A Probabilistic Economic Approach to Optimize Field Development and Maximize Asset Value
Notice bibliographique
Résumé
Abstract Field development optimization is one of the most important and complex tasks in the petroleum industry since technical and economic issues should be considered to maximize an asset value. Usually field development plans are optimized first, under a deterministic or probabilistic approach, by numerical simulation or other engineering tools, and then, a base case model served for economic assessment, with some sensitivity analysis. Complexity in field development optimization is based on uncertainty sources which often arise at the same time, such as strong correlation among oil prices and technical recoverable volumes, development wells, CAPEX and OPEX. Then, uncertainty and dependency between model inputs are not easy to be modeled by conventional tools. A new probabilistic tool has been proposed to integrate technical recoverable volumes estimation, production performance prediction and economic assessment. Hydrocarbon recoverable volumes are estimated using a volumetric equation set up to run Monte Carlo simulation; thousands of realizations are then captured by the model to build an expectation curve. In addition, type curves from analogous fields and conceptual simulation studies are incorporated to obtain the probabilistic distribution of the parameters that will allow us to predict the production performance of a field under a stochastic approach. Finally, all the output production profiles are integrated to a probabilistic cash flow model based on real options, to assess at the same time, the uncertainty placed on oil & gas prices, CAPEX, OPEX, tax royalties and other economic inputs over profitability indicators such as NPV, IRR, pay out and investment/profitability rate. The use of real options will allow us to quantify the value of important flexibilities normally neglected in a typical cash flow analysis such as the value of waiting to develop the lease, the possibility of early development, and the abandonment of the project. To achieve this objective, we will use computational techniques such as finite difference and Monte Carlo simulations considering two price models for oil and gas: Geometric Brownian Motion and Mean Reverting Model. The model was applied to a remote Peruvian oilfield suspected to be under water drive mechanism to optimize its development strategy in early times, by finding the most suitable number of development wells according to field size, and selecting the optimal production rates, increasing recovery factor and delaying water breakthrough, variables that eventually would contribute with maximizing asset value. In addition, the model was capable to test and identify simultaneously the most sensitive inputs affecting recoverable volumes and asset value, as well as, to account for the risk expressed in expectation curves of technical recoverable volumes, as well as, the expected monetary value.
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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 | 0,000 |
| 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 ».