Real Time Optimization of a Water Flood Reservoir
Notice bibliographique
Résumé
Abstract Due to the rising rate of demand for hydrocarbon fuels as well as descending trends in reserve discovery it is of increasing necessity to produce oil and gas fields more efficiently and economically. In this scenario, the use of secondary recovery processes, i.e. waterflooding, is becoming more and more imperative. Also the use of smart wells, which are equipped with data acquisition, monitoring and controlling instruments, is also being proposed as efficient means of maximizing ultimate recovery. In this work a methodology to optimize petroleum production or net present value (NPV) from a waterflood reservoir by controlling operational parameters of the production and injection wells with the use of smart well technology is developed. The optimization procedure involves maximizing the objective function expressed in terms of cumulative oil produced or NPV from a waterflood reservoir by adjusting a set of operational parameters expressed as the production wells flow rates and injector wells bottomhole pressure. The production and injector wells in this reservoir are smart wells whose downhole chokes are automatically adjusted to meet certain optimal requirements. Production strategies can be derived from numerical optimization analyses, the solutions for which are most frequently based on optimal control theory. The approach used in this work considers a simpler alternative to the optimal control approach: a moving-horizon formulation coupled to a heuristic algorithm. This work also provides a validation of the optimization methodology proposed through two heterogeneous and one homogeneous reservoir models with five-spot pattern waterflood schemes. The results of the optimization methodology proposed in this work show an increase in cumulative production and in NPV with respect to the base case. Introduction The increasing availability of intelligent and smart wells as well as integrated technologies permit practical acquisition of production information from every well and dissemination for use by everyone within an organization and allow overall field optimization of operations in real time to happen. Instrumented wells with real-time downhole measurements and remotely activated valves, public wireless communications, database and interface tools provide key and powerful developments for reservoir management and production optimization to gas and oil producers. The concept of real-time optimization (RTO) is not new and has been practiced in some areas of Petroleum engineering like drilling or production operations for quite same time but due to new technologies related to data measurement and processing the extent to which RTO is now feasible has increased tremendously. A well-established definition for RTO is that "it is a process of measure-calculate-control cycle at a frequency, which maintains the system's optimal operating conditions within the time-constant constraints of the system". Based on this definition it is possible to conclude that the concept of realtime optimization requires the use of measurements of a process at a certain frequency to enable decision-making at the same frequency. This whole procedure would ideally be performed continuously, by collecting all available data in real time and also continuously solving all-encompassing optimization problems.
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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 | 0,000 | 0,001 |
| 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 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».