Waterflooding Optimization Using Intelligent Completions

Kewin Passos Granados¹; Bernardo Horowitz¹; Leonardo Correia de Oliveira¹

¹ Federal University of Pernambuco

doi:10.20906/CPS/CILAMCE2017-0137

Resumo

In recent years, the oil industry has focused its efforts on designing new tools that allow greater control over wells without making unnecessary interventions. Flow control devices (FCDs) are equipment that can be installed in the bottomhole with the function of controlling the flow that enters the interior of the tubing, to handle early water or gas breakthrough, and generate additional pressure losses in the fluid to equalize the pressure distribution in the tubing. This study describes the behavior of FCDs installed in a producer well, making a comparative analysis between the results obtained with the FCDs working in passive mode and working in active mode. The model was designed to improve the performance of wells with intelligent completions, to evaluate the economic return of a reservoir, to optimize the settings of the installed FCDs, and to create a base case of intelligent wells that can be implemented in real situations. In this work; a simple and reliable reservoir model and the configuration of the wells are created, the FCDs are installed to create intelligent completions, several iterations are generated to find the optimal setting of the FCDs, and numerical simulations are used to change the controls through setting of the aperture of the FCD. To achieve the objective and to carry out the activities, a software of wells segmentation and a numerical program are used to perform the optimization. It is considered a simple reservoir with two wells, an injector and a producer. The reservoir was submitted to a waterflooding process. In the passive mode, the configuration for each of the FCDs does not change during the concession period, thus evaluating the production and injection variables. In active mode, the FCD setting changes in the control cycles where a significant increase in water production is evidenced, each new setting is generated by several optimization iterations, adjusting the settings of the FCDs to maximize the Net Present Value (NPV) of the reservoir. The Sequential Quadratic Prog

Palavras-chave: Oil Reservoir; Waterflooding Optimization; Intelligent Completions