Production Optimization With Intelligent Wells

Abstract

fax 01-972-952-9435. AbstractThis paper describes an implementation of method to optimize the production in intelligent wells varying the wells inflow control valves settings using an optimization algorithm coupled to commercial flow simulators. The optimization is based on direct search methods. The optimization algorithm was coupled with two different commercial flow simulators and has been applied in two real Brazilian offshore fields to quantify the benefits of intelligent wells over a base case with conventional completion. The first field has three horizontal wells, two producers and one water injector, completed in two zones totalizing six inflow control devices. In this case, different scenarios have been analyzed varying the downhole valves type -on-off and multi-position. The results have shown that the intelligent wells scenarios increased the recovery factor and reduced the production and injection of water when compared with the base case (conventional completion). The second field has fifteen wells -nine producers with binary valves and six water injectors with sixposition valves -producing and injecting in two or three zone totalizing 39 downhole valves to be optimized. In this case, the results have shown a significant increase of the expected cumulative oil production when compared with the base case.

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

“…By actively monitoring the liquid level, injector rates nearby can be increased or decreased to maintain a certain target level in the production well annulus. A future area of research is to coordinate injector action with pump-off action using variable stroke length and speed of rod pumping [123] to increase the reservoir NPV [124].…”

Assisted optimization method for comparison between conventional and intelligent producers considering uncertainties

“…Thus, many studies attempted different optimization methods to solve this problem, but most studies employed simple cases: simulated annealing (Kharghoria et al, 2002), conjugate gradient (Kharghoria et al, 2002;Yeten et al, 2002), gradient-based methods (Aitokhuehi and Durlofsky, 2005;Sarma et al, 2005;Van Essen et al, 2009;Yeten et al, 2004), direct search (Emerick and Portella, 2007), ensembles (Su, 2009), Lagrangian augmented method (Doublet et al, 2009), among others. Although some of these studies have shown certain advantages of one method over another, many of these are based on gradients, presenting difficulty in finding a global solution because a local solution can easily reach the stopping criteria.…”

Optimization of Proactive Control Valves of Producer and Injector Intelligent Wells under Economic Uncertainty

“…As an example of application in real fields, Emerick et al (2007) implemented an algorithm of direct search to optimize production in IW by varying the parameters of control valves through proactive control. The algorithm was coupled to a commercial simulator to study two real Brazilian offshore fields for search (Campos and Potiguar Basins) to quantify the benefits of IW in relation to the conventional completion.…”

“…As a result, several studies attempted different methods to solve this problem, 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), ensemble-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 et al, 2004). This difficulty is mainly because of the complexity of the problem, due to the high number of variables involved in the process.…”