Notice bibliographique
Résumé
Abstract Most future input prices (unit costs) in the upstream petroleum industry are not known with certainty. This can have important effects on asset value and management. One criticism of most past petroleum industry applications of real options analysis (ROA) is that they have neglected to consider the effects of this type of uncertainty. This paper explores a few of the issues that arise in discounted cash-flow (DCF) and ROA asset valuations, if a specific type of input price uncertainty is considered. In particular, we look at the effects of the correlation of unit costs with output petroleum prices. Unit drilling costs, for example, tend to be high/low if petroleum prices are high/low or have gone up/down unexpectedly. The first "rent" effect occurs if the market for drilling services is not completely competitive. The second "quasi-rent" effect occurs if it takes time for the suppliers of these services to adjust the amounts they supply in the face of unexpected changes in demand. We first examine, using both ROA and DCF methods of analysis, some relatively simple assets where the asset cash-flow dependence on input and output prices, and of the input prices on the output prices, is linear. Two points are made. First, for a set of given input price expectations, DCF estimates of the value of these assets will be independent of the level of input price uncertainty, unless the discount rate is adjusted to reflect the change in risk. ROA estimates of value will automatically pick up the effects of different levels of risk. If the unit costs are generally correlated with the macro-economy (as will be the case with a rent effect and most quasirent effects, if the output prices are so correlated), for a set of given input price expectations, a greater level of input price uncertainty would decrease the estimate of the value of the costs in the asset cash-flows, because of the greater risk discounting that it induces. This would increase the ROA estimate of the value of the asset as a whole. Second, it has been conjectured that a greater similarity between revenue and cost uncertainty, by making revenue and cost discounting more similar in ROA analyses, will tend to mitigate some of the differences between the structure of ROA and DCF value estimates. This would decrease the benefits of a shift from the use of DCF methods of analysis to the use of ROA. We analyse variations of two previously published examples of analyses of assets with linear cash-flows to explore these matters. We then examine an asset where there are non-linearities in the dependence of asset cash-flows on prices. These may be due to non-linear price models, non-linear taxes or flexibility in project management. In the specific asset we analyse, which is also based on a previously published example, the non-linearities arise from flexibility. In this case, we find some similarities and some differences in structure of the effects of input price uncertainty on asset value. The example is of an oil field in the final year of its development lease, where there is an option to sanction development immediately, or to appraise or wait for another year and decide then between development or abandonment. At sanction, there is a production capacity choice to be made, and after sanction there is an annual abandonment option. Again, two points are made. First, if the input price expectations are roughly the same, input price uncertainty of the simplest kind (stemming from a linear "rent" effect) again increases ROA estimates of asset value and leaves the DCF value estimates roughly the same. Second, rather than mitigating the differences between DCF methods of analysis and ROA, cost uncertainty in this example accentuates the losses from using DCF methods if ROA should be used. Unit cost uncertainty interacts with the differences in the methods of value estimation to make the management policies suggested by DCF and ROA more different in the presence of this uncertainty. All of this suggests that the industry should keep track of the uncertainty in its input prices, and their correlation with output petroleum prices and with each other. This paper shows how this would be useful for project analysis. It would also be useful in the development of appropriate risk management policies.
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 ».