Reservoir Performance Assessment Based on Intelligent Well Technology
The main challenge facing the oil industry is to reduce development costs while accelerating recovery with maximizing reserves. One of the key enabling technologies in this area is intelligent well completions. Intelligent well technology (IWT) is a relatively new technology that has been adopted by...
Gespeichert in:
Veröffentlicht in: | Journal of chemical and petroleum engineering (Online) 2016-06, Vol.50 (1), p.69-78 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | The main challenge facing the oil industry is to reduce development costs while accelerating recovery with maximizing reserves. One of the key enabling technologies in this area is intelligent well completions. Intelligent well technology (IWT) is a relatively new technology that has been adopted by many operators in recent years to improve oil and gas recovery. Intelligent well completions employ Annular Flow Control Valves (AFCVs) to balance the production profile along the length of the well completion by splitting it into two (or more) sections. The aim of intelligent wells is to optimize the production (Delaying the gas and water breakthrough and decreasing water production). The energy that moves crude oil and natural gas from the subsurface rock to the production well is called the reservoir drive [1]. These energies because of their different mechanisms, have different effects on reservoir production. In spite of advancement in Intelligent Well Technology, the effect of intelligent well on reservoir drive mechanisms under different reservoir characterization have not been well addressed. In this paper, six conceptual models of oil reservoir have been built and different production scenarios have been discussed. Based on the objective function, scenarios will be selected and will compare with a conventional scenario and decide whether to use smart well in these models or not. |
---|---|
ISSN: | 2423-673X 2423-6721 |
DOI: | 10.22059/jchpe.2016.57838 |