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|Type:||Artigo de evento|
|Title:||Comparison Between Smart And Conventional Wells Considering Uncertainties|
|Author:||Da Silva J.P.Q.G.|
|Abstract:||The selection of a production strategy is one of the most important tasks to ensure success of petroleum fields development. In order to improve the performance of fields, the use of smart wells is becoming a common practice. In such wells, devices like valves and sensors are able to monitor and control the production, in real time, adding flexibility to the operation. However, it is possible that the expected gain of these wells production does not pay off the required additional investments. Thus, the aim of this research is to compare the differences between smart and conventional well performances. A methodology of production strategy optimization, which considers the availability of different platforms, each one with a particular fluid treatment capacity, was developed and applied to both the conventional and smart wells. Special care was given to some details of the methodology in order to guarantee a fair comparison between the two options. The methodology was applied to a heterogeneous reservoir, considering a deterministic case. After it, the sensibility of the model was studied by changing geological characteristics, adding uncertainties to the problem. The optimization results showed small differences between the two alternatives. Consequently, smart wells were able to improve oil production and reduce water production but the net present value (NPV) indicated that the use of conventional well was, in average, slightly more advantageous. Also, the application of different platforms with different capacities induced changes in the performance of the two kinds of wells. Finally, a sensitivity analysis and a detailed discussion were made in order to study situations where the smart wells could be applied. With the developed methodology, it was possible to carry out a fair comparison between the smart and conventional wells, providing good confidence and efficiency in the optimization process. Copyright 2009. Society of Petroleum Engineers.|
|Editor:||Society of Petroleum Engineers|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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