Smart (or intelligent) well is a well equipped with downhole sensors and downhole valves to control fluid inflow from the separated perforated intervals towards well, in real-time mode and without well interventions, to optimize production.

The advantages of smart wells can be briefly comprised in the following four points:

  • reduction of operational expenses;

  • increased oil production;

  • improved oil recovery;

  • mitigation of the geological uncertainty impact on the project performance.

But disadvantages of smart well technology, that derive from its technical complexity, restrict a scale of its deployment. These disadvantages can be summarized as follows:

  • High cost;

  • Risk of the valves and sensors failures;

Thus, three questions must be addressed while screening possible smart well deployment:

  • How to estimate the value added by smart completion?

  • How to control smart well completions in order to gain the maximal effect?

  • How to estimate possible losses in production due to ICV failures?

A lot of publications were addressed to these problems in recent years. Majority of researchers used reservoir simulation as the main tool. Within the frameworks of such a concept a reservoir model impersonated both a real field and its reservoir model and was used for optimization. This work, on the one hand, partly adopts these approaches and, on the other hand, suggests the optimization strategy in full field scale based on subdivision of the initial big task into several ones with smaller dimension that require less time for the evaluation. Optimization routine is based on using commercial reservoir simulator coupled with Matlab-based program controller that optimizes downhole valve settings by using Direct Search optimization routine and imitates valves failures. The introduced optimization routine has been tested on multiple realizations of generic reservoir model always showing good performance and the added value.

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