SPE 154511

Intelligent wells can improve oil recovery, mitigat e risks and avoid unnecessary well intervention in petroleum fields. However, there is no consolidated methodology to ev aluate the applicability of intelligent wells and t o represent them in commercial simulators, which complicates the compar ison with conventional wells. Moreover, there are t wo main modes of operation of intelligent well valves, reactive and proactive; each one can provide different benefits. In general, proactive control seeks maximum oil recovery, but it requires larger computational effort and greater knowledge of the reservoir than the reactive control. This paper presents a comparison between different configurations of intelligent wel ls with proactive control and mode operation on/off: (1) five-spot configurat ion with conventional wells (producer and injectors ), (2) one intelligent producer and four conventional injectors, (3) one c onventional producer and four intelligent injectors and (4) one intelligent producer and four intelligent injectors, in order t o compare the different behaviors. The objective of this study is to evaluate the potential of proactive operation for each type of configuration and the benefits of the intelligen t i jectors and producer acting separately or together, considering the effe cts on production and costs of intelligent completi on. For this, a genetic algorithm was coupled to a commercial simulator to optimize the proactive control and to search the ma xi um net present value (NPV), determining the optimum operation cont r l for each valve. The case study consists of one het rogeneous reservoir model, light oil and three economic scena rios (pessimistic, probable and optimistic). Result s show that the use of intelligent injector and producer wells together, i n this case study, can increase of oil production a nd decrease of water production, although it may not be the most advanta geous alternative because of the higher investment. On he other hand, the configuration using only an intelligent producer we ll (lower investment) is capable of increasing oil recovery sufficiently, therefore making the best investment with intellige nt completion, in this case study. Introduction Nowadays, the intelligent wells (IW) have applicati on restricted to a few oil fields and in many of th em they are still used in the form of test or to try to solve some specific p roblems, despite having high intrinsic value becaus e of their greater operational flexibility, provided by sensors that c onstantly monitor the production data and valves ca pable of responding appropriately to the changes occurred during the li fetime production. This is due in large part by the fact that the IWs are more expensive than the conventional wells (CW), and bec ause the real benefits of this technology are still not clear, due to the lack of a consolidated methodology to present the advant ages of one completion over the other. One of the main difficulties presented in the use o f the IW is to find the configuration of wells that will bring the best technical and economical results, which will be onl y with intelligent injector wells, or only with int elligent producer wells, or even a combination of both. This difficulty is main ly because of the complexity of the problem, due to the high number of variables involved in the process. The complexity i s also due to the dependence among several paramete s r lated to the selection of production strategies. Thus, there are still many challenges to include an IW evaluation n a global strategy selection process. Another major difficulty is to o ptimize each valve of several wells in a field, whi ch makes the problem very complex, as the traditional optimization methods ca nnot solve the problem of feasible computational ti me. As a result, several studies attempted different methods to solve this p roblem, among them: simulated annealing (Kharghoria , 2002), conjugate gradient (Kharghoria et al., 2002; Yeten et al., 2002), calculation of gradients (Sarma et al., 2005, Van Essen et al., 2009), direct search (Emerick and Portella, 2007), ensembl -based method (Su et al., 2009), the augmented Lagrangian method with the Karush-Kuhn-Tucker conditions (Doublet et al., 2009), and the gradient-based method (Yeten t al., 2004). Although some of these studies have shown the benefits of one met hod over the other, there is not an optimization me thod to quickly and