of the total world gas demand in 2005) and even to total internationally traded gas, (about 22% of gas trade) according to the National Petroleum Council (2007). Pipeline gas still dominates international trade most notably supply to Western Europe from Russia, North Africa and Norway and supply to the US from Canada. With regards to regional LNG trade, the Pacific Basin and Asian markets almost double the size of the Atlantic Basin and Mediterranean markets. By end of 2010, LNG trade is expected to be more than 10 trillion cubic feet (tcf) annually from the recent 6.5 tcf, with the United States expecting most demand followed by Northern Europe, Japan, South Korea, China and India (BP, 2005). Although trade movement is lower in the Pacific, countries in this region supplied 59% of the global LNG market. In 2006 and 2007 LNG shipment rose by 11.8% and 7.3% respectively; which is in line with historical average considering increased shipments from Qatar and Nigeria (Riihl 2007 & BP 2008). Asia, recorded an incremental average of 10% in LNG imports with Japan and South Korea being the major importing nations (PRLog, 2007), while European imports rose by 20%. In 1995, there were eight LNG exporting countries and nine importing countries (Ogj, 2007). By 2007 the number has increased to 15 exporting countries and 17 importing countries. World trade in LNG reached a total of 211.1 billion cubic meters (bcm) in 2006, an increase of 11.7% on figures for the previous year, according to Cedigaz (2008). In 2002 only 23% of world gas consumption was imported and 26% ofthat was in the form of LNG (Jensen et al., 2004). Between 2000 and 2020 world demand is forecasted to grow by 1727bcm (IEA, 2002). In the same light the US energy information administration also predicts a similar growth of 54tcf between 2005 and 2030 (EIA, 2008). With the exception of Russia and other countries of Eurasia, natural gas production is expected to represent a significant portion for exports in the Mideast (Qatar) and Africa (Nigeria, Algeria, Egypt and Libya). Statement of the Problem The evolution of natural gas trade between Eurasia and its western neighbors cannot be cited without upheavals. In the past, gas importing countries feared an interruption in important gas supplies for a variety of reasons such as contract disputes between Algeria and its customers (Hayes, 2006), political unrest in Indonesia (von der Mehden & Lewis, 2006) and transit country risk such as in Ukraine and Belarus for Russian exports (Victor & Victor, 2006). In March 2008 disputes between Russia and Ukraine accompanied a reduction of Russian supply for 3 days, and Turkmenistan cut supplies to Iran citing technical issues with the pipeline and a breach of pricing contract (EIA, 2008). …
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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 | 0,001 |
| 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 ».